U.S. patent application number 11/724953 was filed with the patent office on 2007-11-01 for mobility based apparatus and methods using dispersion characteristics, sample fragmentation, and/or pressure control to improve analysis of a sample.
This patent application is currently assigned to SIONEX CORPORATION. Invention is credited to Douglas B. Cameron, Lawrence A. Kaufman, Raanan A. Miller, Erkinjon G. Nazarov.
Application Number | 20070252082 11/724953 |
Document ID | / |
Family ID | 34637438 |
Filed Date | 2007-11-01 |
United States Patent
Application |
20070252082 |
Kind Code |
A1 |
Miller; Raanan A. ; et
al. |
November 1, 2007 |
Mobility based apparatus and methods using dispersion
characteristics, sample fragmentation, and/or pressure control to
improve analysis of a sample
Abstract
The invention relates generally to devices and methods for
displaying ion mobility based analysis information using a
processor that processes data from an ion mobility based analyzer
and a display that displays the processed data in a
three-dimensional representation.
Inventors: |
Miller; Raanan A.; (Chestnut
Hill, MA) ; Nazarov; Erkinjon G.; (Lexington, MA)
; Kaufman; Lawrence A.; (Waltham, MA) ; Cameron;
Douglas B.; (Wellesley, MA) |
Correspondence
Address: |
FISH & NEAVE IP GROUP;ROPES & GRAY LLP
ONE INTERNATIONAL PLACE
BOSTON
MA
02110-2624
US
|
Assignee: |
SIONEX CORPORATION
Bedford
MA
|
Family ID: |
34637438 |
Appl. No.: |
11/724953 |
Filed: |
March 16, 2007 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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10998344 |
Nov 24, 2004 |
7227134 |
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11724953 |
Mar 16, 2007 |
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60524830 |
Nov 25, 2003 |
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60549004 |
Mar 1, 2004 |
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60549952 |
Mar 4, 2004 |
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60556349 |
Mar 25, 2004 |
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60566198 |
Apr 28, 2004 |
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Current U.S.
Class: |
250/282 |
Current CPC
Class: |
G01N 27/624
20130101 |
Class at
Publication: |
250/282 |
International
Class: |
B01D 59/44 20060101
B01D059/44 |
Claims
1. A device for displaying ion mobility based analysis information
comprising: a processor for processing data from an ion mobility
based analyzer, and a display for displaying the processed data in
a three-dimensional representation.
2. The device of claim 1, wherein the processing includes receiving
the data.
3. The device of claim 1, wherein the data includes detection
data.
4. The device of claim 3, wherein the detection data includes ion
intensity information.
5. The device of claim 1, wherein the data is associated with a
plurality of processing conditions of the ion mobility based
analyzer.
6. The device of claim 5, wherein the processing conditions include
at least one of Vrf, Vcomp, field strength, duty cycle of Vrf,
frequency of Vrf, time, flow channel pressure, flow channel
temperature, and an amount of dopant.
7. The device of claim 1, wherein processing includes comparing a
set of data from the ion mobility based analyzer with a library of
data to identify one or more ion species.
8. The device of claim 1, wherein the processing includes
generating a three-dimensional spectral signature based on the
data.
9. The device of claim 8, wherein the processing includes comparing
the three-dimensional spectral signature based on the data with a
library of three-dimensional spectral signatures to identify on or
more ion species.
10. The device of claim 8, wherein the spectral signature includes
a set of information associated with at least two processing
conditions and associated detection data of the ion mobility
analyzer.
11. The device of claim 1, wherein a change in value of at least
one of the first, second and a third dimension of the
three-dimensional representation is displayed as a change in at
least one of gray scale, black and white patterning, color, and
color saturation.
12. The device of claim 1, wherein the thee-dimensional
representation includes a first dimension as a length, the second
dimension as a width, and a third dimension as a height.
13. The device of claim 1, wherein the three-dimensional
representation is a dispersion plot.
14. The device of claim 1, wherein the three-dimensional
representation comprises an x-axis for field voltage, a y-axis for
Vcomp, and varying aspects of a color-related feature for ion
intensity.
15. The device of claim 1, wherein the three-dimensional
representation includes an x-axis corresponding to field voltage, a
y-axis corresponding to field compensation voltage, and a z-axis
corresponding to ion intensity.
16. A method for displaying ion mobility based analysis information
comprising: processing data from an ion mobility based analyzer,
and displaying the processed data in a three-dimensional
representation.
17. The method of claim 16, wherein the processing includes
receiving the data.
18. The method of claim 16, wherein the data includes detection
data.
19. The method of claim 18, wherein the detection data includes ion
intensity information.
20. The method of claim 16, wherein the data is associated with a
plurality of processing conditions of the ion mobility based
analyzer.
21. The method of claim 20, wherein the processing conditions
include at least one of Vrf, Vcomp, field strength, duty cycle of
Vrf, frequency of Vrf, time, flow channel pressure, flow channel
temperature, and an amount of dopant.
22. The method of claim 16, wherein processing includes comparing a
set of data from the ion mobility based analyzer with a library of
data to identify one or more ion species.
23. The method of claim 16, wherein the processing includes
generating a three-dimensional spectral signature based on the
data.
24. The method of claim 23, wherein the processing includes
comparing the three-dimensional spectral signature based on the
data with a library of three-dimensional spectral signatures to
identify on or more ion species.
25. The method of claim 23, wherein the spectral signature includes
a set of information associated with at least two processing
conditions and associated detection data of the ion mobility
analyzer.
26. The method of claim 16, wherein a change in value of at least
one of the first, second and a third dimension of the
three-dimensional representation is displayed as a change in at
least one of gray scale, black and white patterning, color, and
color saturation.
27. The method of claim 16, wherein the three-dimensional
representation includes a first dimension as a length, a second
dimension as a width, and a third dimension as a height.
28. The method of claim 16, wherein the three-dimensional
representation is a dispersion plot.
29. The method of claim 16, wherein the three-dimensional
representation comprises an x-axis for field voltage, a y-axis for
Vcomp, and varying aspects of a color-related feature for ion
intensity.
30. The method of claim 16, wherein the three-dimensional
representation includes an x-axis corresponding to field voltage, a
y-axis corresponding to field compensation voltage, and a z-axis
corresponding to ion intensity.
Description
REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of U.S. patent
application Ser. No. 10/998,344, filed on Nov. 24, 2004, which
claims the benefit of and priority to: U.S. Provisional Application
No. 60/524,830, filed on Nov. 25, 2003, entitled "System for
Identification of Ion Species in an Electric Field"; U.S.
Provisional Application No. 60/549,004, filed on Mar. 1, 2004,
entitled "Chemical Agent Detector"; and U.S. Provisional
Application No. 60/549,952, filed on Mar. 4, 2004, entitled
"Tunable Chemical Agent Detector"; U.S. Provisional Application No.
60/556,349, filed on Mar. 25, 2004, entitled "Tunable DMS
Recirculation System"; and U.S. Provisional Application No.
60/556,198, filed on Apr. 28, 2004, entitled "Reduced Pressure
DMS." The entire teachings of the above referenced applications are
incorporated herein by reference.
[0002] This application also incorporates by reference the entire
contents of the following co-pending U.S. patent applications: U.S.
Ser. No. 10/187,464, filed on 28 Jun. 2002; U.S. Ser. No.
10/215,251, filed on 7 Aug. 2002; U.S. Ser. No. 10/462,206, filed
on 13 Jun. 2003; U.S. Ser. No. 10/684,332, filed on 10 Oct. 2003;
U.S. Ser. No. 10/734,499, filed on 12 Dec. 2003; U.S. Ser. No.
10/738,967, filed on 17 Dec. 2003; U.S. Ser. No. 10/797,466, filed
on 10 Mar. 2004; U.S. Ser. No. 10/821,812, filed on 8 Apr. 2004;
U.S. Ser. No. 10/824,674, filed on 14 Apr. 2004; U.S. Ser. No.
10/836,432, filed on 30 Apr. 2004; U.S. Ser. No. 10/840,829, filed
on 7 May 2004; U.S. Ser. No. 10/866,645, filed on 10 Jun. 2004;
U.S. Ser. No. 10/887,016, filed on 8 Jul. 2004; U.S. Ser. No.
10/894,861, filed on 19 Jul. 2004; U.S. Ser. No. 10/903,497, filed
on 30 Jul. 2004; U.S. Ser. No. 10/916,249, filed on 10 Aug. 2004;
U.S. Ser. No. 10/932, 986, filed on 2 Sep. 2004; U.S. Ser. No.
10/943,523, filed on 17 Sep. 2004; and U.S. Ser. No. 10/981,001,
filed on 4 Nov. 2004.
FIELD OF THE INVENTION
[0003] The invention relates generally to mobility-based systems,
methods and devices for analyzing samples. More particularly, in
various embodiments, the invention relates to improving sample
collection, filtration, detection, measurement, identification
and/or analysis (collectively "analysis") using, for example:
dispersion characteristics (2-, 3-, and n-dimensional); sample
fragmentation; and/or variations in flow channel/filter field
conditions. Such conditions may include, without limitation:
pressure; temperature; humidity; field strength, duty cycle, and/or
frequency; and/or compensation voltage.
BACKGROUND
[0004] There are a number of different circumstances in which it is
desirable to perform analysis to identify compounds in a sample.
Such samples may be taken directly from the environment or they may
be provided by front end specialized devices to separate or prepare
compounds before analysis. There exists, a demand for low cost,
compact, low-power, accurate, easy to use, and reliable devices
capable of detecting compounds in a sample.
[0005] One class of known analyzers are mass spectrometers (MS).
Mass spectrometers are generally recognized as being the most
accurate type of analyzers for compound identification. However,
mass spectrometers are quite expensive, easily exceeding a cost of
$100,000 or more and are physically large enough to become
difficult to deploy everywhere the public might be exposed to
dangerous chemicals. Mass spectrometers also suffer from other
shortcomings such as the need to operate at relatively low
pressures, resulting in complex support systems. They also need a
highly trained operator to tend to and interpret the results.
Accordingly, mass spectrometers are generally difficult to use
outside of laboratories.
[0006] A class of chemical analysis instruments more suitable for
field operation is known as Field Asymmetric Ion Mobility
Spectrometers (FAIMS) or Differential Mobility Spectrometers (DMS),
and also known as Radio Frequency Ion Mobility Spectrometers
(RFIMS) among other names. Hereinafter, FAIMS, DMS, and RFIMS, are
referred to collectively as DMS. This type of spectrometer subjects
an ionized fluid (e.g., gas, liquid or vaper) sample to a varying
high-low asymmetric electric field and filters ions based on their
field mobility.
[0007] The sample flows through a filter field which allows
selected ion species to pass through, according to a compensation
voltage (Vcomp) applied to filter electrodes, and specifically
those ions that exhibit particular mobility responses to the filter
field. An ion detector then collects ion intensity/abundancy data
for the detected ions. The intensity data exhibits attributes, such
as "peaks" at particular compensation voltages.
[0008] A typical DMS device includes a pair of electrodes in a
drift tube. An asymmetric RF field is applied to the electrodes
across the ion flow path. The asymmetric RF field, as shown in FIG.
1, alternates between a high or "peak" field strength and a low
field strength. The field varies over a particular time period (T),
frequency (f) and duty cycle (d). The field strength E varies with
an applied field voltage (Vrf) and the size of the gap between the
electrodes. Ions pass through the gap between the electrodes when
their net transverse displacement per period of the asymmetric
field is zero. In contrast, ions that undergo a net displacement
eventually undergo collisional neutralization on one of the
electrodes. In a given RF field, a displaced ion can be restored to
the center of the gap (i.e. compensated, with no net displacement
for that ion) by superimposing a low strength direct current (dc)
electric field (e.g., by applying Vcomp across the filter
electrodes) on the RF. Ions with differing displacement (owing to
characteristic dependence of mobility in the particular field) pass
through the gap at differing characteristic compensation voltages.
By applying a substantially constant Vcomp, the system can be made
to function as a continuous ion filter. Alternatively, scanning
Vcomp obtains a spectral measurement for a sample. A recorded image
of the spectral scan of the sample is sometimes referred to as a
"mobility scan" or as an "ionogram."
[0009] Examples of mobility scans based on the output from a DMS
device are shown in FIGS. 2A and 2B. The compounds for which scans
are depicted are acetone and an isomer of xylene (o-xylene). The
scan of FIG. 2A resulted from a single compound, acetone, being
independently applied to the DMS analyzer. The illustrated plot is
typical of the observed response of the DMS device, with an
intensity of detected ions dependent on Vcomp. For example, the
acetone sample exhibits a peak intensity response at a Vcomp of
approximately -2 Vdc.
[0010] FIG. 2B illustrates the results when analyzing a mixture of
acetone and o-xylene. The combined response shows two peaks in
approximately the same region as for the independent case. The
compounds in the mixture can be detected by comparing the response
against a library, for example, of stored known responses for
independently analyzed compounds, or libraries of mixtures. Thus,
the scans for independently analyzed compounds, such as the scan of
FIG. 2A for acetone, can be stored in a computer system, and when
compound responses such as that in FIG. 2B are observed, the
relative locations of the peaks can be compared against the stored
responses in the library to determine the constitution of the
compound.
[0011] A specific RF field voltage and field compensation voltage
Vcomp permits only ion species having a particular ion mobility
characteristic to pass through the filter to the detector. By
noting the RF level and compensation voltage and the corresponding
detected signal, various ion species can be identified, as well as
their relative concentrations (as seen in the peak
characteristics).
[0012] Consider a plot of ion mobility dependence on Vrf, as shown
in FIG. 3. This figure shows ion intensity/abundancy versus RF
field strength for three examples of ions, with field dependent
mobility (expressed as the coefficient of high field mobility, a)
shown for species at greater, equal to and less than zero. The
velocity of an ion can be measured in an electric field (E) low
enough so that velocity (v) is proportional to the electrical field
as v=KE, through a coefficient (K) called the coefficient of
mobility. K can be shown to be related to the ion species and gas
molecular interaction properties. This coefficient of mobility is
considered to be a unique parameter that enables the identification
of different ion species and is determined by, ion properties such
as charge, size, and mass as well as the collision frequency and
energy obtained by ions between collisions.
[0013] When the ratio of E/N, where N is gas density, is small, K
is constant in value, but at increasing E/N values, the coefficient
of mobility begins to vary. The effect of the electric field can be
expressed approximately as K(E)=K(0)[1+.alpha.(E)], where K(0) is a
low voltage coefficient of mobility, and .alpha. is a specific
parameter showing the electric field dependence of mobility for a
specific ion.
[0014] Thus, as shown in FIG. 3, at relatively low electric field
strengths, for example, of less than approximately 8,000 V/cm,
multiple ions may have the same mobility. However, as the electric
field strengths increase, the different species diverge in their
response such that their mobility varies as a function of the
applied electric field. This shows that ion mobility is independent
of applied RF field voltage at relatively low RF field strengths,
but is field-dependent at higher RF field strengths.
[0015] FIGS. 2A and 2B demonstrate that species can have a unique
behavior in high fields according to mobility characteristics. The
ions passing through the filter are detected downstream. The
detection signal intensity can be plotted as a characteristic
detection peak for a given RF field voltage and field compensation
voltage Vcomp. Peak intensity, location, and shape are typically
used for species identification.
[0016] However, a problem occurs in that the peaks, as seen in the
typical DMS spectra, are generally broad in width. Therefore,
compounds exhibiting intensity peaks at similar compensation
voltages may be difficult to separate from each another.
Consequently, there may be particular conditions under which two
different chemicals generate indistinguishable scans for a
particular Vcomp and a particular RF field voltage, or for other
combinations of filter field/flow channel parameters. In such a
case, it is may not be possible to differentiate between the two
different compounds. Another problem may occur when two or more
chemical species have the same or almost the same ion mobility
characteristic for a particular set of field/flow channel
parameters. This is most likely to happen in the low electric field
regime (referred to herein as Ion Mobility Spectrometry or IMS),
where many existing ion mobility spectrometer systems operate.
Therefore, if two or more chemical species have the same or almost
the same mobility characteristic, then their spectroscopic peaks
will overlap, and identification and quantification of individual
species will be difficult or impossible.
[0017] FIG. 4 is a graph of Vcomp versus Vrf according to an
illustrative embodiment of the invention, but also highlighting the
above described prior art drawback. More particularly, FIG. 4
depicts a graph of Vcomp versus Vrf for four compounds: lutidine;
cyclohexane; benzene; and dimethyl-methl-phosphonate (DMMP). Each
curve shows the location of detected ion intensity peaks, such as
those circled at 100, at the various (Vrf, Vcomp) locations, which
in total provide the peak characteristics for each particular
compound. As shown, there is a region 100 in which the intensity
peaks and mobility curves for DMMP and cyclohexane overlap with
each other. As can be seen, operating in a Vrf region of from
approximately 2,500 Vpeak to approximately 2,650 Vpeak, at a Vcomp
of about -6 Vdc to about -8 Vdc, one would find it virtually
impossible to discriminate between the two compounds based on a
single Vcomp scan at a single Vrf. Specifically, in a conventional
spectral scan approach that plots intensity/abundance versus Vcomp
over a range of Vcomp for a single Vrf would plot the overlapping
peaks as a single peak.
[0018] Accordingly, there is a need for an improved ion
mobility-based compound identification approach that addresses peak
overlap issues and provides improved compound analysis
features.
SUMMARY OF THE INVENTION
[0019] The invention addresses the deficiencies of the prior art by
providing, in various embodiments, improved mobility-based systems,
devices and methods for analyzing constituents in a sample. More
particularly, in various embodiments, the invention provides
improved sample collection, filtration, detection, measurement,
identification and/or analysis (collectively "analysis") using, for
example: dispersion characteristics; sample fragmentation; and/or
sample processing variations, such as and without limitation,
variations in flow channel/filter field conditions. Such conditions
may include, any spectral changes, including, without limitation
changes in: pressure; temperature; humidity; field strength, duty
cycle, and/or frequency; field voltage amplitude, frequency and/or
duty cycle; detector bias voltage magnitude and/or polarity; and/or
filter field compensation voltage magnitude and/or polarity.
