U.S. patent application number 16/547498 was filed with the patent office on 2020-02-27 for pattern recognition algorithm for identifying and quantifying single and mixed contaminants in air with an array of nanomaterial.
The applicant listed for this patent is AerNos, Inc.. Invention is credited to Sundip R. Doshi, Alexey Varganov.
Application Number | 20200064291 16/547498 |
Document ID | / |
Family ID | 69586085 |
Filed Date | 2020-02-27 |
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United States Patent
Application |
20200064291 |
Kind Code |
A1 |
Varganov; Alexey ; et
al. |
February 27, 2020 |
PATTERN RECOGNITION ALGORITHM FOR IDENTIFYING AND QUANTIFYING
SINGLE AND MIXED CONTAMINANTS IN AIR WITH AN ARRAY OF
NANOMATERIAL-BASED GAS SENSORS
Abstract
A method is described for identifying and quantifying single and
mixed contaminants in air by reading nanohybrid gas sensors
multivariate output and processing it inside the algorithm. The
algorithm analyzes sensor signal in real time and outputs estimated
values for concentrations of target gases.
Inventors: |
Varganov; Alexey; (San
Diego, CA) ; Doshi; Sundip R.; (San Diego,
CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
AerNos, Inc. |
San Diego |
CA |
US |
|
|
Family ID: |
69586085 |
Appl. No.: |
16/547498 |
Filed: |
August 21, 2019 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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62721289 |
Aug 22, 2018 |
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62721293 |
Aug 22, 2018 |
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62721296 |
Aug 22, 2018 |
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62721302 |
Aug 22, 2018 |
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62721306 |
Aug 22, 2018 |
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62721309 |
Aug 22, 2018 |
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62721311 |
Aug 22, 2018 |
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62799466 |
Jan 31, 2019 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01N 27/226 20130101;
G01N 33/0022 20130101; G05B 2219/25257 20130101; G01N 27/125
20130101; G01N 27/122 20130101; G01N 33/0011 20130101; B01J 21/185
20130101; G01N 33/0031 20130101; G01N 27/046 20130101; G01N 27/4075
20130101; G01N 27/227 20130101; G01N 27/127 20130101; B01J 23/00
20130101; G01N 33/0042 20130101; G01N 27/228 20130101; G05B 19/042
20130101; B82Y 30/00 20130101; B82Y 40/00 20130101; G01N 27/121
20130101; G01N 27/128 20130101; G01N 33/0047 20130101; G05B
2219/25127 20130101; B01J 31/1691 20130101; G01N 33/0037 20130101;
G01N 33/004 20130101 |
International
Class: |
G01N 27/12 20060101
G01N027/12; G01N 33/00 20060101 G01N033/00 |
Claims
1. A method for the selective detection of a target gas and
measuring the concentration values comprising: taking resistance
values of 8, 16, 32, 64, or 128 channels of nanohybrid gas sensors
sampled every 80, 120, 160, or 200 milliseconds; filtering out the
high frequency noise using the exponential average low pass filter;
computing the rate of sensor response change; and evaluating sensor
response with respect to other sensor channels including the
temperature sensor.
2. The method of claim 1, further comprising predicting settled
sensor resistance values to estimate algorithm input values when
sensor output values are in transition following the change in gas
concentration values.
3. The method of claim 2, further comprising using a gas model that
relates change in resistance of material segments to target gas
concentration via model coefficients, wherein the relation between
sensor response and change in target gas concentration described by
equation:
C.sup.i=.SIGMA..sub.j.alpha..sub.j.sup.i(R.sup.j-R.sup.j.sub.0)/R.sup.j.s-
ub.0+C.sup.i.sub.0; Wherein R.sup.j.sub.0 is defined as the channel
resistance for material j right before the exposure, R.sup.j is
defined as the resistance right after the exposure, and wherein the
sum is taken over all channels of various materials j contributing
to the algorithm input; and C.sup.i.sub.0 is defined as the target
gas i concentration right before the exposure, C.sup.i is defined
as the target gas i concentration right after exposure, wherein for
every target gas i each material j channel contains certain
material-gas coefficient value .alpha..sub.j.sup.i.