[0020] In one practice, the invention employs one or more of the
above to provide a library of spectral signatures for a plurality
of known species, and identifies unknown species by comparing at
least a portion of a spectral signature for the unknown species to
at least a portion of one or more of the spectral signatures stored
in the library. The spectral signature is a compilation of spectral
information for a particular species. The spectral information may
include, without limitation, spectral peak amplitude; spectral peak
width; spectral peak slope; spectral peak spacing; spectral peak
quantity; relative shifts in spectral peaks due, for example, to
changes in processing conditions; spectral discontinuities; Vrf
versus Vcomp characteristics or any other characteristics of any of
the above described conditions plotted against any one or more
other above described conditions.
[0021] According to one aspect, the invention provides improved
ion-based systems, methods and devices for analyzing samples by
varying a first sample processing condition over a first plurality
of values, and one or more second sample processing conditions over
a second plurality of values to determine spectral information for
a sample. In one particular embodiment, the invention scans a field
compensation voltage Vcomp over a range of values for one or more
Vrf values to generate a spectral representation at each of the one
or more Vrf values.
[0022] According to one feature, the invention adjusts a third
sample processing condition to narrow the widths of the resulting
spectral peaks of the determined ion spectral information. Such
width reduction reduces spectral peak overlap for samples having
similar mobility characteristics, improves resolution of an ion
mobility-based analyzer, and thus, provides more accurate
discrimination between sample species. In one configuration, the
third sample processing condition includes pressure in a sample
flow channel, and the invention reduces the pressure in the sample
flow channel to decrease the width of the spectral peaks.
[0023] According to another feature, the invention adjusts a third
sample processing condition to change a location of the resulting
spectral peaks of the determined ion spectral information, relative
to a Vcomp at which they occur. Since peaks of differing species
may shift differently, such shifts can provide improved
discrimination between peaks of species having similar mobility
characteristics. In one configuration, the third sample processing
condition includes Vrf, and the invention applies more than two
field voltages Vrf to provide peak shifting information for species
identification.
[0024] According to another feature, the invention adjusts a third
sample processing condition to provide spectral information
regarding both positive and negative ions of the sample. More
particularly, in one configuration, the invention provides both a
negative and a positive bias voltages to multiple detector
electrodes concurrently or to a single detector electrode
alternatively to provide both negative and positive mode scans.
Since compounds that have similar ion mobility characteristics
relative to one mode may have differing ion mobility
characteristics relative to the other mode, adjusting the polarity
of a bias voltage to detector electrodes can further improve sample
analysis.
[0025] In a further embodiment, the invention employs various
n-dimensional representations of ion spectral information, to
enhance the quality of spectral signatures, improve differentiation
between species having similar ion mobility characteristics, and
thus, improve identification accuracy, specifically, and sample
analysis, generally. By way of example, in one configuration, the
invention scans Vcomp for >2 field voltages Vrf, to capture
additionally, for example, spectral peak shift information. The
invention then generates an n-dimensional representation of the
spectral information that aggregates the spectral information
captured by scanning Vcomp at each Vrf. In one example, the
n-dimensional representation is a two-dimensional plot of Vrf
versus Vcomp aggregating the spectral information captured by
scanning Vcomp at each of the >Vrf field voltages. In a further
example, the aggregated representation is a three-dimensional
representation aggregating the spectral information captured from
scanning Vcomp at the >2 Vrf field voltages.
[0026] According to one approach, the three-dimensional
representation is a plot of ion intensity as a function of Vrf and
Vcomp. According to one implementation, Vcomp and Vrf are
represented in special coordinates, such as x- and y- coordinates,
and variations in ion intensity at the (Vcomp, Vrf) coordinates is
represented in variations of any color-related feature, including
without limitation, variations in gray scale, color saturation, or
color at those coordinates. Such color-related representations
provide easily recognized distinctions between species that were
difficult or impossible to distinguish between, without the
n-dimensional aggregation of the invention.
[0027] In a related implementation, a curve circumscribing the
color-related differences may be generated and the color-related
differences themselves may be discarded. In this way, the invention
can provide a two-dimensional representation of the spectral peaks,
for example, on a Vcomp versus Vrf grid, while still incorporating
the spectral information captured by scanning Vcomp over a
plurality of Vrf values. In another alternative implementation,
Vcomp, Vrf, and ion intensity are mapped into a three-dimensional
(x,y,z) spatial representation.
[0028] According to a related embodiment, any or all of the
spectral information may be represented in n-dimensional space as a
function of any or all of the processing variations to create >3
dimensional spectral signatures for both known and unknown species.
Conventional n-dimensional cluster matching techniques may then be
employed for identifying the unknown species.
[0029] In any of the above described n-dimensional representations,
any or all of the spectral information represented may be
incorporated into the spectral signatures for known species and
stored in the library of such signatures. Conventional pattern
recognition techniques may be employed to correspond at least
portions of the spectral signatures from unknown species with at
least portions of the signatures from known samples stored in the
library to identify the unknown species. In other implementations,
both the library of signatures and the captured signatures from the
unknown species are represented as mathematical descriptions, and
any suitable approach for making comparisons between such
mathematical descriptions may be employed to identify the unknown
species.
[0030] According to another embodiment, the invention employs
fragmentation to improve DMS analysis. Fragmentation includes
breaking large molecules of samples into smaller molecules,
molecule clusters, components, and/or base elements. The fragments
may then be individually analyzed, in series and/or in parallel to
generate more spectral information for the sample than would be
otherwise available without fragmentation. Fragmentation may be
achieved, for example and without limitation, by using any one or a
combination of a chemical reaction, a high energy field strength,
high Vrf, heating, laser light, colliding the sample molecules with
other molecules, soft x-ray, electromagnetic waves, or the like.
According to one feature, the invention incorporates any or all of
the above described spectral information for the fragment spectral
peaks into the spectral signature. According to a further feature,
the invention incorporates the point (e.g. the temperature,
pressure, field strength, Vrf, colliding molecule mass, colliding
molecule velocity, laser intensity, laser frequency, x-ray
intensity etc.) into the spectral signature.
[0031] According to other aspects, the invention provides various
serial and parallel combinations of ion-based analyzers employing
features, including those summarized above. In additional aspects,
the invention provides various compact, handheld, lightweight and
low power based analyzers, for example, for detecting chemical
warfare agents (CWAs), Toxic Industrial Compounds (TICs), and/or
Toxic Industrial Materials (TIMs).
[0032] The invention will now be described with reference to
various illustrative embodiments.
BRIEF DESCRIPTION OF THE DRAWINGS
[0033] The patent or application file contains at least one drawing
executed in color. Copies of this patent or patent application
publication with color drawings will be provided by the Office upon
request and payment of the necessary fee.
[0034] The foregoing and other objects, features, advantages, and
illustrative embodiments of the invention will now be described
with references to the following drawings in which like reference
designations refer to the same parts throughout the different
views. These drawings are not necessarily drawn to scale, emphasis
instead being placed upon illustrating principles of the
invention.
[0035] FIG. 1 is a graph depicting an asymmetric field having a
peak RF, time period, and duty cycle.
[0036] FIGS. 2A and 2B are graphs showing ion abundance (intensity)
versus applied field compensation voltage for acetone alone and for
a combination of ortho-xylene and acetone, respectively, as
detected in a field asymmetric ion mobility spectrometer.
[0037] FIG. 3 is a graph of ion mobility versus electric field
strength for three different compounds in a differential mobility
spectrometer (DMS).
[0038] FIG. 4 is a graph of Vrf versus Vcomp indicating intensity
peak locations according to an illustrative embodiment of the
invention and conceptualizing drawbacks of prior art
approaches.
[0039] FIG. 5 is a conceptual diagram of a DMS according to an
illustrative embodiment of the invention.
[0040] FIG. 6 is a graph of ion intensity versus field compensation
voltage for positive mode spectra for a sample containing various
amounts of ethyl mercaptan as measured in a DMS.
[0041] FIG. 7 is a graph of ion intensity versus compensation
voltage for negative mode spectra of a sample containing various
amounts of ethyl mercaptan.
[0042] FIG. 8 is a graph of ion intensity versus field compensation
voltage illustrating negative mode separation between monomer and
reactant ion peak (RIP) detections for sulfur hexafluoride
(SF6).
[0043] FIG. 9 is a graph of ion intensity versus field compensation
voltage illustrating the positive mode separation between monomer
and reactant ion peak (RIP) detections for sulfur hexafluoride
(SF6).
[0044] FIG. 10 is a graph of ion intensity versus field
compensation voltage illustrating a DMS response at various RF
voltage levels in the negative ion mode and also showing the RIP
detected in absence of SF6.
[0045] FIG. 11 is a graph of ion intensity versus field
compensation voltage illustrating a DMS response in the positive
ion mode where the SF6 peak is not isolated from the RIP.
[0046] FIG. 12 is graph of ion intensity (abundance) versus field
compensation voltage illustrating an ability to improve
discrimination between detected ion species by observing ion
spectral peak shifts corresponding to a change in field
strength.
[0047] FIGS. 13A and 13B are graphs of ion intensity (abundance)
versus field compensation voltage illustrating an ability to
improve discrimination between detected ion species by observing
ion spectral peak shifts due to reducing field strength.
[0048] FIGS. 14A and 14B are graphs of ion intensity at multiple
field strengths versus field compensation voltage, showing the
affect of changes in compensation voltage on specific spectra, and
show the divergent behavior of monomer, cluster, and reactant ion
peak (RIP) detections with changes in field strength and field
compensation voltage.
[0049] FIG. 15A is a three-dimensional color dispersion plot
illustrating detection of methyl salicylate over a range of field
voltages and field compensation voltages with varying ion intensity
represented in varying color according to an illustrative
embodiment of the invention.
[0050] FIG. 15B is a two-dimensional graph of ion intensity versus
field compensation voltage for mehyl salicylate at a single field
voltage.
[0051] FIG. 16A is a three-dimensional color dispersion plot
illustrating detection of DMMP over a range of field voltages and
field compensation voltages with varying ion intensity represented
in varying color according to an illustrative embodiment of the
invention.
[0052] FIG. 16B is a two-dimensional graph of ion intensity versus
field compensation voltage for DMMP at a single field voltage.
[0053] FIG. 17 is a three-dimensional color dispersion plot
illustrating detection of DIMP over a range of field voltages and
field compensation voltage with varying ion intensity represented
in varying color according to an illustrative embodiment of the
invention.
[0054] FIG. 18 is a two-dimensional graph of ion intensity versus
field compensation voltage for DIMP at a single field voltage.
[0055] FIG. 19 is a graph of ion intensity at a plurality of field
voltages versus field compensation voltage illustrating the effects
of changes in field conditions on location of individual detection
peaks and the ability to separate the detection.
[0056] FIG. 20A is a graph of ion intensity versus field
compensation voltage illustrating the separation of detection peaks
at different compensation voltages between light and heavy
molecules according to an illustrative embodiment of the
invention.
[0057] FIG. 20B is a graph of ion intensity versus field
compensation voltage showing the increase in number of peaks
detected after sample fragmentation according to an illustrative
embodiment of the invention.
[0058] FIG. 21 is a conceptual diagram of a DMS system using
fragmentation operating in parallel with a DMS system not using
fragmentation to improve sample analysis according to an
illustrative embodiment of the invention.
[0059] FIG. 22 is a conceptual diagram of a DMS system not using
fragmentation operating in series with a DMS system using
fragmentation to improve sample analysis according to an
illustrative embodiment of the invention.
[0060] FIG. 23A is a graph of ion intensity versus field
compensation voltage showing peak detection for the DMS system of
FIG. 22 not using fragmentation.
[0061] FIG. 23B is a graph of ion intensity versus field
compensation voltage showing peak detection for the DMS system of
FIG. 22 using fragmentation.
[0062] FIG. 24 is a conceptual block diagram of a DMS system
including a fragmentation region according to an illustrative
embodiment of the invention.
[0063] FIG. 25 is a three-dimensional color dispersion plot
illustrating detection of agent GA according to an illustrative
embodiment of the invention.
[0064] FIGS. 26A-26H are two-dimensional graphs of ion intensity
versus field compensation voltage at particular field voltages, the
two-dimensional graphs being of the type combinable into the
three-dimensional color dispersion plot of FIG. 25, according to an
illustrative embodiment of the invention.
[0065] FIGS. 27A and 27B are graphs of ion intensity at a plurality
of pressures versus field compensation voltage according to an
illustrative embodiment of the invention.
[0066] FIGS. 28A and 28B are graphs of ion intensity versus
pressure showing a quantifiable effect on positive and negative
background spectra, respectively, caused by a decrease in pressure
according to an illustrative embodiment of the invention.
[0067] FIGS. 29A and 29B are graphs of ion intensity at a plurality
of pressures versus field compensation voltage showing the effect
of varying pressure on negative and positive tert-butylmercaptan or
tert-butylithiol (TBM) spectra, respectively, according to an
illustrative embodiment of the invention.
[0068] FIGS. 30A and 30B are graphs of ion intensity versus
pressure showing the effect of varying pressure on negative and
positive TBM ion peak parameters, respectively, according to an
illustrative embodiment of the invention.
[0069] FIG. 31 is a graph that shows the effect of reduced pressure
on analyte peaks for chemical warfare agents such as DMMP, DIMP,
and MS.
[0070] FIGS. 32A-32D are graphs of ion intensity versus field
compensation voltage showing improved detection resolution for
agent GF at reduced pressures according to an illustrative
embodiment of the invention.
[0071] FIG. 33 is a three-dimensional color dispersion plot
illustrating detection of positive ions of 0.005 mg/m.sup.3 DIMP at
about 0.65 atm and over a range of field voltages and field
compensation voltages with varying intensity depicted by varying
colors.
[0072] FIG. 34 is a three-dimensional color dispersion plot
illustrating detection of positive ions of 0.005 mg/m.sup.3 DIMP at
about 0.5 atm and over a range of field voltages and field
compensation voltages with varying intensity depicted by varying
colors.
[0073] FIG. 35 is a graph that shows positive (left) and negative
(right) three-dimensional color dispersion plots for 0.85
mg/m.sup.3 agent GB with a relative humidity (RH)=87 in a DMS
system operating at 0.5 atm and for a fragmented sample.
[0074] FIGS. 36A and 36B are graphs that show a plot of
compensation versus field strength of detected monomer and cluster
ion peaks for a family of ketones according to an illustrative
embodiment.
[0075] FIGS. 37 and 38 are tables, each including a collection of
detection data for a group of monomer and dimers (clusters) of
eight ketones respectively, that were used to generate the curves
in the graphs of FIGS. 36A and 36B.
[0076] FIGS. 39A and 39B are graphs of a ratio of field strength to
gas density (E/N) versus field compensation voltage that illustrate
the results of calculating normalized alpha parameter curves.
[0077] FIG. 40A is a flow diagram of an exemplary sequence of steps
of a computer process used to acquire data concerning a particular
chemical ion species.
[0078] FIG. 40B shows a diagram of a data structure for a library
of stored compound data measurement information.
[0079] FIG. 40C is a flow diagram of a series of steps that may be
applied to perform a chemical recognition.
[0080] FIG. 40D is a flow diagram of a series of steps that may be
added to the data acquisition and chemical recognition processes
using alpha curve fitting.
[0081] FIG. 40E shows a diagram of a more complex data
structure.
[0082] FIG. 40F is a flow diagram of a sequence of processes that
may be used to distinguish monomer and cluster peak responses.
[0083] FIG. 40G is a flow diagram of a process showing the
combination of monomer and cluster scoring.
[0084] FIG. 41 is a conceptual diagram of a compact DMS analyzer
system 1400 used to detect and identify chemical warfare agents
(CWAs), Toxic Industrial Compounds (TICs) and Toxic Industrial
Materials (TIMs) which may be released in warfare or terrorist
situations according to an illustrative embodiment of the
invention.
[0085] FIG. 42 is a graph of multiple plots showing experimental
results for a series of warfare agent simulants selectively mixed
with 1% headspace of AFFF.
[0086] FIG. 43 is a three-dimensional color dispersion plot of the
detection of positive ions of agent GA over a range of field
voltages and field compensation voltages with varying intensity
represented in varying color according to an illustrative
embodiment of the invention.
[0087] FIG. 44 is a conceptual block diagram of a chemical and/or
biological agent detection system using an ion mobility analyzer
system, membrane, and recirculation system according to an
illustrative embodiment of the invention.
[0088] FIG. 45 is a conceptual block diagram of a chemical and/or
biological agent detection system configured for reduced pressure
analysis according to an illustrative embodiment of the
invention.
[0089] FIG. 46 is a conceptual block diagram of a chemical and/or
biological agent detection system using a cylindrical DMS analyzer
system, recirculation system, and multiple flow channels according
to an illustrative embodiment of the invention.
[0090] FIGS. 47-53 are conceptual block diagrams respectively of
chemical and/or biological agent detection systems using various
configurations of a DMS analyzer system, recirculation system, and
other components according to an illustrative embodiment of the
invention.
DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS
[0091] As described above in summary, the invention is generally
directed to systems, methods and devices for providing improved
detection, measurement, discrimination and analysis (collectively
"analysis") of compounds. The compounds analyzed may include any
compound, both organic and inorganic, without limitation elements,
chemicals, and biologicals. In particular illustrative embodiments,
the invention is directed to improved ion mobility-based compound
analysis. Particular features of the invention include, use of
dispersion plots, sample fragmentation and/or pressure controls to
improve discrimination between compounds having similar or
overlapping ion mobility characteristics.
[0092] Although the illustrative embodiments of the invention are
described in terms of Field Asymmetric Ion Mobility Spectrometers
(FAIMS), also known as Differential Mobility Spectrometers (DMS),
or Radio Frequency Ion Mobility Spectrometers (RFIMS) among other
names (collectively DMS), the features of the invention may be
similarly employed in combination with ion mobility spectrometry
(IMS), time of flight (TOF) IMS, gas chromatography (GC), Fourier
transform infrared (FTIR) spectroscopy, mass spectrometry (MS), and
liquid chromatography mass spectrometry (LCMS).