4. The method of claim 1, wherein preprocessed signals from
nanohybrid gas sensor channels are grouped into segments each
representing a specific material deposited on sensor channel, and
wherein multiple segments are used in engaging a single target gas
model.
5. The method of claim 4, wherein multiple models are concurrently
executed in the algorithm predicting concentration values for
gases, including at least one of NO2, SO2, CO, CO2, O3, CH2O, CH4,
NH3, N20, organic compounds such as Acetone and Ethanol, and
various Hydrocarbons.
6. The method of claim 1, wherein a response of a sensor is a
result of exposure to multiple gas constituents in the atmosphere
as well as the reaction of the sensor to various environmental
factors such as humidity, temperature, pressure and air flow, and
further comprising resolving the cross-sensitivity complexity via
an over-constrained system of modeling equations.
7. The method of claim 6, wherein the compensation coefficients to
account for environmental factors are a combination of: humidity
compensation coefficient, temperature compensation coefficient, and
pressure and air flow compensation coefficient.
8. A method for tracking null reference baseline using
multiple-channel time series signal from a hybrid nanostructure gas
sensor, comprising: taking resistance values of multiple channels
of nanohybrid gas sensors; comparing them against the reference
resistance values benchmarked in ambient atmosphere with known
concentrations of contributing gases; and adjusting the starting
values for target gas concentrations using the deviations from
benchmarked values for at least some of temperature, humidity and
multiple channels of nanohybrid gas sensors.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims priority to U.S. Provisional Patent
Application No. 62/721,289, filed Aug. 22, 2018, U.S. Provisional
Patent Application No. 62/721,293, filed Aug. 22, 2018, U.S.
Provisional Patent Application No. 62/721,296, filed Aug. 22, 2018,
U.S. Provisional Application No. 62/721,302, filed Aug. 22, 2018,
U.S. Provisional Patent Application No. 62/721,306, filed Aug. 22,
2018, U.S. Provisional Patent Application No. 62/721,309, filed
Aug. 22, 2018, U.S. Provisional Application No. 62/721,311, filed
Aug. 22, 2018, U.S. Provisional Patent Application No. 62/799,466,
filed Jan. 31, 2019, the contents of which are incorporated herein
by reference.
BACKGROUND
1. Technical Field
[0002] The embodiments described herein relate generally to systems
and methods for measuring an analyte gas and mixtures in air, and
more particularly, to systems and methods for simultaneous gas
mixture concentrations measurement with an array of
nanomaterial-based gas sensors.
2. Related Art
[0003] Commercially available gas sensors can be cumbersome to use,
expensive and limited in performance (e.g. accuracy, selectivity,
lowest detection limit, etc.). In addition, other major drawbacks
may include inability to detect different types of gases at the
same time, inability to measure absolute concentration of
individual gases, the requirement for frequent re-calibration, a
size incompatible with integration into small form factor systems
such as wearable devices, the reliance on power-hungry techniques
such as heating or on technologies not well suited to manufacturing
in very high volume.
[0004] The ability to accurately detect multiple gases at the same
time, often at parts-per-billion (PPB) sensitivity is becoming
crucial to a growing number of industries as well as to the
world-wide expansion of air quality monitoring initiatives aiming
to address household and urban air pollution challenges.
SUMMARY
[0005] A nano gas sensor architecture that delivers key fundamental
attributes required for the broad deployment of sensors capable of
low detection limits (PPB) in support of highly granular collection
of gas information in ambient air is described herein.