[0093] FIG. 5 is a block diagram of a DMS system 10 of the type
that may employ the invention. The system 10 includes a flow
section 15 and a processor section 40. The flow section 15 includes
a flow channel 11 extending from a flow inlet 12 to a flow outlet
13. Opposing filter electrodes 20 and 21 are located within the
flow channel 11. Detector electrodes 26 and 30 are also located
within the flow channel 11. The processor section 40 includes an RF
voltage generator 42 for providing an RF field voltage to the
filter electrodes 20 and 21, and direct current (dc) voltage
generator 44 for providing a dc compensation voltage Vcomp to the
filter electrodes 20 and 21. The processor section 40 also includes
a processor 46 for controlling the voltage generators 42 and 44,
and for processing inputs from the ion detectors 28 and 30 by way
of the amplifiers 36 and 38 the A/D converter 48. The processor
section 40 also provides a display 49 for providing analysis
information to a user. One feature of the system 10 is that it may
be contained in a hand held unit weighing less than about one
pound.
[0094] In operation, a sample S enters the flow channel 11 at the
flow channel inlet 12. The sample S may, for example, be drawn in
from the environment or received from a front end device, such as
another DMS, an IMS, TOFIMS, GC, FTIR, MS, or LCMS. The sample S
may be mixed with an effluent, such as a gas, liquid or vapor. In
the instant example, a carrier gas CG is employed to flow the
sample S through the flow channel 11. Upon entering the flow
channel 11, the sample S flows into an ionization region 14. The
sample is ionized by an ionization source 16 as it flows through
the ionization region 14, creating a set of ionized molecules 17+,
17-, with some neutral molecules 17n, of various chemical species
in the sample S. This may include, for example, monomer ions and
cluster ions. Such clusters may be created when a monomer combines
with water molecules or other background molecules, and the
combination is ionized.
[0095] The carrier gas CG then carries the ionized sample S into
the ion filter field 18 located between the opposing filter
electrodes 20 and 21 of the ion filter 24. Filtering proceeds based
on differences in mobility in the filter field 18 of the various
ions included in the sample S. Ion mobility is influenced, for
example, by ion size, shape, mass and charge. The field generator
42 applies an asymmetric field voltage Vrf across the filter
electrodes 20 and 21 to cause the field strength within the filter
field 18 to alternate between high and low field strengths. The
ions 17+, 17- and 17n move in response to the field, based on their
mobility characteristics. Typically, an ion's mobility in the high
field strength condition differs from its mobility in the low field
strength condition. This mobility difference produces a net
transverse displacement of the ions as they travel longitudinally
through the filter 24. The transverse displacement defines an ion
trajectory for each of the sample S ions.
[0096] As described above, the voltage generator 44, under the
control of the processor 46, applies a dc compensation voltage
Vcomp across the electrodes 20 and 21. The compensation voltage
Vcomp causes particular ion species to be returned toward the
center of the flow path 14, and thus enables them to exit the
filter field 18, without colliding with either of the filter
electrodes 20 or 21 and without being neutralized. Other species,
for which the applied Vcomp is not sufficient ultimately collide
with the filter electrodes 20 and 21 and are neutralized. The
neutralized ions are purged, for example, by the carrier gas CG, or
by heating the flow path 11.
[0097] The illustrative system 10 of FIG. 5 also can discriminate
between ions based on differences in polarity, as is the case with
the ions 17- and 17+. According to one feature, the system 10 of
FIG. 5 can be operated to concurrently, or in some instances,
substantially simultaneously detect both positive and negative ions
in the sample S. This feature enables identification of two
compounds concurrently, or in some instances, substantially
simultaneously. This feature also enables concurrent or
substantially simultaneous detection of two modes of a single
compound.
[0098] In operation, the two species of ions 17+ and 17-, enter the
detection region 25, where further separation occurs followed by
their intensity determination. In a illustrative embodiment, the
electrode 28 of the detector 26 may be positively biased to attract
the ions 17- and repel the ions 17+. Alternatively, the electrode
30 of the detector 26 may be biased negatively to attract the ions
17+ while repelling the ions 17-. The signals generated by the ions
collecting at the detector electrodes 28 and 30 are amplified by
respective amplifiers 36 and 38 and provided to the processor 46 by
way of the A/D converter 48. According to one feature, the
processor 46 compares the digitized signals from the A/D converter
48, with a library of ion intensity curves for known compounds
stored in the memory 47, to identify compounds in the sample S. The
results of the comparison operation can then be provided to an
appropriate output device, such as the display 49, or may be
provided to an external destination by way of an interface 56.
[0099] According to a further illustrative embodiment, the system
10 is calibrated prior to employing it for analyzing a sample. More
particularly, the library of ion intensity curves for known species
of ions at particular Vcomp and Vrf settings is created and stored
in the memory 47. According to one feature, once the system 100 is
calibrated, it may be used continuously, without need for further
calibration. However, it is also within the scope of the invention
to calibrate the system 10 using the reactant ion peak (RIP) or a
dopant peak, for example.
[0100] According to various illustrative embodiments, field
strength within the filter field 18 resulting from an applied field
voltage Vrf may have values ranging from about 1,000 V/cm to about
30,000 V/cm, or higher. The frequency of Vrf may have values
ranging from about 1 to about 20 megahertz (MHz), with the higher
frequencies having an approximately 30 percent duty cycle.
[0101] It should be noted that the system 10 may be tuned by
employing any suitable operating values of, for example, Vrf,
Vcomp, field strength, Vrf duty cycle, Vrf wavelength and Vrf
frequency. Additionally, as described in further detail below, to
improve analysis, the system 10 may be tuned by varying values of
other flow channel conditions, such as and without limitation,
temperature, pressure, humidity, flow rate, doping and carrier gas
CG composition. As also described below in more detail, multiple
scans of the sample S taken, for example, by recirculating the
sample S and/or processing the sample in parallel and/or in series
with one or more additional DMS, IMS, TOFIMS, GC, FTIR, MS, or
LCMS, at differing flow channel/filter field conditions may be
employed to improve analysis of the sample S.
[0102] According to one illustrative embodiment, the processor 46
causes the voltage generator 44 to scan or sweep a range of field
compensation voltages Vcomp for a particular RF field strength as
controlled by the applied Vrf to obtain a first spectrum for the
sample S. Then, Vrf is set to a different level and the Vcomp is
once again scanned to establish a second spectrum for the sample S.
This information can be compared to a library of spectral scans in
a similar fashion as that described above to identify a compound in
a sample.
[0103] If a particular combination of peaks in a spectral scan is
known to indicate the presence of a particular compound, data
representing the multiple peaks can be stored and future detection
data can be compared against this stored data. For example, under
controlled filter field conditions, such as at a raised field
strength, a clustered compound may become de-clustered. The
detection results in a signature of peaks that can be used to
identify the source compound being detected even as detected in a
single scan.
[0104] According to one illustrative application, the invention is
used for detecting sulfur-containing compounds in a hydrocarbon
background. In one example, negative and positive ions are
separately detected. The detected data enables a quantitative
measurement of concentration of these sulfur-containing compounds,
independent of the hydrocarbon background.
[0105] In another illustrative application, the invention is used
for detecting trace amounts (parts per million (ppm), parts per
billion (ppb), or parts per trillion (ppt)) of mercaptan in varying
and even high hydrocarbon backgrounds. The system 10 of FIG. 5 is
also able to characterize hydrocarbon gas backgrounds. For example,
the invention is capable of detecting mercaptans, such as ethyl
mercaptan in a methane background, and is also capable of detecting
a gas, such as methane, in a mercaptan background.
[0106] In this practice of the invention, where mercaptans were
detected in hydrocarbon background, the asymmetric voltage applied
to the ion filter electrodes ranged from about 900 to about 1.5 kV
(high field condition), and a low voltage of about -400 to about
-500 V (low field condition). The frequency ranged from about 1 to
about 2 MHz, and the high frequency had an approximate 30% duty
cycle, although other operating ranges may be employed. In one
embodiment, the detector electrodes were biased at +5v and -5v.
With this arrangement, the mercaptans can be detected by the
negative mode (-5v) detector and the hydrocarbon gases can be
detected by the positive mode (+5v) detector.
[0107] The system 10 employs various conventional components. By
way of example, the amplifiers 36 and 38 may be Analog Devices
model 459 amplifiers. Additionally, the A/D converter may be
included on a National Instruments circuit component (model 6024E)
for digitizing and storing the scans, and may include software for
displaying the results as spectra, topographic plots, dispersion
plots or graphs of ion intensity versus time. Alternatively, such
software may be stored in the memory 47 and may control the
processor 46. The ionization source may be, for example, a plasma,
laser, radioactive, UV lamp, or any other suitable ionization
source.
[0108] According to one illustrative embodiment, Vrf is applied
across the filter electrodes 20 and 21. However in some
configurations, Vrf is applied to one filter electrode, e.g.,
electrode 20, and the other electrode, e.g., electrode 22, is tied
to ground. Vcomp is then applied to one of the filter electrodes 20
and 21, or alternatively, across the filter electrodes 20 and 21,
according to the ions species to be passed. According to another
feature, the detector electrodes 28 and 30 are biased with a
floating bias, such as with the electrode 28 being biased at -5 Vdc
and the electrode 30 being biased at +5 Vdc, leads to good
performance for detection of mercaptans in hydrocarbon or air
backgrounds.
[0109] FIG. 6 is a graph of ion intensity versus field compensation
voltage for "positive mode" spectra for a sample containing varying
amounts of ethyl mercaptan as measured in a DMS system of the type
depicted at 10 in FIG. 5. For positive mode detection, the detector
electrode 28 is negatively biased and attracts positive methane
ions 17m+ for detection. FIG. 7 is a graph of ion intensity versus
compensation voltage for "negative mode" spectra of a sample
containing various amounts of ethyl mercaptan. For negative mode
detection, the detector electrode 30 is positively biased and
attracts the negative mercaptan ions 17m- for detection. As can by
seen from FIGS. 6 and 7, the mercaptan signatures are captured
independent of the air-hydrocarbon carrier gas CG background, at
various dosage levels and the detected sample peaks are fully
isolated from the background. As can be seen in FIG. 6, the
reactant ion peak (RIP) is isolated; and as shown in FIG. 7, the
background (sample #9) is flat.
[0110] As mentioned above, the detector electrodes 28 and 30 can be
oppositely biased to enable concurrent, or in some configurations,
substantially simultaneous detection of both positive and negative
ions. Even in a sample such as mercaptan, which when ionized may
have predominantly negative ions, detecting both positive and
negative ions provides improved analysis accuracy over a single
mode detection approach. This, in turn, improves identification
accuracy and confidence, and reduces the likelihood of false
positives and false negatives.
[0111] For example, Sulfur hexafluoride (SF6) can be well detected
in the negative mode. However, the response in the positive mode,
while alone not definitive, has a profile, and thus in combination
with the negative mode, is confirmative and provides a lower
likelihood of a false detection. According to one feature, the
invention can detect SF6 in single mode (e.g., only negative mode
detection) or dual mode (both negative and positive mode
detection), seriatim, concurrently, or simultaneously.
[0112] SF6 gas is used in atmospheric tracer applications to
monitor air flow, as a tracer for leak detection in pipes to point
detect sources of leaks, in power plants to isolate switches to
reduce, or prevent breakdown of the switches, among other uses.
Isolation and detection of SF6 is often found to be a difficult
proposition.
[0113] According to one illustrative application, a system of the
invention is employed to detect SF6 in air. According to a further
illustrative embodiment, the invention provides a portable, battery
powered unit for the detection of SF6 with a sensitivity of about
1.times.10-9 atm cc/sec SF6 (0.01 PPM). In this illustrative
embodiment, the invention may be used, for example, in the power
industry to ensure the leak tightness of High Voltage Switchgear
and in the laboratory for testing fume hoods to the ASHREA 110
specification. Other applications include torpedo head, pipework
systems, and air bag integrity testing. The high sensitivity,
rugged design and ease of use and set up of the invention are
advantageous for many applications that involve the detection of
SF6.
[0114] FIG. 8 is a graph of ion intensity (y-axis) versus Vcomp
(x-axis) for negative mode detection of SF6 according to an
illustrative embodiment of the invention. As can be seen,
application of the invention provides a distinct peak for the SF6,
separate from the reactant ion peak. FIG. 9 provides a similar plot
for SF6 for positive mode detection. As can be seen, for positive
mode detection, there is no significant difference between the
signal 51 without the SF6 present and the signal 53 with the SF6
present. FIG. 10 shows a plot of intensity (y-axis) versus Vcomp
(x-axis) for SF6 at three different field voltages Vrf (shown at
57, 58 and 61 for negative mode detection along with the RIP 55
detected in absence of SF6. FIG. 11 shows a similar plot to that of
FIG. 10, for positive mode detection. As would be expected the,
positive mode detection curves 69, 71 and 73, substantially track
their corresponding RIP curves 63, 65 and 67, respectively. As
mentioned above with respect FIG. 16, while alone this is not
definitive, it is an expected detection and therefore may be used
as confirmative when combined with a definitive SF6 negative mode
detection.
[0115] According to another feature, the above described library
data for known ion species intensity signatures for known device
characteristics may be accessed for either single mode or
simultaneous positive and negative mode detections. By comparison
with historical detection data for the device, these peaks can be
more clearly identified as the tell-tale spectra of the mercaptan.
Both spectra give an indication of the mercaptan, qualitatively and
quantitatively. Although the advantages of the simultaneous
positive and negative mode detection is described above with
respect to mercaptan, they may be employed to the analysis of any
sample, and are especially useful with real-time analysis of
complex samples, such as ones containing mercaptans and hydrocarbon
gas, which have similar ion mobility characteristics, and are
therefore, difficult to discriminate between.
[0116] The foregoing demonstrates favorably obtaining multiple
detection data from a single mobility scan for identification of
detected ion species in a sample. This innovation is useful in many
applications. Notwithstanding this valuable innovation, a still
higher level of confidence and further reduced false positives may
be obtained by (1) obtaining multiple detection data from multiple
ion mobility scans, and (2) further processing such data to extract
device independent attributes, such as a mobility coefficient,
.alpha..
[0117] According to one illustrative "multiple scan" embodiment,
ions are identified based not on a single set of field conditions,
but instead on multiple ion intensity scans taken at least two and
possibly additional numbers of field conditions (e.g., at least two
field measurement points). Detections are correlated with the Vrf
and Vcomp, at the at least two different field conditions, to
characterize a given detected compound. Because multiple detection
data are associated with a given ion species of interest, more
accurate detections can be made. Comparison with stored data
results in reliable identification of detected compounds.
[0118] Strategies for identifying detected ions based on data in
spectral peaks or in mobility curves include: curve matching, peak
fitting, deconvolution (for overlapping peaks), multi-dimensional
mapping, for example, employing three-dimensional representations,
including (x,y,z, etc.) spatial coordinate systems and/or (x,y,
etc.) coordinate systems, with z- or other values represented by
color variations. These techniques enable identification of
detected ion species based peaks in a single scan, including
simultaneous positive and negative mode detections, and also in
multiple scans. The goal is the same: analysis of multiple
detection data that can be used to definitively identify, detect,
measure or otherwise analyze the species of a detected ion.
[0119] As described above, different ion species of chemicals
exhibit different mobility as a function of the compensated applied
Vrf. Thus, by applying a set of different Vrf voltages and
measuring the Vcomp at the ion abundance peak locations, for
example, as detected by the detector 26 of FIG. 1, for the various
compounds, a family of measurement points characteristic of a
compound can be developed. This family of points can then be
plotted to determine the ion mobility curve signature for specific
species as a function of Vrf and Vcomp, for example, as shown in
FIG. 4. As also described above, such data can be stored and
compared with data from scans of unknown compounds to identify the
unknown compounds. While some comparison approaches perform curve
matching, other approaches determine an ion intensity for a
particular ion species for two nearby field strength and Vcomp
conditions. The slope between the two data points is calculated and
employed as a signature for the particular ion species. The
selection of measurement points and the number of measurement
points may be adjusted for the specificity required for a
particular application. The minimum number of measurement points is
two, which at least identifies an aspect (such as slope) of the
characteristic curve for a compound, given the known field
values.
[0120] Although performing slope and/or curve matching for an
individual or for multiple scans, where a single filter field/flow
channel condition is varied, may provide sufficiently accurate
results for some applications, one illustrative embodiment of the
invention recognizes that multiple scans taken while varying
multiple filter field/flow channel conditions can provide improved
results. By way of example, according to one illustrative
embodiment, the invention steps Vrf through a plurality of values
and scans Vcomp at each of the plurality of Vrf values to generate
unique sets of data, which better distinguish between compounds and
thus, provide more accurate identification of detected compounds.
This approach can be employed to create a data store of more
accurate ion mobility signatures for compounds of interest.
[0121] According to one illustrative embodiment, the invention
incorporates information regarding shifts in an ion abundance peak
for a particular ion species at multiple filter field/flow channel
conditions into the spectral signature for a compound. More
specifically, at a particular Vrf (Vrf1) a ion abundance peak may
be detected at a particular Vcomp (Vcomp1). However, the ion
abundance peak may shift to be detected at a second Vcomp (Vcomp2)
for a second Vrf (Vrf2). One illustrative embodiment of the
invention recognizes that, in many instances, the ion peak shift
from Vcomp1 to Vcomp2 in response to varying Vrf from Vrf1 to Vrf2
is indicative of a particular ion species. Similar measurements of
unknown compounds can be compared against this portion of the
spectral signature to aid in identification of the unknown
compound.
[0122] FIG. 12 depicts an example illustrating the above described
ion abundance spectral shift due to a change in Vrf from 1400 Vpeak
to 1450 Vpeak over a scanned Vcomp. In FIG. 12, the peaks 110-1,
110-2, 110-3, and 110-4 occur at a particular field compensation
voltages Vcomp, for Vrf at 1400 Vpeak (corresponding to a field
strength of 28,000 V/cm), but shift to be located at different
compensation voltages in response to Vrf being changed to 1450
Vpeak (corresponding to a field strength of 29,000 V/cm). As can be
seen from FIG. 12, even small changes in a field condition, such as
a change in Vrf, can cause a measurable ion peak shift, and can
thus provide significant additional information to the ion spectral
signature. In the specific example of FIG. 12, the shift in ion
peak due to the change in Vrf is employed when making a comparison
to ion spectral signatures for known compounds to identify an
unknown compound.