[0006] According to one aspect, a method for the selective
detection of a target gas and measuring the concentration values
comprising: taking resistance values of 8, 16, 32, 64, or 128
channels of nanohybrid gas sensors sampled every 80, 120, 160, or
200 milliseconds; filtering out the high frequency noise using an
exponential average low pass filter; computing the rate of sensor
response change; and evaluating sensor response with respect to
other sensor channels including the temperature sensor.
[0007] According to another aspect, a method for tracking null
reference baseline using multiple-channel time series signal from a
hybrid nanostructure gas sensor, comprising: taking resistance
values of multiple channels of nanohybrid gas sensors; comparing
them against the reference resistance values benchmarked in ambient
atmosphere with known concentrations of contributing gases; and
adjusting the starting values for target gas concentrations using
the deviations from benchmarked values for at least some of
temperature, humidity and multiple channels of nanohybrid gas
sensors.
BRIEF DESCRIPTION OF DRAWINGS
[0008] These and other features, aspects, and embodiments are
described below in the section entitled "Detailed Description."
[0009] FIG. 1 illustrates the basic principles to construct a gas
sensor;
[0010] FIG. 2 is a prospective view of a physical implementation of
a hybrid nanostructure gas sensing element in accordance with one
embodiment;
[0011] FIG. 3 is a diagram illustrating an embodiment of a gas
sensor array that can be included in the hybrid nanostructure gas
sensing element of FIG. 2;
[0012] FIG. 4 is a block diagram of the hybrid nanostructure gas
sensor system that incorporates the hybrid nanostructure gas
sensing element of FIG. 2 in accordance with one embodiment;
[0013] FIG. 5 is a chart showing the flow of gas information
through the hybrid nanostructure gas sensor system of FIG. 4;
[0014] FIG. 6 is an exploded view of an example wearable product
built around a PCB embodiment of the hybrid nanostructure gas
sensor system of FIG. 4;
[0015] FIG. 7 is a block diagram illustrating an example wired or
wireless system that can be used in connection with various
embodiments described herein;
[0016] FIG. 8 is a graph illustrating the filtering out of high
frequency noise using an exponential average low pass filter in
accordance with one embodiment; and
[0017] FIG. 9 is a diagram illustrating an example process for
predicting settled resistance value for transient material response
to changing gas concentration in accordance with one
embodiment.
DETAILED DESCRIPTION
[0018] Embodiments for a hybrid nanostructure gas sensing system
are described herein. The disclosure and the various features and
advantageous details thereof are explained more fully with
reference to the non-limiting embodiments and examples that are
described and/or illustrated in the accompanying drawings and
detailed in the following. It should be noted that the features
illustrated in the drawings are not necessarily drawn to scale, and
features of one embodiment may be employed with other embodiments
as the skilled artisan would recognize, even if not explicitly
stated herein. Descriptions of well-known components and processing
techniques may be omitted so as to not unnecessarily obscure the
embodiments of the disclosure. The examples used herein are
intended merely to facilitate an understanding of ways in which the
disclosure may be practiced and to further enable those of skill in
the art to practice the embodiments of the disclosure. Accordingly,
the examples and embodiments herein should not be construed as
limiting the scope of the disclosure. Moreover, it is noted that
like reference numerals represent similar parts throughout the
several views of the drawings.
[0019] The architecture embodied in the hybrid nanostructure gas
sensing system described hrein achieves the basic requirement of
selectively identifying the presence of a gas analyte in diverse
mixtures of ambient air but it is also designed to identify
multiple gases at the same time, to be compatible in terms of size
and power with very small form factors (including for mobile and
wearable applications), to be easy to Integrate in IoT applications
and to be self-calibrating, thus unshakling the application and/or
the service provider from the burden and expense of regular
re-calibration.
[0020] FIG. 1 describes the basic ingredients for a successful gas
sensor 100. As can be seen, such a sensor includes a sensing
element 102 that is created by depositing a sensitive layer 104
over a substrate 106. The sensing element 102 can then interact
with gaseous chemical compounds 108 altering one or more electrical
properties of the sensing element 102. The change in electrical
properties can be detected by feeding the sensor raw signals 110
through specially designed signal processing electronics 112. The
resulting response signals 114 can be measured and quantified
directly or through the application of pattern recognition
techniques.
[0021] The embodiments described herein comprise six basic
elements. The first is the basic sensor element or sensing channel,
which combines a structural component, built on a substrate
suitable for reliable high-volume manufacturing, with a deposited
electrolyte containing hybrid nano structures in suspension. The
formulation of the electrolyte is specific to a particular gas or
family of gases. A silicon substrate 106 and the structural
component can be built using a MEMS manufacturing process. The
structural component is essentially an unfinished electrical
circuit between two electrodes. The deposition of the electrolyte
completes the electrical circuit and, when biased and exposed to
gas analytes, changes to one or more of the electrical
characteristics of the circuit are used to detect and measure
gases.
[0022] The second element is the arrangement of multiple sensing
channels into an array structure specifically designed and
optimized to interface with data acquisition electronics 112. The
array structure, combined with the use of pattern recognition
algorithms, makes it possible to detect multiple gases at the same
time with a single sensor by customizing one or more of the
individual sensing channels in the array for a specific gas or
family of gases while using other sensing channels to facilitate
such critical functions as selectivity.
[0023] FIG. 2 is a conceptual view of a hybrid nanostructure
physical sensing element 102 in accordance with one example
embodiment. Different materials can be used for the substrate 106
on which the rest of the sensing element 102 is constructed. But
from the perspective of very high volume manufacturing, silicon
technology can be preferred and specifically MEMS technology, which
provides the necessary foundation for a customer-defined set of
manufacturing steps with the flexibility to modulate the complexity
of the process based on the sophistication of the sensor chip being
built, e.g., to support further innovation or to address special
product needs. Silicon technology also provides access to
time-proven test methods and multiple sources of Automated Test
Equipment that can be customized to fit the needs of gas sensing
technology.
[0024] The sensing element 102 is made of an incomplete or "open"
electrical circuit between two electrodes 202, which is then
completed or "closed" by depositing, a molecular formulation
electrolyte 204 with hybrid nanostructures 208 in suspension. The
process is compatible with several commonly used deposition
techniques but does require specially customized equipment and
proprietary techniques to achieve the desired quality and
reproducibility in a high-volume manufacturing environment. In
certain embodiments, the sensing element 102 can be specially
patterned to support efficient deposition of nanomaterial in
pico-litter amounts and to facilitate incorporation of multiple
elements into an array to enables the design of multi-gas
sensors.
[0025] Electrodes 202 can then be bonded to bonding pads 206 in
order to communicate signals 110 to the rest of the system.
[0026] One or more molecular formulations may be necessary to
completely and selectively identify a particular gas. Combining
multiple sensing elements 102, each capable of being "programmed"
with a unique formulation, into a sensor array provides the
flexibility necessary to detect and measure multiple gases at the
same time. It also enables rich functional options such as for
instance measuring humidity, an important factor to be accounted
for in any gas sensor design, directly on the sensor chip (after
all water vapor is just another gas). Another example is the
combination for the same gas or family of gases of a formulation
capable of very fast reaction to the presence of the gas while
another formulation, slower acting, may be used for accurate
concentration measurement; this would be important in applications
where a very fast warning to the presence of a dangerous substance
is required but actual accurate concentration measurement may not
be needed at the same time (e.g. first responders in an industrial
emergency situation).
[0027] FIG. 3 illustrates the preferred embodiment of a
multichannel, gas sensor array 305 where a silicon substrate 302 is
used with a MEMS manufacturing process to build the structure of
the sensing channels on which the molecular formulations 204 can be
deposited. For illustration purposes the size of the individual
sensor die 304 is shown as being much larger than achievable in
practice; a single 8'' wafer 300 will typically yield several
thousand multi-gas capable sensor chips. An array 305 of sensing
elements 102 is implemented on a single die 304 and each wafer 300
yields several thousand dies, or chips 304. Each sensing element
102 can then be functionalized by depositing a specific molecular
formulation 204 thereon.