[0123] FIGS. 13A and 13B show an experimental example illustrating
how ion spectral peak shifting can be employed to identify an
unknown species. In FIGS. 13A and 13B, in a field strength of about
24000 V/cm, peaks for three different isomers of xylene in a
sample, p-, o-, and m-, were detected. In FIG. 13A, the peaks for
p- and o- are indistinguishable, while the peak for m- is well
defined. To further evaluate the sample, a second detection (FIG.
13B) was performed at a lower field strength of 18000 V/cm. As can
be seen in FIG. 13B, the peak shift due to the change in field
strength causes the three different isomers p-, o-, and m- of
xylene to be more clearly distinguishable, and thus more accurately
identified. As can be seen from FIGS. 13A and 13B, better
discrimination between species is not always a result of applying a
higher field strength. More particularly, in this example, the p-
and o- xylene isomers become more distinguishable at reduced a
field strength.
[0124] According to another illustrative embodiment and as
mentioned above, the invention generates detection data over a
range of applied filter field/flow channel conditions. For example,
FIGS. 14A and 14B show the effect of changes in field strength on
the location of detection peaks at different Vcomp levels for
hexanone and octanone, as detected in a DMS system of the type
depicted at 10 in FIG. 1. The curves are offset on the vertical
axis, with the offset increasing as electric field strength
increases. While various operating ranges are possible, as an
illustration, FIGS. 14A and 14B may be understood as presenting
peak Vrf between a low of about 620 Vpeak (lowermost plot in each)
and a high of around 1450 Vpeak (uppermost plot in each). Several
attributes are noted in this series of responses. For example,
referring specifically to the hexanone plot of FIG. 14A, a monomer
peak of 601-1 of particular interest is somewhat obscured in the
lowest field strength condition. However, at the highest applied
field strength, the peak 601-m corresponding to hexanone is clearly
discernable from the other peaks.
[0125] Several phenomena have occurred with the increase in
increasing applied field strength. First, a reactant ion peak (RIP)
605-1 is relatively dominant in the low field strength detection.
However, as electric field strength is increased, the RIP 605-m
shifts to the left at a more rapid rate than the monomer ion peak
601-m of interest. This is because the .alpha. parameter for the
mobility coefficient for the reactant ion species is different than
the .alpha. parameter for the monomer ion of interest.
[0126] In addition, the relative amplitude of the RIP 605 decreases
markedly with the increase in the electric field strength. Thus,
RIP 605-m is observed at much lower amplitude and well separated
from the monomer peak 601-m of interest at a specific field
condition. While the monomer peaks 601 also shift, they do not
shift by the same amount, or by as much. Thus, by analyzing the
compound over a range of applied field conditions, a condition can
be discovered at which the RIP 605 shifts away from or off the
scale of other observed peak voltages. In some cases, this allows
easier detection of the monomer ion peak 601 of interest.
[0127] Similar behavior is observed in the monomer peaks 610-1,
610- . . . , 610-n observed for octanone and the resulting reactant
ion peaks 615-1 to 615-m. This information can thus be used to
identify a species by comparing a family of response curves to a
stored family of known response curves.
[0128] Another observed effect shown in both FIGS. 14A and 14B is
that a group of cluster ions 608 and 610 are seen. The cluster ions
608 represent clusters of chemical materials in the sample. Typical
cluster ions, having a heavier chemical weight, have peaks that are
shifted differently from monomer ion peaks of interest. In this
example, the cluster peaks shift in a direction away from the
direction of shift of the monomer peaks with increasing applied
field strength. This characteristic feature of cluster ions,
observed with this sample, can also be stored and utilized in
recognizing the hexanone and/or octonone ions. The curves shown in
FIGS. 14A and 14B are but one example of how applying a range of
field/flow channel conditions to detect a given sample can be
utilized to an advantage.
[0129] As mentioned above briefly, according to one illustrative
embodiment, the invention employs multi-dimensional compound
signatures for comparison with multi-dimensional representations of
unknown compounds to identify and more generally analyze the
unknown compounds. Such multi-dimensional representations may
arise, for example, from plotting ion abundancy as a function of a
plurality of varying filter field/flow channel conditions. Such
conditions may include, without limitation, Vrf, Vcomp, filter
field strength, Vrf duty cycle, Vrf wavelength and Vrf frequency;
temperature, pressure, humidity, flow rate, doping and carrier gas
CG composition. Multi-dimensional representations may also result
from taking multiple scans of the sample S taken, for example, by
recirculating the sample S and/or processing the sample S in
parallel and/or in series with one or more additional DMS, IMS,
TOFIMS, GC, FTIR, MS, or LCMS, at the same or differing flow
channel/filter field conditions. The multi-dimensional
representation, according to one illustrative embodiment, is a
three-dimensional dispersion plot, employing x- and y- spatial
coordinates, with a z-coordinate being represented by a variation
in color.
[0130] FIG. 15A shows a three-dimensional color dispersion plot 620
depicting detection of methyl salicylate over a range of field
voltages Vrf (y-axis) and field compensation voltages Vcomp
(x-axis), with varying ion intensity (abundance) represented in
varying colors, according to and illustrative embodiment of the
invention. Although, particular color coordination may vary, the
dispersion plot of FIG. 15A represents the highest ion intensity in
blue with yellow representing the lowest. The three-dimensional
color dispersion plot 620 represents an aggregation of data from a
plurality of two-dimensional graphs, such as that shown in FIG.
15B. More specifically, FIG. 15B shows a plot 622 of ion intensity
(y-axis) versus Vcomp (x-axis) at a particular Vrf for methyl
salicylate. A plurality, illustratively more than two, of such
graphs taken at a plurality, illustratively more than two, of field
voltages Vrf are aggregated to provide the color plot 620 of FIG.
11A. Aggregating a plurality of scans taken at a plurality of
filter field voltages Vrf (and thus, field strengths) provides a
more discriminating scan than a single scan taken at a single Vrf.
One reason for this is that the aggregated scans incorporate the
above discussed peak shifting that occurs due to the changes in
Vrf. As can be seen, the three-dimensional representation of FIG.
15A provides three signature peaks 621, 623, and 625, as opposed to
the two peaks 627 and 629 of FIG. 15B.
[0131] The effect of the increased resolution provided by employing
dispersion plots, is even more evident, when trying to distinguish
between compounds having similar ion mobility characteristics. By
way of example, FIGS. 16A and 16B show positive mode plots 624 and
626 for DMMP, while FIGS. 17 and 18 show positive mode plots 628
and 630 for DIMP. More specifically, FIGS. 16B and 18, plot ion
intensity (y-axis) versus Vcomp (x-axis) at a particular Vrf for
DMMP and DIMP, respectively. As shown, both FIGS. 16B and 18
included three peaks of similar magnitude, located at a
approximately the same field compensation voltages, and similarly
spaced apart. Distinguishing between DMMP and DIMP, based solely on
the individual plots 626 and 630 of FIGS. 16B and 18 is at best
unreliable, and at worst impossible. However, referring to FIGS.
16A and 17, the three-dimensional plots 624 and 636 are easily
visually distinguishable.
[0132] More particularly, the DMMP color plot 624 of FIG. 16A shows
three clear peaks 638, 639 and 640, while the DIMP color plot 628
shows four clear peaks 631, 632, 634 and 636. While the peaks 638,
639 and 640 nearly overlay the peaks 631, 634 and 636, the fourth
blue peak 632 for DIMP, which is lacking for DMMP, easily
distinguishes the DMMP scan from the DIMP scan. Also, the branches
634 and 636 of the color plot 628 are closer together than the
branches 638 and 640 of the color plot 624. Additionally, the color
distribution (e.g., saturation) throughout the branches of the
three-dimensional color plot 624 is not the same as the color
distribution throughout the branches of the plot 628. As in the
case of previously discussed signature scans, three-dimensional
signature scans of the type depicted in FIGS. 15A-18 may be stored
in a library for known compounds. At least portions of one or more
of the stored scans may be compared with at least portions of
similar scans of unknown species to identify and generally analyze
the unknown species. Any suitable pattern matching approach,
including conventional pattern matching approaches, may be employed
for such comparison.
[0133] It should be noted that although the above discussed
dispersion plots of FIGS. 15A, 16A and 17 employ color changes to
indicate intensity, changes in any color-related feature, such as
changes in color saturation, gray scale or black and white may be
employed instead or in combination. Additionally, in a further
illustrative embodiment, the invention generates a curve
circumscribing the intensity peaks, and the color-related
information may be discarded. By way of example, in this
illustrative embodiment, the outlines, for example, for the
intensity peaks 632, 634 and 636 would remain, without the
color-related information. Removing the color-related information
provides a two-dimensional dispersion representation of, for
example, Vrf versus Vcomp that also takes into account the spectral
information gained from aggregating a plurality of Vcomp scans at a
plurality of Vrf values. Any or all of this two-dimensional
information may be incorporated into the above discussed signature
information.
[0134] As described above, various illustrative comparison
approaches may employ pattern matching using, for example, the
above described two- and/or three-dimensional dispersion plots.
However, in other illustrative embodiments, the information
provided by the dispersion plots is stored in the library as
mathematical relationships, and suitable conventional approaches
for comparing such mathematical relationships are employed to
identify the unknown species.
[0135] According to another illustrative embodiment, Vcomp may be
plotted on the x-axis, Vrf on the y-axis, and ion intensity on the
z-axis. Thus, instead of showing ion intensity as color,
saturation, gray scale or black and white variations, as in the
three-dimensional color plots 620, 624, and 628, ion intensity may
be depicted/conceptualized in a topographical manner.
Multi-dimensional signature representations of this sort may also
stored in the library of known species and used in the same fashion
as the above described ion mobility signatures. In other
embodiments of the invention, more than three dimensions may be
employed, for example, plotting spectral data as clusters in
n-dimensional space and employing known cluster matching
algorithms.
[0136] A processor, such as the processor 46 of FIG. 5, may be
programmed in a conventional fashion to automatically step an
analyzer, such as the system 10, through a range of field voltages
Vrf and a scanned Vcomp, and provide the data to a display or other
system for processing and generation of a three-dimensional
dispersion plot.
[0137] Another analysis improving effect can be observed with the
application of relatively high field strengths. Specifically,
complex ion groupings can be fragmented, for example, by applying a
high field strength to the sample. Sample fragmentation is a useful
technique for enhancing species separation, detection, and
identification. Fragmentation includes a process in which large
molecules of samples are broken up into smaller molecules,
components, or fragments prior to sample detection. This enables
the components of the group to be individually detected and more
generally analyzed.
[0138] FIG. 19 is an example of such an effect on a mercaptan
sample. In particular, a range of background voltages (from
620-1450 Vpeak) were applied to an ethyl mercaptan spectra in which
a general shift of ion peak behavior can be seen as an electric
field conditions are strengthened. However, a fragmentation
condition can also be observed. Specifically, at lower applied
field conditions, strong single peak is observed, such as at 701-1.
However, as electric field strength is increased, multiple peaks
701-n, 702, . . . 710 are observed in a spectra. By observing and
recording the peak locations, not only at the low voltage field
conditions, but also at a range of field conditions, this
fragmentation behavior can be further exploited to better identify
compounds. According to one feature, data indicating the peak RF
voltage at which fragmentation occurs is incorporated into the
stored spectral signatures for the known samples. According to
another feature, the locations of the fragment peaks are also or
instead incorporated into the stored spectral signatures for
further use for matching detection data with known data.
[0139] FIG. 20A is a graph 712 of ion intensity (y-axis) versus
field compensation voltage Vcomp (x-axis) illustrating the
separation of detection peaks at different compensation voltages
between light and heavy molecules according to an illustrative
embodiment of the invention. The graph 712 shows that light
molecules associated with the RIP background peak 714 may be
identified at an arbitrary -30 Vdc compensation voltage, while
heavier molecules tend to be clustered and form a peak 716 at about
0 Vdc compensation. By fragmenting a sample of heavy molecules and
detecting the fragments using, for example, a DMS or IMS system, a
plurality of ion intensity peaks, each associated with a fragment,
may be used create a unique signature of the sample to enable
subsequent identification of that sample. Fragmentation of a sample
may be achieved, for example and without limitation, by using any
one or a combination of a chemical reaction, a high energy field at
high strength, high field voltage, heating, laser light, colliding
the sample molecules with other molecules, soft x-ray, or the
like.
[0140] FIG. 20B is a graph 718 of ion intensity (y-axis) versus
field compensation voltage (x-axis) showing the increase in number
of peaks detected after sample fragmentation according to an
illustrative embodiment of the invention. The graph 718 shows that
fragments are lighter, and therefore, have lower mass and higher
associated compensation voltages, resulting in improved resolution
of and differentiation between the fragments. Also, the graph 718
shows an increased number of peaks 720 associated with the
fragmented sample, which increases the collective data that may be
used to fingerprint the compound. The additional detection data
enable a more accurate identification of the detected species, such
as by comparing the signature detected with a set of signatures in
a look up table and by other techniques disclosed herein.
[0141] FIG. 21 is a conceptual block diagram of a dual channel
detection system 748 including a first DMS system 722 using
fragmentation and forming a first channel operating in parallel
with a second DMS system 124 not using fragmentation and forming a
second channel to improve sample analysis according to an
illustrative embodiment of the invention. As shown, the DMS system
724 includes a sample inlet 726, ionization region 728, ion source
730, analyzer region 732, and outlet 734. Similarly, the DMS system
722 includes a sample inlet 736, ionization region 738, ion source
740, analyzer region 742, and outlet 744. The DMS system 722,
however, also includes a fragmentation energy source 746 within the
ionization region 738. The analyzer regions 732 and 742,
respectively, include a DMS filter and detector to enable detection
and identification of samples. In operation, the dual channel
detection system 748 operates DMS systems 722 and 724 concurrently,
simultaneously or alternatively. With respect to the DMS system
724, a sample S is introduced into ionization region 728 via the
sample inlet 726. The ionization source 730 may then ionize the
sample S into positive and/or negative ions that are then delivered
to the analyzer region 732. The analyzer region 732 performs
filtering and detection of the sample which then exits the DMS
system 724 via the outlet 734. The DMS system 722 operates in a
similar manner as the DMS system 724, but with an additional
fragmentation source 746. Thus, when the sample S enters ionization
region 738 of DMS system 724, the fragmentation source 746 breaks
up/fragments the sample S molecules into lighter, less massive
molecules. These lighter molecules are then delivered to analyzer
region 742 for filtering and detection.
[0142] Thus, the dual channel detection system 748 using DMS
systems 722 and 724 may improve sample analysis by substantially
simultaneously analyzing a sample S and its fragments to create a
more complete signature of the sample. Alternatively, the dual
channel detection system 748 may selectively compare the
fragmentation spectra, depending on the sample species to be
detected and the need for better discrimination from other
interferants or compounds.
[0143] FIG. 22 is a conceptual diagram of a DMS system 750, not
using fragmentation, and operating in series with a DMS system 752
using fragmentation to improve sample analysis according to an
illustrative embodiment of the invention. The combination of the
DMS systems 750 and 752 form a serial detection system 754. As
shown, the serial detection system 754 includes a sample inlet 756,
the DMS system 750, the DMS system 752, and an outlet 758. The DMS
system 750 includes an ionization region 760, ion source 762, ion
filter 764, and detector 766. The DMS system 752 includes an
ionization region 768, ion source 770, fragmentation source 772,
ion filter 774, and detector 776.
[0144] In operation, a sample S is introduced into the serial
detection system 754 via the sample inlet 756. The DMS system 750
ionizes the sample S using the ionization source 762 within the
ionization region 760. Then, the ionized sample S is delivered to
the ion filter 764. The ion filter 764 applies a combination of
field and field compensation voltage to the sample S to allow
select ion species to reach and be detected by the detector
766.
[0145] FIG. 23A is a graph 778 of ion intensity (y-axis) versus
Vcomp (x-axis) showing peak detection for the DMS system 750. As
shown previously, when no fragmentation occurs, the relatively
heavy sample molecules cluster to form a peak 780 at
Vcomp=approximately 0 Vdc.
[0146] After analysis by the DMS system 750, the sample S is
delivered to the DMS system 752, where the sample S is ionized by
an ionization source 770, and also fragmented by the fragmentation
source 772. The fragmentation source 772 may be a radioactive
source, a high energy voltage source or the like with enough energy
to break up the relatively large sample molecule into a plurality
of fragment molecules, fragments, components, or atoms. Then, the
fragments are delivered to the ion filter 774 whereupon a
combination of filter field voltages Vrf and field compensation
voltages Vcomp applied a plurality of filter field conditions to
the fragments to filter them before detection by the detector
776.
[0147] FIG. 23B is a graph 782 of ion intensity versus compensation
voltage showing peak detection for the DMS system 752 of FIG. 22
using fragmentation. As shown previously, when fragmentation
occurs, the relatively lighter fragments form a plurality of ion
intensity peaks 784 at various distinct field compensation voltages
Vcomp.
[0148] Thus, the serial detection system 754 using the DMS systems
750 and 752 may improve sample analysis by serially detecting a
sample S and its fragments to create a more complete signature or
fingerprint of the sample. Alternatively, the serial detection
system 754 may selectively compare the fragmentation spectra
depending on the sample species to be detected and the need for
better discrimination from other interferants or compounds.
[0149] FIG. 24 is a conceptual block diagram of a DMS system 786
including a fragmentation region 792 according to an illustrative
embodiment of the invention. As shown, the DMS system 786 includes
a sample introduction region 788, ionization region 790,
fragmentation region 792, fragmentation source 806, fragmentation
effluent inlet 794, transport effluent inlet 796, ion filter 798,
detector 800, and controller 812. An ionization source 802 may
optionally be located within the fragmentation region 792. An
ionization source 804 may optionally be located within ion filter
798.
[0150] In operation, a sample S is introduced into sample
introduction region 788. The sample introduction region 788 may
perform pre-separation of the sample S to reduce the amount of
interferants or unwanted compounds. The ionization source 808 then
ionizes the sample S in the ionization region 790. Once the sample
S is delivered to the fragmentation region 792, the fragmentation
source 806 fragments the relatively heavy molecules of the sample S
into a plurality of lighter fragments. Alternatively, a
fragmentation gas including fragmentation molecules may be
introduced into fragmentation region 792 via fragmentation gas
inlet 794. The fragmentation gas molecules, upon colliding with the
sample S molecules, cause a portion of the sample S molecules to
break up into sample S fragments.
[0151] After fragmentation, a transport effluent, such as a carrier
gas CG may be introduced via the transport effluent inlet 796 to
deliver the sample S fragments to the ion filter 798. After
filtering, the fragments are then detected by the detector 800. The
ionization source 802 may optionally be located in the
fragmentation region 792. Furthermore, as in the case of all of the
previously described illustrative embodiments, the fragmentation
source 806 may function additionally as a ionization source. The
ionization source 804 may optionally be located in the ion filter
798. Furthermore, the ion filter 798 may also act as either a
fragmentation source 810 or an ionization source 804.
[0152] It should be noted that although the previously described
embodiments refer to separate ionization and fragmentation sources,
in other illustrative embodiments, a single source may attend to
both fragmentation and ionization. Additionally, any of the
previously described fragmentation approaches may be employed in
addition to or in replacement of the fragmentation sources of FIGS.
21, 22 and 24. The controller 821 may switch fragmentation on and
off as needed by activating or deactivating the fragmentation
source 806 or by introducing or not introducing a fragmentation
effluent via fragmentation effluent inlet 794.
[0153] The foregoing fragmentation techniques and system
implementing these fragmentation techniques may be used to enhance
the detection of a sample S, such as without limitation, Sarin gas,
also known as: [0154] GB [0155] Zarin [0156] Phosphonofluoridic
acid, methyl-, isopropyl ester [0157] Phosphonofluoridic acid,
methyl-, 1-methylethyl ester [0158] Isopropyl
methylphosphonofluoridate [0159] Isopropyl ester of
methylphosphonofluoridic acid [0160] Methylisoproposfluorophosphine
oxide [0161] Isopropyl Methylfluorophosphonate [0162] 0-Isopropyl
Methylisopropoxfluorophosphine oxide [0163] 0-Isopropyl
Methylphosphonofluoridate [0164] Methylfluorophosphonic acid,
isopropyl ester [0165] Isoproposymethylphosphonyl fluoride
[0166] Sarin, a colorless and odorless gas, has a lethal dose of
0.5 milligram for an adult. It is 26 times more deadly than cyanide
gas and is 20 times more lethal than potassium cyanide. Just 0.01
milligram per kilogram of body weight in a pinprick sized droplet
will kill a human.
[0167] FIG. 25 is a three-dimensional color dispersion plot 814 of
the type described above with respect to FIGS. 15A-18 and
illustrating detection of agent GA over a range of field voltages
Vrf and field compensation voltages Vcomp with varying ion
intensity presented in varying color according to an illustrative
embodiment of the invention. The color dispersion plot 814 includes
branches 816, 818, 820, and 822 that represent the detection of
fragments of agent GA using, for example, DMS system 786 having a
Ni63 ionization source for fragmentation of the GA sample at 0.14
ng/l. The branch 840 represents an original peak before
fragmentation.
[0168] FIGS. 26A-26H depict two-dimensional graphs 824, 826, 828,
830, 832, 834, 836, and 838 of ion intensity (y-axis) versus Vcomp
(x-axis), each at a particular Vrf. As described above with respect
to FIGS. 15A-18, the two-dimensional graphs 824, 826, 828, 830,
832, 834, 836, and 838 are aggregated into the three-dimensional
color dispersion plot 814 of FIG. 25. As discussed previously, the
color dispersion plot 814 improves the analysis process of a
particular species such as agent GA or GB, for example, because it
takes into account peak shifts due to changes in Vrf, and because
the color nature of the three-dimensional dispersion plot 814 makes
more evident the signature behavior of particular ion species in
relation to other ion species, especially after fragmentation.
[0169] As described above with respect to FIGS. 15A, 16A, and 17,
the dispersion plot of FIG. 25, may employ color saturation, gray
scale variations, black and white variations and/or peak outlines
in place of the color variations depicted.
[0170] The fragmentation techniques described herein are not
limited to DMS systems and may be employed with other
mobility-based detection systems such as ion mobility spectrometry
(IMS), time of flight (TOF) IMS, gas chromatography (GC), Fourier
transform infrared (FTIR) spectroscopy, mass spectrometry (MS),
liquid chromatography mass spectrometry (LCMS), surface acoustic
wave (SAW) sensors, and the like.
[0171] Another technique for improving ion species detection,
identification and analysis generally is operating the
mobility-based detection system, such as any of the systems
described herein, below atmospheric pressure. By operation below
atmospheric pressure, the separation between ion intensity
detection peaks is increased and the width of the peaks is
narrowed. This provides for improved the resolution, resulting in
improved system discrimination and sensitivity. By operating, for
example a DMS system at various pressure conditions, the change in
ion species behavior with respect to pressure may be measured and
used as another characteristic for identifying ion species.
According to various illustrative embodiments, the invention
performs ion scans at pressures between about 0.2 and about 0.9
atmospheres, less than about 0.3 atmospheres, less than about 0.4
atmospheres, less than about 0.5 atmospheres, less than about 0.6
atmospheres, less than about 0.7 atmospheres, or less than about
0.8 atmospheres.
[0172] FIG. 27A is a graph 840 of background (RIP) ion intensity
versus field compensation voltage at a plurality of pressures for a
DMS system in positive ion detection mode according to an
illustrative embodiment of the invention. The graph 840 shows that
the field voltage may be adjusted to maintain the ion intensity
peak within the same compensation voltage position as the pressure
within a DMS system is adjusted. More specifically, according to
the graph 840, as the pressure decreases, the field voltage
decreases to maintain the ion intensity peak for a species at the
same compensation voltage. Furthermore, changes in pressure at
lower pressures result in the need for greater changes in field
voltage to maintain a constant compensation voltage. For example,
when reducing the pressure by approximately 100 mmHg from 760 mmHg
to 655 mmHg, the reduction in field voltage is approximately 40
Vpeak from about 1050 Vpeak to about 1010 Vpeak. For approximately
the same pressure reduction from 655 mmHg to 556 mmHg, the
reduction in Vrf is approximately 90 volts from about 1100 Vpeak to
about 920 Vpeak. Thus, the field voltage decrease is approximately
twice as great for changes in pressure in the 600 mmHg range, which
indicates that the resolution is improved at reduced pressure.
[0173] FIG. 27B is a graph 842 of background (RIP) ion intensity
versus field compensation voltage at a plurality of pressures for a
DMS system in negative ion detection mode according to an
illustrative embodiment of the invention. Like positive mode graph
840, the graph 842 shows that, in negative detection mode, the
field voltage may be adjusted to maintain the ion intensity peak
within the same compensation voltage position as the pressure
within a DMS system is adjusted.
[0174] As shown by comparing the graph 840 with the graph 842,
there is an offset in the ion intensity peak between the positive
mode ion intensity peaks of graph 840 and negative mode ion
intensity peaks of graph 842 at the same pressure and field
voltage. This offset may indicate a difference in the alpha
parameter between positive and negative mode detection for an ion
species. The alpha parameter is discussed in further detail below.
The DMS flow rate is approximately 300 cc/min in graphs 840 and
842.
[0175] FIGS. 28A and 28B depict graphs 844 and 846, respectively,
of ion intensity (y-axis) versus pressure (x-axis) showing a
quantifiable effect on positive and negative background spectra,
respectively, caused by a decrease in pressure according to an
illustrative embodiment of the invention. More specifically, the
graph 844 shows that field voltage is decreased by about 50% when
pressure is decreased to about 0.3 atmosphere (atm). The graph 846
also shows a similar field voltage decrease of about 50% when
pressure is decreased to about 0.3 atm.
[0176] FIGS. 29A and 29B depict graphs 848 and 850, respectively,
showing ion intensity (y-axis) versus field compensation voltage
(x-axis) for a plurality of pressures and showing the effect of
varying pressure on negative and positive tert-butylmercaptan and
tert-butylithiol (TBM) spectra, respectively. While the graphs 848
and 850 show that field voltage decreases as pressures decreases
for a particular field compensation voltage, the graphs 848 and 850
also show that the ion intensity peak positions for TBM spectra
shift in the opposite direction as the ion intensity peak shifts
for the background (RIP) spectra of graphs 840 and 842.
Furthermore, the level of change of the ion intensity peaks in
graphs 848 and 850 for TBM spectra is less than the level of change
of the ion intensity peaks in graphs 840 and 842 for background
spectra.
[0177] FIGS. 30A and 30B depict graphs 852 and 854 showing ion
intensity (y-axis) versus pressure (x-axis) and showing the effect
of varying pressure on negative and positive TBM ion peak
parameters, respectively. More specifically, the graph 852 shows
that the ion intensity peak remains relatively constant as the
pressure is varied for negative ion spectra. The graph 854 shows
that the ion intensity peak remains relatively constant with the
level decreasing slightly at a lower pressure for positive spectra.
Because changes in pressure impact the background (RIP) and analyte
spectra differently, pressure may be manipulated, regulated, or
otherwise controlled in such a manner as to improve the ability of
a DMS system to detect and identify ion species with better
resolution while minimizing the negative effects of background
spectra interference.
[0178] In certain embodiments, it may be desirable to maintain
uniform detection results by maintaining a constant ratio of
electric field strength to gas density N or pressure P where the
ratio is expressed as E/N or E/P. Thus, when the gas operating
pressure within a DMS system is decreased, the field voltage is
correspondingly lowered to maintain a constant E/N or E/P. This
reduction in field voltage results in a reduction in power
consumption which, in turn, results in smaller, lighter weight, and
lower cost detection systems.
[0179] FIG. 31 is a graph 856 showing the effect of reduced
pressure on analyte peaks for chemical warfare agents, such as
DMMP, DIMP, and MS. The top graph 857 show the ion intensity
results at atmospheric pressure, while the bottom two graphs 859
and 861 show the results at 0.65 and 0.5 atm, respectively. At 1
atm with field voltage at Vrf=about 1000 Vpeak, the top spectra
shows the overlap 858 of monomer and dimmer cluster peaks for DIMP
over a range of about 10 Vdc field compensation voltage. But at
0.65 atm and Vrf=about 800 Vpeak, the monomer peak 860 and cluster
peak 862 are separated with the monomer peak 860 at Vcomp=about -3
Vdc and cluster peak 862 at Vcomp=about +1 volt. At 0.5 atm and
Vrf=about 650 Vpeak, the DIMP monomer peak 864 and DIMP cluster
peak 866 are each narrower with the peaks 864 and 866 at
Vcomp=about -2.5 Vdc and about +1 Vdc, respectively. The narrower
peaks 864 and 866 at 0.5 atm result in higher resolution for a DMS
system.
[0180] FIGS. 32A-32D depict graphs 868, 870, 872, and 874,
respectively, showing ion intensity (y-axis) versus Vcomp (x-axis).
The graphs 868, 870, 872 and 874 show improved detection resolution
for agent GF at reduced pressures, according to an illustrative
embodiment of the invention. The graphs 868 and 870 show the ion
intensity spectra of agent GF at Vrf of 1500 and 1000 Vpeak,
respectively, at 1 atm. The graphs 872 and 874 show the ion
intensity spectra of agent GF at Vrf of 1000 and 750 Vpeak,
respectively, at 0.5 atm. According to the graph 870, the monomer
and dimer peaks overlap at peak 876 at Vrf=about 1000 Vpeak.
According to the graph 868, however, the monomer peak 878 and dimer
peak 880 are separated at Vrf=about 1500 Vpeak. Thus, DMS system
resolution may be increased by increased the field voltage
(Vrf).
[0181] In the graph 872, the DMS system pressure is reduced to
about 0.5 atm with Vrf at about 1000 Vpeak. The graph 872 shows the
monomer peak 882 clearly isolated from any dimer peak, because the
cluster or dimer RIP peaks are off-scale of the graph 872. In the
graph 874, the field voltage Vrf is reduced to about 750 Vpeak,
with a system pressure at about 0.5 atm. The graph 874 shows clear
separation of the GF monomer peak 884 from the dimer peaks 886 and
RIP peak 888. Thus, GF may be detected and identified by the
signature peaks illustrated in graph 874 in a DMS system utilizing
reduced pressure, reduced field voltage, and, therefore, reduced
power.
[0182] As described above, three-dimensional color dispersion plots
may be used to significantly enhance the ability of a DMS system to
detect and identify ion species of interest by allowing a user or
pattern recognition program to match the color patterns against a
library of similar color pattern for known compounds.
[0183] FIG. 33 is a three-dimensional color dispersion plot 890
depicting intensity of positive ions of 0.005 mg/m.sup.3 DIMP at
about 0.65 atm and over a range of field strengths, gas densities
(E/N) and field compensation voltages Vcomp. As shown, gas density
is plotted on the x-axis, Vcomp is plotted on the y-axis, and
variations in intensity depicted by variations in color. The plot
890 includes several prominent branches 892, 894, and 896.
[0184] FIG. 34 plots the same information as FIG. 33, except as
obtained at a decreased pressure of about 0.50 atm. As shown in
plot 898, the reduction in pressure in relation to plot 890,
results in significantly more prominent branches 900, 902, and 904,
thus providing enhanced resolution.
[0185] FIG. 35 is a graph depicting positive (906) and negative
(908) mode three-dimensional color dispersion plots for about 0.85
mg/m.sup.3 of agent GB RIP, at a relative humidity (RH)=about 87%,
in a DMS system operating at about 0.5 atm for a fragmented sample.
The negative mode plot 908 shows only a single strong RIP branch
909, while the positive mode plot 906 shows two strong trace
analyte peaks 901 and 903 to the right of the heavy background RIP
branch 905. Thus, plotting three-dimensional graphs for both the
positive and negative ion species of a sample provides further
enhanced ion species identification over three-dimensional plots of
positive or negative mode measurements alone.
[0186] The three-dimensional color dispersion plots 906 and 908, as
illustrated above, may also show discontinuities in the branches,
i.e., peak plots or traces, that are also useful for species
identification. For example, the plot 906 includes a break in the
trace or branch 901 that may be included as part of the stored
signature for future comparisons.
[0187] As described above with respect to FIGS. 15A, 16A, 17 and
25, the dispersion plots of FIGS. 33 and 35, may employ color
saturation, gray scale variations, black and white variations
and/or peak outlines in place of the color variations depicted.
[0188] According to another feature, the identification above
described analysis approaches may be made device-independent. FIGS.
36A and 36B show experimental detection data for a homologous group
of ketones, including: acetone, butanone, pentanone, hexanone,
heptanone, octanone, nonanone, decanone. FIGS. 37 and 38 are tables
showing monomers and clusters, respectively, for the above listed
keytone species. As shown in FIGS. 36A and 36B, each species has a
unique mobility curve, and thus a unique mobility signature, for
the given set of field conditions. As described above, the mobility
signatures may be obtained and enhanced in any of a plurality of
ways. However, the identification process can be further enhanced
by making it device-independent. With device independence,
signature data can be created that can be used on any device.
According to one illustrative embodiment, the invention
accomplishes this by determining the parameters of a function
derived from the fundamental mobility coefficient associated with
each species.
[0189] Therefore, for example, the multiple data represented in
FIGS. 36A, 36B, 37 and 48 each can be used to provide positive
identification of a detected species by the unique and inherent
mobility characteristic that identifies that species. According to
one feature, the comparison can be made to a lookup library
specific to the device in question, but also can be made to a
universal set of data that is device-independent. Thus, in general,
one does not wish to only compare the plot of abundance curves
versus compensation voltage individually, but rather generate a
plot of observed peak locations for specific compensation voltages,
so that curves, slopes, signs, and various details can be discerned
for each of the detected ions for comparison to a library of lookup
data.
[0190] More specifically, in computing mobility signatures, we have
found that an expression of the field-dependence of ion mobility,
the so-called a coefficient, expressed as a function of field, can
be used to generate a unique a function that is inherent for that
species and is device independent. Thus the a function can be used
as the unique signature of a species; this function expresses both
a characteristic signature for the ion species and is device
independent. In short, according to one feature, the invention
recognizes that peaks change position in signature ways because
they have different alpha signatures.
[0191] In one illustrative embodiment, the invention employs the a
function as a mobility signature for detected species. The
signature can be determined for a detected unknown compound, based
on the field conditions that are used, and then this can be used to
make an identification according to a lookup table of stored known
signature data associated with known compounds. More particularly,
in practice of a preferred embodiment of the invention, ion species
are identified based on the mobility dependence of the species
under various field conditions. Data is collected for the sample
under test for at least two field conditions, the data is
processed, and a comparison of detection data computed as an a
function for the sample under test versus the stored data enables
identification of the compounds in the sample.
[0192] Referring again to the discussion of the a parameter, FIG. 3
is a plot of mobility versus electric field strength for three
examples of ions, with field dependent mobility (expressed as the
coefficient of high field mobility, .alpha.) shown for species at a
greater, equal to and less than zero. For any given set of field
conditions, the field strength and compensation can be correlated
with an .alpha.) value. This is shown in the work of Buryakov et.
al., A New Method Of Separation Of Multi-Atomic Ions By Mobility At
Atmospheric Pressure Using A High-Frequency Amplitude Asymmetric
Strong Electric Field, Intl J MassSpec and Ion Proc. (1993), at p.
145.
[0193] We have observed that knowing the .alpha. parameter alone at
a particular field strength does not prevent false positives. This
would occur at the intersection of the two plots in FIG. 4, at the
point indicated by reference numeral 100. Without more information,
knowledge of the .alpha. parameter for the respective ion species
at that location does not provide unique mobility signatures for
both compounds. Thus, without doing more, any number of readings at
this intersection is likely to result in a detection error.
[0194] However, we have also found that we can express an ion's a
mobility characteristic as a function of field, i.e., as
.alpha.(E), and can define a unique mobility signature for the ion
species which is device-independent. This .alpha.(E) or "alpha
function" relates the size, effective cross-section, shape, and
mass of the ion to field conditions. It is understood that as the
applied electric field increases, the increasing electric field
tends to displace, stretch, and/or breaks the bonds of the ion such
that the stronger the field, the greater the induced dipole,
quadripole, or higher order moments of the ion. These, in turn,
affect the relative mobility of the specific ion. The result of
relating these aspects is to define a unique mobility signature for
the ion species of interest. This also turns out to be
device-independent.