[0028] Thus, after MEMS manufacturing, additional steps are
required to complete the fabrication of each sensing element 102.
First, molecular formulations 204 are deposited and cured using
specialized equipment. This happens at wafer level and the
equipment is designed in a modular fashion to allow for the scaling
of the output of a manufacturing facility by duplicating modules
and fabrication processes in a copy-exactly fashion. After
completion of the manufacturing steps, the wafers 300 must be
singulated using a clean dicing technology in order to prevent
damage to the sensing elements 102. An example of such technology
is Stealth dicing.
[0029] The third element is the electronic transducer that detects
changes in the electrical characteristics of the sensor array 305,
provides signal conditioning and converts the analog signal from
the sensor elements 102 into a digital form usable by the data
acquisition system, described in more detail below. The transducer
can be a low voltage analog circuit that provides biasing to the
array of sensing channels and two functional modes: parking and
measurement. Sensing channels are in parking mode either when not
in measurement mode or when not used/enabled at all for a given
application. The circuitry is designed to maintain the sensing
channels in a linear region of operation, to optimize power
consumption, to enable any combination of channels in either
parking or measurement modes and to provide a seamless transition
between modes.
[0030] FIG. 5 shows the basic flow of information through a
complete nano gas sensor system, such as system 400 described in
more detail below. When the sensor array 305 is exposed to the
mixture of gas analytes 108 in its environment, in step 502, the
sensitive layers 104 of the materials deposited on the sensor
elements 102, or sensing channels react, according to their
formulation 202, to the presence of specific component gases in the
mixture. The reaction causes a change in the electrical
characteristics of the sensing channels 102, which is captured by
the transducer in the electronics sub-system, in step 504, and then
analyzed by the pattern recognition system programmed in the
sub-system MCU, in step 506. The output is an absolute value of the
concentration of the gases being detected. This is then combined,
in step 508, with other desirable meta-data such as time or
geo-location into a digital record. This digital record (or a
portion of it) can optionally be displayed locally in step 510 (for
example, in the case of a wearable application where the sensor is
paired to a phone, the data can be further manipulated and
displayed by a specially written mobile application running on the
phone). More importantly the data is uploaded, via a mechanism that
is dependent on the application, to a Cloud data platform in step
512, where the data can be normalized in step 514 and accessed via
various application in step 516.
[0031] The fourth element is a MCU-based data acquisition and
measurement engine, which also provides additional functions such
as overall sensor system management and communication, as necessary
with encryption, to and from a larger system into which the sensor
is embedded.
[0032] The third and fourth elements are designed to work together
and to form a complete electronic sub-system specifically tuned to
work with the array of sensing channels 305 implemented as a
separate component. The transducer 404 is firmware configurable to
provide optimal A/D conversion for a pattern recognition system
running on the MCU 406 and implementing the gas detection and
measurement algorithm(s).
[0033] The electronic sub-system 402 is suitable for implementation
in a variety of technologies depending on target use model and
technical/cost trade-offs. PCB implementations will enable quick
turn-around and the declination of a family of related products
(for instance with different communication interfaces) to support
multiple form factors and applications with the same core
electronics. When size and power/performance trade-offs are
critical, the electronic sub-system 402 is implemented as a System
On a Chip (SoC), which can then be integrated together with a MEMS
chip carrying the array of sensing channels 305 into a System In a
Package (SIP).
[0034] The sensor die 304 must then be assembled with the sensor's
electronic sub-system to complete the hybrid nanostructure gas
sensor 400 for which a functional block diagram is shown in FIG.
4.