[0195] The relationship of the .alpha.(E) function to field
conditions is shown in the following: V c .function. ( E ) = <
.alpha. .times. .times. E s .times. f .function. ( t ) > 1 +
< .alpha. > + < d .alpha. d E .times. E s .times. f
.function. ( t ) > ( 1 ) ##EQU1## where: Vcomp (peak position);
Es-electric field strength; f(t)-waveform parameters (wave shape
and so forth).
[0196] Thus, for each spectral detection, we can compute a as a
function of field conditions, i.e., .alpha.(E). Specifically, the
asymmetric waveform in a planar field asymmetric waveform mobility
spectrometer, E.sub.max(t)=E.sub.maxf(t), is designed to satisfy
the following conditions: 1 / T .times. .intg. 0 T .times. E s
.function. ( t ) .times. d t = < E s .times. f .function. ( t )
>= 0 ( 3 .times. a ) < f 2 .times. n + 1 .function. ( t )
> .noteq. 0 ( 3 .times. b ) ##EQU2## where f(t)--is a normalized
function which describes the waveform, and E.sub.max is the maximum
amplitude of the waveform. The waveform is designed such that its
average value is zero (equation 3a) while the polarity of the
electric field during one period is both positive and negative. The
addition of the compensation field, C, to the waveform E.sub.s(t)
yields Equation 4: E(t)=E.sub.s(t)+C=E.sub.sf(t)+C (4) so the
average ion velocity over a period of the asymmetric waveform can
be written as: V=<V(t)>=<K(E)E(t)> (5) Only ions with
average velocity of zero, v=0, will pass through the gap without
neutralization. An expression for the compensation field required
to enable an ion to pass through the gap can be obtained by
substituting Equations 2, 3, and 4 into Equation 5 as shown in
Equation 6: C = < .alpha. .times. .times. E s .times. f
.function. ( t ) > 1 + < .alpha. . > + < d .alpha. d E
.times. E s .times. f .function. ( t ) > . ( 6 ) ##EQU3## The
value of this compensation electric field can be predicted
precisely when the alpha parameter for the ion species, the
waveform f(t), and the amplitude of the asymmetric waveform
E.sub.max are known.
[0197] A procedure for extraction of .alpha.(E) from experimental
measurements of the electric field dependence of the mobility scans
is thus known. In this section, some additional considerations
regarding the alpha parameter and methods to determine this
parameter described. First, emphasis must be given that the alpha
parameter is a function (not a number) and the physical and
chemical information about an ion is contained in the shape of the
.alpha.(E) curve. The method of representing this curve is
incidental to the topic. The only criterion critical in these
methods is that the calculated values for mobility (i.e.
K(E)=K.sub.o{1+.alpha.(E)]) should be as close as possible to the
experimental values. The function for .alpha.(E) can be represented
as an even power series or in complex form. In either instance, the
curves of experimental results and calculated should agree closely.
Thus, the quality of the approximation is limited by the accuracy
of the experimental results and has been illustrated. Discerning
the quality of a model based upon two parameters, three parameters,
or a nonlinear function with five parameters was difficult. All
approximations were located within the error of .DELTA.C.sub.1 (at
.+-.9%).
[0198] In this work, a simple uniform method is described to
represent the function of .alpha.(E), which will be suitable for
comparison of results obtained under different experimental
conditions. These methods could be used for differing asymmetric
waveforms or different designs of IMS drift tubes: linear,
cylindrical, or planar DMS.
[0199] In general then, the criteria for choosing the level of
approximation of alpha is first to ensure that the method of
extracting the alpha parameter uses the least number of individual
parameters of the experimental device. Second, the result should
contain the fewest number of adjustable parameters, and the
approximation curves should be within the experimental error bars.
In the next section, the general method to extract the alpha
parameter is described and then applied in the subsequent
section.
[0200] The function of .alpha.(E) can be given as a polynomial
expansion into a series of electric field strength E degrees as
shown in Equation 7: .alpha. .function. ( E ) = n = 1 .infin.
.times. .alpha. 2 .times. n E 2 .times. n ( 7 ) ##EQU4##
Substituting Equation 7 into Equation 6 provides a value of the
compensation voltage as shown in Equation 8 where an uneven
polynomial function is divided by an even polynomial function.
Therefore an odd degree polynomial is placed after the identity
sign to approximate experimental results: C = n = 1 .infin. .times.
.alpha. 2 .times. n .times. S 2 .times. n + 1 .times. f 2 .times. n
+ 1 .function. ( t ) 1 + n = 1 .infin. .times. ( 2 .times. n + 1 )
.times. .alpha. 2 .times. n .times. S 2 .times. n .times. f 2
.times. n .function. ( t ) .ident. n = 1 .infin. .times. c 2
.times. n + 1 .times. S 2 .times. n + 1 .times. f 2 .times. n + 1 (
8 ) ##EQU5## This allows the a comparison of the expected
coefficient (approximated) to be compared to the values of alpha
parameter as shown in Equation 9: c 2 .times. n + 1 = .alpha. 2
.times. n .times. f 2 .times. n + 1 - k = 1 n - 1 .times. ( 2
.times. ( n - k ) + 1 ) .times. c 2 .times. k + 1 .times. .alpha. 2
.times. ( n - k ) .times. f 2 .times. ( n - k ) ( 9 ) ##EQU6##
Alternatively, alpha parameters can be calculated by inverting the
formula by using an approximation of the experimental results per
Equation 10: .alpha. 2 .times. n = 1 f 2 .times. n + 1 .times. { c
2 .times. n + 1 + k = 1 n - 1 .times. ( 2 .times. ( n - k ) + 1 )
.times. c 2 .times. k + 1 .times. .alpha. 2 .times. ( n - k )
.times. f 2 .times. ( n - k ) } ( 10 ) ##EQU7##
[0201] Any number of polynomial terms (say 2n), in principle, can
be determined from Equation 10 though a practical limit exists as
the number of polynomial terms in the experimental result of the
approximation c.sub.2n+1 should be higher than the expected number
of alpha coefficients .alpha..sub.2n. Since the size of n depends
on the experimental error, the power of the approximation of the
experimental curves C(E.sub.s) cannot be increased without limit.
Usually N experimental points of C.sub.i(E.sub.si) exist for the
same ion species and experimental data can be approximated by the
polynomial using a conventional least-square method. Finally, the
number series terms cannot exceed the number of experimental points
so increasing the number of series terms above the point where the
fitted curves are located within the experimental error bars in
unreasonable. In practice, two or three terms are sufficient to
provide a good approximation shown in prior findings. The error in
measurements must be determined in order to gauge the order of a
polynomial for alpha. The sources of error in these experiments
(with known or estimated error) were: [0202] 1. Error associated
with measurement and modeling of the RF-field amplitude
(.about.5%); [0203] 2. Error in C(E.sub.s) from a first-order
approximation of Equation 4 (.about.3%), and [0204] 3. Error in
measuring the compensation voltage (.about.5-8%). An approximate
error may be .about.10% and there is no gain with approximations
beyond two polynomial terms; thus, alpha can be expressed as
.alpha.(E/N)=1+.alpha..sub.1(E/N).sup.2.alpha..sub.2(E/N).sup.4
with a level of accuracy as good as permitted by the
measurements.
[0205] A standard least-square method (regression analysis) was
used to approximate or model the experimental findings. For N
experimental points with C.sub.i(E.sub.si) and for
C=C.sub.3S.sup.3+c.sub.5S.sup.5 a function y=c.sub.3+c.sub.5x can
be defined where y=C/S.sup.3; x=S.sup.2 so c.sub.5 and c.sub.3 are
given by Equations 11 and 12, respectively: c 5 = i = 1 N .times. x
i .times. i = 1 N .times. y i - N .times. i = 1 N .times. x i
.times. y i ( i = 1 N .times. x i ) 2 - N .times. i = 1 N .times. x
i ( 11 ) c 3 = 1 N .times. ( i = 1 N .times. y i - c 5 .times. i =
1 N .times. x i ) ( 12 ) ##EQU8## Through substituting experimental
value C.sub.3, C.sub.5, values for .alpha..sub.2 and .alpha..sub.4
can be found per Equations 13 and 14: .alpha. 2 = c 3 / f 3 ( 13 )
.alpha. 4 = c 5 + 3 .times. c 3 .times. .alpha. 2 .times. f 2 f 5 (
14 ) ##EQU9## In order to calculate .alpha..sub.2n, knowledge is
needed for the approximations of experimental curves for C(E.sub.s)
and for the function f(t)--which is a normalized function
describing the asymmetric waveform.
[0206] For example, nine data points were identified for each of
the eight ketones of FIGS. 36A, 36B, 37, and 38, based on the data
collected in the tables of FIGS. 37 and 38. These can be used to
compute the .alpha. curve for that species, such as with a
piecewise linear approximation to the .alpha. curve. For example,
two data points for butanone are a(Vcomp-a, Vrf-a) and b(Vcomp-b,
Vrf-b). Between these two points, the slope and sign of the
butanone curve can be computed. More complete characterization of
the curve, such as with polynomial curve fitting, is also
possible.
[0207] Now this data set becomes part of a data store for use in
identification of the species of an unknown detected ion species
for which two data points are collected and the corresponding curve
data is computed. In short, in an illustrative practice of the
invention, we collect data on at least two closely associated
points (peaks) for a given ion sample and generate the curve data
accordingly. Once we have the detected and computed data, we assume
this approximates the alpha curve and therefore do a lookup to our
stored data. Upon finding a match, we can then positively identify
the sample.
[0208] In FIGS. 39A and 39B (monomers and clusters, respectively)
we computed unique a curves for keytone ions (acetone, butanone,
pentanone, hexanone, heptanone, octanone, nonanone, decanone) based
on data collected in the tables of FIGS. 37 and 38, plotting the
percent change in a against the change of field strength for the
various data collected. These plots of percent change in a against
field strength express a unique signature for each of these ion
species. This is loaded in our data store for later comparison: the
signature data includes the RF field strength and the compensation
voltage at which the peak is detected, we also associate with it
the identifying data for the known a function associated with that
detected peak location and field conditions for each species.
[0209] FIGS. 39A and 39B thus express the .alpha. function for
individual ketones spanning electric fields of 0 to 80 Td
(.about.23 kV/cm), expressed as a percentage change in alpha as a
function of field conditions. These plots are fundamental signature
features of these ion species that are independent of the drift
tube parameters and can be used in other mobility spectrometers.
Thus, the .alpha. function can be favorably used in practice of the
invention to provide a mobility identification data set that is
device-independent.
[0210] These results are surprising and demonstrate that for
chemicals with the same functional group, protonated monomers of a
single type exhibit a broad range of behavior vis-a-vis the
dependence of coefficients of mobility on electric fields. This
difference in behavior for a common moiety suggests that the effect
from the electric field must be associated with other aspects of
molecular structure. One possible interpretation is that ions are
heated during the high field and the effect on the protonated
monomer should be striking. These ions with structures of
(H.sub.3O).sup.+M (H.sub.2O).sub.n or perhaps (H.sub.3O).sup.+M
(H.sub.2O).sub.n(N.sub.2).sub.2, should be prone to dissociations
with slight increases in ion temperature caused by the high field
conditions. Thus, ion cross-sections and mobilities would accompany
declustered small ions at high fields.
[0211] Referring again to FIG. 39A, it should be noted that there
is approximately a 20% increase in .alpha.(E) for the protonated
monomer of acetone with high fields. As the molecular weight of the
keytone is increased, ion heating is less pronounced and reflected
in the .alpha.(E) function. The .alpha.(E) function for proton
bound dimers (clusters) is consistent with decreases in mobility
under high field conditions. Consequently, the basis for the
.alpha.(E) function differs from that of protonated monomers.
Indeed, the proton bound dimer for decanone undergoes about a 5%
decrease at high fields. The cause for a decrease in mobility at
high fields has no existing model but should be due to increased
collisional size or increased strength of interaction between the
ion and the supporting gas.
[0212] Furthermore, if we were to do the same for the cyclohexane
and DMMP in FIG. 4, the computed alpha curves would differ
accordingly. In this manner, the invention can distinguish ion
species even when their mobility curves overlap, as long as we have
at least a second detection data set to associate with each
detected species in question. Therefore, the invention achieves a
high level of assurance for the accuracy of identifications.
[0213] Thus we have shown that the fundamental dependence of
mobility for ions in high electric field can be obtained from field
asymmetric ion mobility spectrometry. Functions of dependence can
be extracted from experiments using known methods to treat
imperfect waveforms. These findings show an internal consistency
with a homologous series of ketones, and also indicates a mass
dependence not previously reported.
[0214] Focusing attention now on FIGS. 40A-40F a specific sequence
of steps is described that may be carried out to perform species
identification in several of the embodiments of the invention.
These steps are provided by way of illustration and not limitation.
In this illustration, the sequence of steps may be performed by the
microprocessor 46 of the ion mobility spectrometer device 10 of
FIG. 5. The microprocessor 46 provides digital control signals to
the RF dispersion voltage (Vrf) generator 42 and compensation
voltage (Vcomp) generator 44 to control the drive voltages for the
filter 24. The voltage generators 42 and 44 may also include, for
example, digital-to-analog converters, not shown in detail in FIG.
5.
[0215] The microprocessor 46 coordinates the application of
specific RF dispersion voltages Vrf and compensation voltages
Vcomp, also taking into account the function of observing responses
from the detector 26 as read through the analog to digital
converter 48. By detecting attributes (such as the peaks) of
observed abundances of a particular ion species across a range of
Vrf voltages, the microprocessor 46 can thus take steps to identify
particular compounds. These may include, for example, comparing or
correlating particular "response curve" data against a library of
response curve data as stored in the memory 47. They can also
include computation of a curve parameters. The results of the
comparison operation can be provided in the form of an appropriate
output device such as a display or personal computer or the like,
or maybe provided by electrical signals through an interface to
other data processing equipment.
[0216] As shown more particularly in FIG. 40A, a state 1000 is
entered into the microprocessor 46 in which a compound is to be
analyzed. Here, the compound is known and identified, such as by a
user supplying an identifying text string to the computer. A
sequence of steps is then performed by which data is to be acquired
concerning the known chemical compound. From this state 1000, a
next state 1002 is entered in which a range of dispersion voltages
Vrf and compensation voltages Vcomp are determined by the processor
46. These ranges include a beginning voltage (b) and an end voltage
(s) and step voltage(s) to be applied to each of the ranges Vrf is
thus varied from an initial value Vrf(b) to a final value Vrf(e) by
a step amount Vrf(s). Similarly, Vcomp is to be varied from
Vcomp(b) to a final value Vcomp(e) by a step amount Vcomp(s).
[0217] The voltage ranges are then applied in the following steps.
Specifically, step 1004 is entered in which the Vrf is allowed to
step through a range of values. A state 1008 is entered next in
which the compensation voltage Vcomp is also swept or stepped
through a series of values or ranges. In state 1010, the response
to each applied voltage is stored as a value, (a).
[0218] If the last compensation voltage has not yet been tested,
then processing returns to state 1008 in which the next
compensation voltage is applied. However, in state 1012, if all of
the compensation voltages have been applied, then processing
proceeds to a state 1014 wherein a test is made to see if all of
the dispersion have been applied.
[0219] The loop continues until all of the compensation and
dispersion voltages have been applied. The resulting set of data is
then analyzed in a state 1018 to identify features of interest. In
the specific example being described, it is the peak locations that
are of interest. For each such peak in an observed response for a
given applied dispersion voltage Vrf, a response value for a
specific Vcomp is determined and its corresponding amplitude (a) is
detected and stored.
[0220] The response curve data, or certain attributes thereof such
as the peak locations are then stored as a data object P (or table)
as shown in FIG. 40B. Such an object illustratively contains an
identification of the tested compound such as a text string. Also
stored are a set of the applied dispersion voltages Vrf. For each
such dispersion voltage Vrf, a corresponding peak compensation
voltage is stored. Specifically, at least the compensation voltage
Vcomp at which a peak was observed, and preferably, the
corresponding amplitude of the response (abundance) observed at
that peak is stored.
[0221] As previously described in detail, for a given Vrf, there
may be a set of compensation voltages at which a number of "peaks"
are observed. For example, as was described in connection with FIG.
14A, the sample analyzed can be made up of a compound of specific
ions, including monomers, cluster ions, and reactant ion peaks.
Thus, illustratively, there is an accommodation in the structure of
object P to anticipate that there will be more than one peak
observed in any particular mobility scan, and that the number of
peaks per response curve may not always be the same number.
[0222] An example, the illustrative object P of FIG. 40B includes a
data element, where for a single RF dispersion voltage Vrf-1, peaks
may be observed at compensation voltages Vc11, . . . , Vcmn having
corresponding amplitudes all, . . . , amn. This may correspond to
the case of the lowest applied dispersion voltage in FIG. 14A,
where numerous peaks 601-, 605-1, 608-1 are detected. However, at
another dispersion voltage Vrf-m, only a single peak at Vcomp-m, am
was detected. This might correspond to a case such as in the
uppermost curve of FIG. 6A, where only a single peak 601-m was
detected.
[0223] In an illustrative application, a library of data objects P
(reference vectors) is developed by performing the steps of FIG.
40A for a plurality of known compounds of interest. This then
permits an instrument to eventually enter a chemical recognition
state 1200 as shown in FIG. 40C. Next, a series of measurements are
taken in states 1202-1214. This series is similar to the
measurements taken in FIG. 40A. Specifically, a series of
measurements are taken for a specified compensation and RF
voltages. It should be understood that an entire set of all of the
same measurements need not be taken in this mode as were taken in
the chemical data acquisition mode. Specifically, not all points on
a relatively dense response curve need to be taken, only enough to
identify each compound.
[0224] Once the measurements are taken, a state 1220 is entered in
which features, such as peaks of the response are identified for
each peak a corresponding compensation voltage and amplitude may be
identified and these stored to a candidate measurement vector P'.
The candidate vector P' thus represents a series of data that need
to be tested against a number of candidate compounds. The candidate
vector P' is then analyzed in states 1230 and/or 1240 by looking up
corresponding counterparts in the library of reference vector
objects P, and scoring a match between P and P'. These steps may be
iterated until such time as a match or a best match is determined
in a state 1250.
[0225] It should be understood that any number of techniques may be
used to determine a degree of match between P and P'. For example,
if the elements (Vcomp, a) of P and P' are considered to be data
points in Euclidian geometry space, a distance can be computed. The
comparison with the smallest Euclidian distance can then be
selected as the best match. However, other recognition techniques
may be used to determine an identity of an unknown compound, for
example, more sophisticated signal processing techniques such as
correlation may be used to resolve peaks; or other known pattern
recognition algorithms, neural networks or artificial intelligence
techniques may be used to find a best match for P'. This best match
is then identified to a user, such as by looking up the compound
identifier field and displaying in state 1260.
[0226] FIG. 40D shows a series of steps, which may be added to the
data acquisition phase and the chemical recognition phase to take
advantage of second order data processing characteristics. For
example, in the data acquisition state, a series of states 1020,
1022, 1024 and 1026 may be added to curve-fit specific attributes
of the measured response. Specifically, a state 1020 may be entered
in which for each data element of the object P a vector, z, is
formed consisting of the peak compensation voltages vc11, vc12, . .
. vc1m.
[0227] This vector is a vector of point locations for the peaks
observed for a range of compensation voltages. Returning attention
to FIG. 14A, briefly, this may correspond, for example, to locating
the points 601-1, . . . 601-m, . . . 601-n corresponding to peak
height and locations for the monomer ions of interest. A curve may
then be fit through these peaks such as by applying a curve fitting
algorithm, in state 1024. In the illustrated example it is assumed
that a quadratic equation is fitting the peaks of the form
y.sup.2=.beta.x.sup.2+.gamma.. The .beta. and .gamma. coefficients
can then be stored in the state 1026 associated with the vector.
The chemical is thus identified by a curve fit to its peak
locations approximating its mobility (.alpha. coefficient)
behavior.
[0228] If this is done, a corresponding set of steps 1270, 1272 and
1274 can be added to the recognition process to identify peaks by
performing a curve fit to observe data, and then, determining
.gamma. and .beta. coefficients, rather than comparing raw data
values in states 1270 and 1272. In state 1274, the .beta. and
.gamma. coefficients are tested to determine closest matches in the
P object library.
[0229] FIG. 40F shows a series of steps that may be used to
identify or distinguish peaks in the acquisition phase. Here
initial data may be added to the objects P by identifying peaks as
a cluster peak or monomer peak. Specifically, if a peak shift over
a range of field condition voltages (e.g., FIG. 14A) increases
(i.e., shifts to the right), then this may be identified as a
cluster peak. If the peak does not meet specific shifting criteria,
it may be identified as a monomer peak. States 1310, 1331, and 1332
may thus be added to the identification process. The results of
these steps adds an additional parameter L associated with each
data point in the object P to further identify each peak as a
monomer cluster or other peak type, as shown in FIG. 40E.
[0230] Other approaches to this may be used to label peaks. For
example, reactant ion peaks (RIP) may also be identified by
performing an analysis on a response of the instrument, with no
sample S applied. In this mode, only the RIPs occur, and in their
behavior across a range of compensation voltages can be stored.
Information concerning the particular type of peak may be stored in
pointer data in a state 1320, at which such a peak is detected.
This information can then be added to the objects P, specifically
as shown in FIG. 40E.
[0231] FIG. 40G shows additional processing steps, which may be
performed in the compound recognition state to take advantage of
the situation of FIGS. 36A-38 in which monomer and cluster ion
behavior is observed. Specifically, the steps of FIG. 40G may be
added as further steps 1280 in the recognition phase. Here, for
every candidate peak P', a corresponding monomer peak in the
reference array P is compared. A score is then associated with the
closest of the match in state 1284. Similarly, in state 1286, a
cluster peak may be compared with its corresponding in the peak
library P. A score sc is then determined in step 1288, depending on
the closest of this match. In a state 1290, a final score sf can be
associated with weighting the monomer peak score and the cluster
peak score by weighting factors wm and wc. For example, in an
instance where cluster peaks are expected to provide more
information than monomer peaks, cluster peaks may be weighted
highly and monomer peaks relatively low or zero factor. Using this
weighting, both monomer and cluster peak identification can be
combined to further refine compound analysis.
[0232] In various applications, the above described approaches to
ion-based sample analysis may be employed in relatively compact,
such as handheld, analyzer systems. FIG. 41 is a conceptual diagram
of such a compact DMS analyzer system 1400. The DMS system may be
used, for example, to analyze compounds, such as chemical warfare
agents (CWAs), and Toxic Industrial Compounds (TICs), and Toxic
Industrial Materials (TIMs) according to an illustrative embodiment
of the invention. By operating the compact DMS analyzer system 1400
at less than atmospheric pressure, e.g., 0.5 atm, as described
above, the system 1400 approximately doubles its resolution over
existing state-of-the-art systems, while reducing its power
consumption and size. By performing sample fragmentation, as
described above, sample analysis may be further enhanced. By
utilizing three-dimensional color dispersion plots, as also
described above, analysis of CWAs, TICs, and TIMs is further
enhanced.
[0233] The DMS analyzer system 1400 may employ an electromechanical
pump, compressed gas or air, or the solid-state flow generator
1402, which includes an ion source 1404, an ion attractor 1406, and
a constrained flow channel 1408 for controlling sample flow and/or
pressure within the system 1400. The ion source 1404 provides a
source of ions and the ion attractor 1406 attracts either positive
or negative ions, depending on an applied bias voltage. The ion
flow created in the constrained channel 1408 due to the ion flow
generated by the interaction of the ion source 1404 with the ion
attractor 1406 creates a fluid, e.g., a sample effluent, flow. In
some illustrative embodiments, the DMS analyzer system 1400 may be
miniaturized, such that the analyzer unit 1410 is included in
application-specific integrated circuits (ASICs) embedded on a
substrate 1412. A solid state flow generator of the type employed
by the invention is described in further detail in co-pending and
co-owned U.S. patent application Ser. No. 10/943,523, filed on 17
Sep. 2004, the entire contents of which are incorporated above by
reference.
[0234] The constrained channel 1408 includes an inlet end 1414 and
an outlet end 1416. The constrained channel 1408 also includes a
sample introduction inlet 1418 to enable the analyzer 1410 to
collect the sample gas for analysis. A pre-concentrator 1420 may be
employed at the sample introduction inlet 1418 to concentrate the
sample .and improve analysis accuracy. An ionizer 1422 provides
ionization of the sample using, for example, a radioactive
Ni.sup.63 foil, or non-radioactive plasma ionizer, or other
suitable ionization source within ionization region 1424. A plasma
ionizer has the advantage of enabling precise control of the energy
imparted to the sample gas for ionization. Ideally, the ionizer
1422 imparts only enough energy to ionize the sample gas, without
producing nitric oxides (NOx's) and ozone. A fragmentation region
may also be included in the system 1400. NOx's and ozone are
undesirable because they can form ion species that interfere with
the ionization of CWA agents. Because diffusion and mobility
constants generally depend on pressure and temperature, the DMS
analyzer system 1400 may include a temperature sensor 1426 and/or a
pressure sensor 1428 for regulating the temperature and/or pressure
of the sample gas within the analyzer unit 1410 for more accurate
analysis. The analyzer 1410 may also include a humidity sensor. The
analyzer 1410 also includes an analytical region 1440 with filter
plates 1442 and detector plates 1444. A molecular sieve 1446 may be
employed to trap spent analytes.
[0235] The controller 1446 provides control of filtering and
detection while also providing an output of the detection results.
The power supply 1448 provides power to the filter plates 1442,
solid-state flow generator 1402, and any other component requiring
electrical power. The controller electronics 1446 for Vcomp, Vrf,
the ion heater pumping, the DMS ion motion, and the
pre-concentrator 1420 heater may be located with the analyzer unit
1410. Also, the detector 1444 electronics, pressure 1426 and
temperature 1428 sensors, and the processing algorithm for a
digital processor may reside within analyzer 1410.
[0236] At atmospheric pressure, to realize the benefits of mobility
nonlinearity, the DMS analyzer system 1400 illustratively employs
RF electric fields of about 10.sup.6 V/m, and a Vrf of about 200
Vpeak at about a 200.times.10.sup.-6 .mu.m gap. However, any
suitable RF electric field parameters may be employed. The power
supply 1448 may be remotely located relative to the analyzer unit
1410 to generate RF voltage for filter the plates 1442. At less
than atmospheric pressure, the RF electric field may be reduced as
described above to further reduce the power consumption and size of
the DMS analyzer system 1400.
[0237] The DMS analyzer system 1400 may also interface with a
personal computer (PC) or controller 1446 to utilized
signal-processing algorithms that convert analyzer 1410 outputs
into detection, identification, and/or measurement of analytes and
concentration levels. The controller 1446 or an interfacing PC may
also facilitate control and power management for the DMS analyzer
system 1400. The supporting electronics for the DMS analyzer system
1400 may be implemented, for example, on an ASIC, a discrete
printed circuit board (PCB), or System on a Chip (SOC).
[0238] In operation, the solid-state flow generator or
electromechanical transport pump 1402 draws samples into the DMS
analyzer system 1400 at the inlet 1414 and past a CWA-selective
chemical membrane concentrator 1420 having an integrated heater.
The CWA-selective chemical membrane pre-concentrator 1420 may also
serve as a hydrophobic barrier between the analytical region 1440
of the analyzer system 1400 and the sample introduction region
1450. The membrane of the pre-concentrator 1420, illustratively,
allows CWA agents to pass, but reduces the transmission of other
interferants and act as a barrier for moisture.
[0239] The pre-concentrator 1420 may use selective membrane
polymers to suppress or block common interferences (e.g., burning
cardboard) while allowing CWA agents or CWA simulants to pass
through its membrane. Although many selective membrane materials
are available, poly-dimethyl siloxane (PDMS) may be a preferred
membrane/concentrator/filter to reject water vapor and collect CWA
analytes. At high concentration levels, water vapor molecules may
cluster to the analytes, altering the analytes' mobilities.
Membrane materials such as hydrophobic PDMS tend to reduce the
vapor to acceptable levels while absorbing and releasing analyte
atoms. The thin membrane of the pre-concentrator 1420 may also be
heated periodically to deliver concentrated analytes to the
ionization region 1424 and analytical region 1440.
[0240] Except for diffusion of analytes through the
membrane/filter/pre-concentrator 1420, the analytical region 1440
is generally sealed to the outside atmosphere. Thus, the analyzer
system 1400 may employ elements for equalizing the pressure inside
analytical region 1440 with the atmospheric pressure outside the
analyzer system 1400 or maintain pressure in the analytical region
1440 at less than atmospheric pressure for improved ion intensity
peak resolution. Once the sample gas molecules are ionized, the
ions are driven longitudinally in the direction indicated by the
arrow 1452 through the ion filter plates 1442 by static or
traveling electrostatic fields, as opposed to being driven by the
carrier gas. The filter plates 1442 apply transverse radio
frequency (RF) field voltages and dc excitation electric
compensation fields to the ions moving through analytical region
1440 to separate the species within a sample.
[0241] With water vapor removed, interferants (e.g., hydrocarbons
and others) typically comprise roughly 0.10% of the incoming air
volume by weight. Depending on the collection efficiency of the
pre-concentrator 1420, the molecular sieve 1446 may be sized to
support about 6, 9, 12 or more months of substantially continuous
or continuous operation before saturating. The molecular sieve 1446
may also be configured to allow movement of air in a circulatory
fashion through the ion filter electrodes 1442 and back to the
ionization region 1424.
[0242] The DMS analyzer system 1400 may be used for detecting low
concentrations (e.g., parts per trillion (ppt)) of CWAs, such as,
without limitation, nerve and blister agents. In one illustrative
embodiment, the DMS analyzer system 1400 includes a
high-sensitivity, low-power, sample gas analyzer 1404 that builds
on MEMS technology, but further miniaturizes the DMS analyzer
system 1400 to achieve parts-per-trillion sensitivity, about 0.25 W
overall power consumption (i.e., 1 Joule measurement every 4
seconds), and a size of about 2-cm.sup.3 or less.
[0243] Because of the smaller analytical region 1440 and the
resulting lower flow rate requirements, a low-power (e.g., mW)
solid-state gas transport pump 1402, using ionic displacement, may
be employed to draw an air sample into the DMS analyzer system 1400
and onto the CWA-selective chemical membrane pre-concentrator 1420.
Compact DMS analyzer systems according to the invention have shown
very high sensitivities to CWA simulants. By way of example, a
compact DMS analyzer system according to the invention has been
shown to detect methyl salycilate at parts-per-trillion (ppt)
levels. The DMS analyzer system 1400 has the ability to resolve CWA
simulants from interferants that cannot be resolved by current
field-deployed detection technologies.
[0244] FIG. 42 is a graph depicting a DMS spectra showing
resolution of dimethylmethylphosphonate (DMMP) from aqueous
firefighting foam (AFFF) as measured in a DMS analyzer system 1400.
AFFF is one interferant that has proved extremely challenging for
conventional IMS systems to resolve CWAs or other simulants. The
AFFF ion intensity peak tends to overlap with the agent peak during
sample detection in DMS or IMS systems.
[0245] FIG. 42 is a graph of multiple plots showing experimental
results for a series of CWA simulants selectively mixed with 1%
headspace of AFFF. The top plot 1460 of FIG. 42 shows RIP for a DMS
analyzer system 1400 with background air but no sample present with
the sensor at atmospheric pressure. In the next plot 1462, the AFFF
interferant is added. This results only in a slight shift to the
left (more negative compensation voltage) of the RIP ion intensity
peak. Then, in plot 1464, the CWA stimulant DMMP is introduced into
the spectrometer and the typical monomer and dimmer peaks appear
together with a corresponding reduction in the RIP peak ion
intensity. When 1% AFFF is added according to plot 1468, the DMMP
peaks are not effected and only a slight leftward shift of the RIP
is observed. The same experiment was repeated with DIMP in plots
1468 and 1470, and the effect of AFFF was negligible. In plot 1472,
MS is introduced, and according to monitored negative ion peaks,
gives similar data illustrating the lack of interference with AFFF.
The conclusion is that 1% AFFF has virtually no effect. Thus, FIG.
42 illustrates the ability of the DMS analysis system 1400 to
resolve CWA simulants from interferants.
[0246] In one illustrative embodiment, the compact hand-held DMS
analyzer system 1400 is achieved by combining the following design
characteristics: (a) using the analyzer/filter/detector 1410 with
improved sensitivity and size reduction; (b) using the solid-state
flow generator or electromechanical pump as a gas transport pump
1402 to sample and move analytes; (c) using the CWA-selective
chemical membrane pre-concentrator 1420 with integrated heater (in
some configurations provided by using a solid-state generator or
electromechanical pump to transfer heat from other analyzer system
components to the pre-concentrator 1420) to remove water vapor and
to concentrate; and/or (d) using electric field propulsion of the
ions 1454 through the analytical region 1440 of analyzer 1410.
[0247] According to various illustrative embodiments, the invention
improves the resolution of species identification over conventional
systems, while decreasing size and power to achieve
parts-per-trillion sensitivity, a less than about 0.25 mW overall
power dissipation, and a size of about a 2-cm.sup.3 or less in an
entire system not including a power source or display, but
including an RF field generator. According to some embodiments, an
analyzer system of the invention has a total power dissipation of
less than about 15 W, about 10 W, about 5 W, about 2.5 W, about 1
W, about 500 mW, about 100 mW, about 50 mW, about 10 mW, about 5
mW, about 2.5 mW, about 1 mW, and/or about 0.5 mW. According to
further embodiments, an analyzer system according to the invention,
optionally including a display (e.g., indicator lights and/or an
alphanumeric display) and a power source (e.g., a rechargeable
battery) compartment, along with an RF field generator, may have a
total package outer dimension of less than about 0.016 m.sup.3,
0.0125 m.sup.3, 0.01 m.sup.3, 0.0056 m.sup.3, 0.005 m.sup.3, 0.002
m.sup.3, 0.00175 m.sup.3, 0.0015 m.sup.3, 0.00125 m.sup.3, 0.001
m.sup.3, 750 cm.sup.3, 625 cm.sup.3, 500 cm.sup.3, 250 cm.sup.3,
100 cm.sup.3, 50 cm.sup.3, 25 cm.sup.3, 10 cm.sup.3, 5 cm3, 2.5
cm.sup.3, with the package being made, for example, from a high
impact plastic, a carbon fiber, or a metal. According to further
embodiments, an analyzer system, for example, according to the
invention, including an RF generator, and optionally including a
display, keypad, and power source compartment, may have a total
package weight of about 5 lbs, 3 lbs, 1.75 lbs, 1 lbs, or 0.5
lbs.
[0248] Table 1 provides a comparison of drift tube (e.g., the
constrained channel) dimensions, fundamental carrier gas
velocities, and ion velocities for a various illustrative
embodiments of a DMS analyzer system 1400 depending on the flow
rate (Q) available to the analysis unit. Designs 1-4 provide flow
rates of varying orders of magnitude ranging from about 0.03 l/m to
about 3.0 l/m. Table 1 illustrates that as the flow rate is
decreased through the DMS analyzer system 1400, the filter plate
dimensions and power requirements are reduced. Table 1 is
applicable to a DMS analyzer system 1400 using either a sample gas
or longitudinal field-induced ion motion. The time to remove an
unwanted analyte is preferably less than about the time for the
carrier to flow through the filter region (tratio). Also, for a
particular target agent, the lateral diffusion as the ion flows
through the analyzer 1410 is preferably less than about half the
plate spacing (difratio). Based on this criteria, the plate
dimensions may be reduced to about 3.times.1 mm.sup.2 or smaller,
while the ideal flow power may be reduced to less than about 0.1
mW. Thus, even for design 4, the number of analyte ions striking
the detectors is sufficient to satisfy a parts-per-trillion
detection requirement. TABLE-US-00001 TABLE 1 Illustrative DMS
Analyzer System Design Specifications and Characteristics Design 1
Design 2 Design 3 Q = 3 l/m Q = 0.3 l/m Q = 0.3 l/m Design 4
Description Units Symbol Baseline Base dimen scaled Q = 0.03 l/m
plate dimensions *length m L 0.025 0.025 0.005 0.001 *width m b
0.002 0.002 0.001 0.0004 *air gap m h 0.0005 0.0005 0.0005 0.0002
*volume flow rate l/min Qf 3 0.3 0.3 0.03 Flow velocity m/s Vf 50 5
10 6.25 pressure drop Pa dPf 1080 108 43.2 33.75 flow power W Powf
0.054 0.00054 2.16E-04 1.69E.05 RF excitation V Vrf 650 650 650 260
design ratios Time to remove unwanted analyte divided by carrier
time s tratio 0.0128 0.0013 0.0128 0.0160 wanted ions-lateral
diffusion divided by half gap s difratio 0.200 0.632 0.200 0.283
ions to count per cycle -- Nout 1.22E+07 1.22E+06 1.22E+06
1.22E+05
[0249] For sample/carrier gases, there does not appear to be an
electromechanical pump that operates at the preferred flow
characteristics with an efficiency better than about 0.5%. With a
0.5% efficiency, an ideal flow loss of about 0.05 mW results in an
actual power consumption of about 10 mW, about a factor of 100
greater than in the above discussed illustrative embodiment of the
invention.
[0250] The DMS system 1400 may simultaneously detect both positive
and negative ion intensity peaks which further improves detection
selectivity. The combination of the positive and negative ion
channel information, the shift in spectral peak as a function of
applied field strength or voltage, and the display is this
information in a three-dimensional manner provide a novel mechanism
for chemical identification.
[0251] FIG. 43 is a three-dimensional dispersion plot 1750 of the
detection of positive ions of agent GA over a range of field
voltages and field compensation voltages with varying intensity
represented in varying color according previously described
illustrative embodiments of the invention. The plot 1750
illustrates the enhanced identification (selectivity) of a compound
using a three-dimensional dispersion plot by, for example, a DMS
system 1400. In comparison, FIG. 25 is a three-dimensional
dispersion plot of negative ions of GA over a range of RF voltage
versus compensation voltage with varying intensity represented in
varying color that illustrates the enhanced identification
(selectivity) of a compound using a three-dimensional dispersion
plot by, for example, DMS system 1400. Both measurements were
performed with a concentration of GA at 0.14 ng/l, a Ni.sup.63
source, 50% RH, 3 scan averaging, and 350 cc/min carrier gas flow.
The differences between the three-dimensional plots 814 of FIGS. 25
and 1750 of FIG. 50 illustrate that performing both positive and
negative ion mode detection provides enhanced signature
identification of ion species.
[0252] In certain illustrative embodiments, the compact DMS system
1400 of FIG. 41 and various other figures may employ features
and/or be incorporated into systems described in further detail in
U.S. Pat. Nos. 6,495,823 and 6,512,224, the entire contents of both
of which are incorporated herein by reference.
[0253] FIGS. 44-53 are conceptual block diagrams of chemical and/or
biological agent detection systems using various configurations of
a mobility detection analyzer system such as those depicted and
described herein, a recirculation system, and other components
according to illustrative embodiments of the invention. More
particularly, FIG. 44 is a conceptual block diagram of a CWA and/or
biological agent detection system 1476 according to an illustrative
embodiment of the invention. The system 1476 employs a mobility
detection system 1478, molecular sieve 1480, pump 1482 with
optional vent 1484, optional second molecular sieve 1486,
circulating channel 1488, sample inlet 1490, exhaust 1492, membrane
1494, and orifice 1496. The system 1476 may also employ filtered
air or gas 1498 to circulate or transport a sample through the
system 1476. The mobility analyzer system 1478 may be a compact DMS
analyzer system 1400 of FIG. 48, DMS system 10 of FIG. 5, an IMS, a
TOF-IMS, a GC-IMS, an MS or the like. The system 1476, like all of
the previously described illustrative systems, may employ one or
more dopants to enhance analysis.
[0254] In operation, the system 1476 receives a sample S at inlet
1490 and passes it through the membrane 1494 into the circulation
channel 1498. The membrane 1494 may filter out unwanted
interferants, if desired, in the same or similar manner as the
pre-concentrator 1420 of FIG. 48. The orifice 1496 may, in a fixed,
controlled, or adjustable manner, regulate the gas and/or sample
flow into the analyzer system 1478 and thereby regulate or control
the pressure within the analyzer system 1478. Thus, the analyzer
system 1478 may operate at atmospheric pressure, below atmospheric
pressure, or above atmospheric pressure. The pump 1482 maintains
gas flow in the analyzer system 1478 and pressure control either
independently or in coordination with the orifice 1496. Thus, in
one example, the pump 1482 draws sample flow through the orifice
1496 into the analyzer system 1478 to enable detection and
identification of select ion species. The analyzer system 1478 may
be a DMS system 1400 that tunably detects certain ion species by
adjusting its field/flow channel conditions, such as, its Vrf and
Vcomp, parameters and in some configurations, controlling the pump
1484 and/or the orifice 1496 to control pressure within the system
1400.
[0255] Once detection and identification are performed, the
molecular sieve 1480 may trap spent analytes from the analyzer
system 1478. Again, the pump 1484, whether electromechanical or
solid-state, propels the gas, optionally through a second molecular
sieve 1486, through the circulating channel 1488. The sample gas is
then expelled through the membrane 1494 and the outlet 1492 or
mixed and re-circulated with more sample S back into the orifice
1496.
[0256] FIG. 45 is a conceptual block diagram of a CWA and/or
biological agent detection system 1500, configured for reduced
pressure analysis, according to an illustrative embodiment of the
invention. The system 1500 is similar to the system 1476 except
that an additional sample flow channel 1502 is employed instead of
a membrane. The system 1500 includes sample S inlet 1504, orifice
1506, ionization region 1508, deflector plate 1510, attractor plate
1512, channel 1502 pump 1514, second channel 1516, analyzer system
1518, molecular sieve 1520, pump 1522, and optional second
molecular sieve 1524.
[0257] In operation, the system 1500 draws sample S through the
sample inlet 1504 and through the orifice 1506. The orifice 1506
may be controlled, fixed, or adjustable to regulate sample gas flow
and/or pressure in the channel 1502. The pump 1514 may also be used
in coordination with the orifice 1506 to regulate gas flow and/or
pressure within the channel 1502. The deflector plate 1510 may
force, push, or selectively separate ions into the channel 1516
through the opening 1526 while the attractor 1512 may attract ions
from the channel 1502 into the channel 1516. A pressure drop across
the opening 1526 may be adjusted so that only sample ions enter the
channel 1516 while sample neutrals are prevented from entering. The
sample ions may be directly introduced into the analyzer system
1518 or the ions may be neutralized and then re-ionized in the
analyzer system 1518. The analyzer system 1518 may be a DMS system,
IMS system, or the like. The analyzer system 1518 may include
multiple DMS, IMS, or other like systems or a combination of such
systems to perform sample detection and identification. For
example, system 748 of FIG. 21 or system 754 of FIG. 22 may be
employed to apply conventional DMS detection in combination with
fragmentation to enhance sample analysis.
[0258] The channel 1516 pump 1524 may then draw the sample S from
the analyzer system 1518 through the molecular sieve 1520 and then
propel the sample S, optionally through the second molecular sieve
1524. The molecular sieves 1520 and 1524 will capture most of the
spent sample S analytes. Any remaining sample S is mixed with new
sample S gas and returned to the analyzer system 1518 via the
channel 1516. The outlet 1528 expels sample S gas from the channel
1502.
[0259] FIG. 46 is a conceptual block diagram of a cylindrical or
coaxial CWA and/or biological agent detection system 1530 according
to an illustrative embodiment of the invention. The system 1530
includes a sample S inlet 1532, constrictor 1534, inner channel
1536, opening 1538, clean transport gas inlet 1540, outer channel
1542, analyzer system 1544, channel 1542 outlet 1546, and channel
1536 outlet 1548.
[0260] In operation, the system 1530 draws the sample S into the
channel 1536 through the constrictor or orifice 1534. The
constrictor 1534 may be adjustable, controllable or fixed to enable
a pressure reduction below 1 atm, for example to 0.5, 0.65, or 0.85
atm, in the channel 1536. The clean transport gas inlet 1540
receives clean transport gas into the channel 1542. The channel
1542 may operate at pressures below 1 atm. The sample S maybe drawn
or attracted into the channel 1542 through the opening 1538 by a
pressure differential with the channel 1536, an ion attractor in
channel 1542, gas flow into channel 1542, or other like technique.
The analyzer system 1544 then detects and identifies the ion
species of the sample S and expels the sample S through the outlet
1546. The sample neutrals in the channel 1536 may be expelled
through the outlet 1548.
[0261] FIG. 47 is a DMS system 1550 including an orifice 1552 at
the system 1550 inlet to control pressure within the system 1550 in
coordination with a pump 1554. The system also includes the
molecular sieve 1556, ion source 1558, filter 1560, and detector
1562. In operation, the pump 1554 has sufficient power to draw a
sample S through the orifice 1552 to then enable detection of the
sample at a reduced pressure.
[0262] FIG. 48 is a DMS system 1564 including an orifice 1566,
ionization source 1568, filter 1570, detector 1572, molecular sieve
1574, pump 1576, a second molecular sieve 1578, a membrane 1580, an
inlet 1582, and outlets 1584 and 1586. Because the membrane 1580 is
positioned upstream of the orifice 1566 and the sample flow is in
direction 1588, the membrane 1580 operates at atmospheric pressure
while the ionization source 1568, filter 1570, and detector 1572
operate below atmospheric pressure due to a pressure drop across
the orifice 1566. It may be advantageous to operate the membrane
1580 at atmospheric pressure to prolong its useful life.
[0263] FIG. 49 is a DMS system 1590 including an orifice 1592,
ionization source 1594, filter 1596, detector 1598, molecular sieve
1600, pump 1602, a second molecular sieve 1604, a membrane 1606, an
inlet 1608, and outlets 1610 and 1612. Because the membrane 1606 is
positioned downstream of the orifice 1592 and the sample flow is in
the direction 1614, the membrane 1606 operates below atmospheric
pressure along with the ionization source 1594, filter 1596, and
detector 1598 due to a pressure drop across the orifice 1592. It
may be advantageous to operate the membrane 1606 below atmospheric
pressure.
[0264] FIG. 50 is a DMS system 1616 including an orifice 1618,
ionization source 1620, filter 1622, detector 1624, molecular sieve
1626, pump 1628, a second molecular sieve 1630, a membrane 1632, an
inlet 1634, and outlets 1636 and 1638. Because the membrane 1632
and the ionization source 1620 are positioned upstream of the
orifice 1618 and the sample flow is in direction 1640, the membrane
1632 and the ionization source 1620 operate at atmospheric pressure
while the filter 1622 and detector 1624 operate below atmospheric
pressure due to a pressure drop across the orifice 1618. It may be
advantageous to operate the membrane 1632 and ionization source
1620 at atmospheric pressure.
[0265] FIG. 51 is a DMS system 1642 including a first channel 1644
and a second channel 1646 operating at atmospheric pressure. The
first channel 1644 includes an ionization source 1648, deflector
electrode 1650, pump 1652, inlet 1666, and outlet 1668. The second
channel 1646 includes a filter 1654, detector 1656, molecular sieve
1658, pump 1660, and molecular sieve 1662. An opening 1664 provides
fluid communication between the channels 1644 and 1646.
[0266] In operation, the system 1642 receives a sample S at the
inlet 1666 into the channel 1644. The ionization source 1648
ionizes the sample S. The ionized portions of the sample S, e.g.,
the positive ions, are deflected through the opening 1664 into the
channel 1646 by the deflector 1650 having a positive charge. When
the deflector 1650 is negatively charged, the deflector 1650 may
deflect negative ions of sample S through the opening 1664 into the
channel 1646. The neutrals and non-deflected ions of sample S are
then drawn by the pump 1652 to the outlet 1668 and expelled from
the system 1642 while the ions in the channel 1646 are filtered by
the filter 1654 and detected by the detector 1656. The pump 1660
creates circulation flow in the direction 1670 within the channel
1646 to draw the sample S through the molecular sieve 1658 which
collects spent analytes and then through a second molecular sieve
1662.
[0267] FIG. 52 is a DMS system 1672 including a first channel 1674
and a second channel 1676 operating below atmospheric pressure
without a membrane. The first channel 1674 includes an ionization
source 1678, deflector electrode 1680, pump 1682, inlet 1684,
outlet 1686, and orifice 1700. The second channel 1676 includes a
filter 1688, detector 1690, molecular sieve 1692, pump 1694,
molecular sieve 1696, and orifice 1702. An opening 1698 provides
fluid communication between the channels 1674 and 1676.
[0268] In operation, the system 1672 receives a sample S at the
inlet 1684 into the channel 1674 and through the orifice 1700. The
orifice 1700 provides a pressure drop within the channel 1674
caused by the gas and/or air flow generated by the pump 1682. The
ionization source 1678 ionizes the sample S. The ionized portions
of the sample S, e.g., the positive ions, are deflected through the
opening 1698 into the channel 1676 by the deflector 1680 having a
positive charge. When the deflector 1680 is negatively charged, the
deflector 1680 may deflect negative ions of sample S through the
opening 1698 into the channel 1676. The neutrals and non-deflected
ions of sample S are then drawn by the pump 1682 to the outlet 1686
and expelled from the system 1672 while the ions in the channel
1676 are filtered by the filter 1688 and detected by the detector
1690. The pump 1694 creates circulation flow in the direction 1704
within the channel 1676 to draw the sample S through the molecular
sieve 1692 which collects spent analytes and then through a second
molecular sieve 1696.
[0269] FIG. 53 is a DMS system 1706 including a first channel 1708,
a second channel 1710, and a third channel 1712 with the second
channel 1710 and third channel 1712 capable of operating at or
below atmospheric pressure using a membrane 1714. The first channel
1708 includes an inlet 1716 and an outlet 1718. The second channel
1710 includes an ionization source 1718, optional ionization source
1720, deflector electrode 1722, filter 1724, and detector 1726. The
third channel 1712 includes an attractor electrode 1728, filter
1730, and detector 1732. The combined circulation channel 1734
includes the chemical filter 1736, pump 1738, and optional chemical
filter 1740. An opening 1742 provides fluid communication between
the channels 1710 and 1712.
[0270] In operation, the system 1706 receives a sample S at the
inlet 1716 into the channel 1708. The sample S may be introduced
from a GS column. The membrane 1714 may filter a portion of the
sample S and provide a pressure barrier to enable a pressure below
atmospheric pressure in the channels 1710 and 1712. The channels
1710 and 1712, along with the combined circulation channel 1734,
circulate filtered and clean carrier gas. The ionization source
1718 ionizes the sample S within this clean carrier gas.
Optionally, a second ionization source 1720 may be employed in the
channel 1710 to enhance the ability of the deflector 1722 and
attractor 1728 to transfer select ions from the channel 1710 to the
channel 1712. For example, the ionized portions of the sample S,
e.g., the positive ions, are deflected through the opening 1742
into the channel 1712 by the deflector 1722 when the deflector 1722
is positively charged. When the deflector 1722 is negatively
charged, the deflector 1722 may deflect negative ions of sample S
through the opening 1728 into the channel 1712.
[0271] The neutrals and non-deflected ions of sample S are then
drawn by the pump 1738 through the channel 1710, filter 1724 and
detector 1726 while the selected ions are drawn through the channel
1712, filter 1730, and detector 1732. The pump 1738 creates
circulation flow in the direction 1744 within the channels 1710,
1712, and 1734 to draw the carrier gas from the channels 1710 and
1712 into the channel 1734 and through the chemical filter 1736
and, optionally, the second chemical filter 1740. The chemical
filters 1736 and 1740 remove unwanted contaminants from the carrier
gas. A make up gas may also optionally be introduced into the
channel 1734 from an outside system.
[0272] The deflector 1722 and the attractor 1728 may be activated
in a controlled manner to transport ions from the channel 1710 to
the channel 1712. In the channel 1710, the non-deflected ions are
filtered by filter 1724 and detected by detector 1726 while, in the
channel 1712, the deflected and attracted ions are filtered by the
filter 1730 and detector 1732. The resulting detected measurements
from the channels 1710 and 1712 can then be compared, added, or
subtracted from each other to enhance the identification of ion
species. The controlled ionization of the sample S which is
performed in a clean carrier gas, the detection in the channel 1712
of monomer or de-clustered ions, and the detection of clustered
ions in the channel 1710 provide enhanced compound and ion species
identification.
[0273] Although the invention has been described with regard to
particular illustrative embodiments, it should be appreciated that
the invention is broader in scope and can be applied to any system
for identification of unknown species of ions traveling through a
varying controlled excitation field, the identification being based
on the known characteristic travel behavior of the species under
the varying field conditions. The ion or ions to be identified may
be traveling alone or in a group of ions of same or differing
characteristic travel behavior. The filter field may be compensated
in any of various manners as long as a species of interest is
returned to the center of the flow and permitted to pass through
the filter while all other species are retarded or neutralized.
Identification is made based on known field-dependent differential
mobility behavior of at least one species of ions traveling in the
field at known field conditions.
[0274] It should also be appreciated that in various practices, the
invention provides improved systems, methods and devices for ion
species identification. According to some features, the invention
varies one or more filter field/flow channel conditions to improve
species discrimination. For example, according to some illustrative
embodiments, the invention determines changes in ion mobility,
based, for example, on changes in: Vrf; Vcomp; field strength; Vrf
duty cycle; Vrf wavelength; Vrf frequency; and/or flow channel
temperature, pressure, humidity, flow rate, doping and/or carrier
gas CG composition. According to other features, the invention
takes multiple scans of the sample S, for example, by recirculating
the sample S and/or processing the sample S in parallel and/or in
series with one or more additional DMS, IMS, TOFIMS, GC, FTIR, MS,
or LCMS, at differing flow channel/filter field conditions.
[0275] According to further features, the invention employs
approaches, such as, fragmenting, lowering pressure, and
three-dimensional dispersion plotting to enhance detection
resolution. According to other features, the invention stores a
library of signatures for known compounds and pattern matches data
from unknown compounds with the stored library to identify the
unknown compounds. It should be understood that the invention is
applicable not only to planar DMS systems, but may be applied in
general to ion mobility spectrometry devices of various types,
including various geometries, ionization arrangements, detector
arrangements, and the like, and brings new uses and improved
results even as to structures that are all well known in the
art.
[0276] Thus, the invention is not limited to configurations of the
illustrative embodiments and may be practiced in any other suitable
configurations, including radial and cylindrical DMS devices.
Additionally, various modifications and variations may be made to
the invention without departing from the spirit and scope
herein.
* * * * *