[0035] The electronic sub-system can be implemented as a PCB or as
a SoC. If the PCB route is followed the sensor die 304 can be
either wire-bonded to the electronic sub-system 402 board after
completion of the PCB Assembly (PCBA) step or, if the sensor die
304 has itself been individually assembled in a SMT package, it can
be soldered on the board as part of PCBA. If the SoC route is
followed, the sensor die together with the SoC die of the
electronic sub-system 402 can be stacked and assembled together
into a single package (System In a Package) or each can possibly be
assembled into individual packages.
[0036] Either assembled into its own package or assembled into a
SIP, the sensor chip 304 must be exposed to ambient air. Therefore,
the package lid must include a hole of sufficient size over the
sensor.
[0037] Testing happens at various points of the sensor
manufacturing process.
[0038] After sensor functionalization (deposition of the molecular
formulations 204), certain handling precautions must be followed
for the rest of the product manufacturing flow to prevent
accidental damage to the sensor chip 304 (e.g. a pick and place
tool must not make contact with the surface of the sensing
elements).
[0039] The fifth element is the gas detection and measurement
algorithm. The algorithm implements a method for predicting target
gas concentration by reading the hybrid nanostructure sensor
array's multivariate output and processing it inside the algorithm.
The algorithm analyzes sensor signals in real time and outputs
estimated values for concentrations of target gases. The algorithm
development is based on models that are specific to the materials
deposited on the sensing channels of the sensor array. These models
are trained based on the collection of an abundant volume of data
in the laboratory (multiple concentrations of target gases,
combinations of gases, various values of temperature, relative
humidity and other environmental parameters). Sophisticated
supervised modeling techniques are used to attain the best possible
agreement between true and predicted values of target gas
concentrations. Prior to deployment, extensive lab and field
testing is carried out to optimize model performance and finalize
sensor validation.
[0040] In certain embodiments, the algorithm can use exponential
average low pass filtering to ensure efficient memory management
and fast processing speeds. FIG. 8 is a graph illustrating the
filtering of the high frequency noise using the exponential average
low pass filter. The high frequency component is depicted as plot
802, while the filtered signal is plotted as line 804.
[0041] FIG. 9 is a diagram illustrating the computation of settled
resistance value estimate for transient material response to
changing gas concentration. First, in step 902, the resistance rate
for each channel is computed. The value of resistance rate for each
channel is then taken as a byproduct of the exponential average low
pass filter and multiplied by the material time constant to
evaluate the transient resistance in step 904. The time constant is
the measured property of the material response to the target gas.
The settled resistance estimate, which is a sum of the transient
resistance and a current resistance value is then determined in
step 906.
[0042] Thus, in certain embodiments, a method for the selective
detection of a target gas and measuring the concentration values
comprises processing the resistance values of 8, 16, 32, 64, or 128
channels of nanohybrid gas sensors sampled every 80, 120, 160, or
200 milliseconds and filtering out the high frequency noise using
the exponential average low pass filter illustrated in FIG. 8. This
is then followed by signal processing such as: computing the rate
of sensor response change; and evaluating sensor response in
relation to other sensor channels including a temperature sensor
channel.
[0043] Predicting settled sensor resistance values, as shown in
FIG. 9, can then be used to estimate algorithm input values when
sensor output values are in transition following the change in gas
concentration values. This is done in order to accelerate target
gas concentration predictions without the need for waiting a long
time to reach equilibrium in interaction between the sensor
material and changing gas.
[0044] A gas model can then be used to relate change in resistance
of material segments to target gas concentration via model
coefficients. The relation between sensor response and change in
target gas concentration is described by the equation:
C.sup.i=.SIGMA..sub.j.alpha..sub.j.sup.i(R.sup.j-R.sup.j.sub.0)/R.sup.j.-
sub.0+C.sup.i.sub.0.
[0045] R.sup.j.sub.0 is defined as the channel resistance for
material j right before the exposure, R.sup.j is defined as the
resistance right after the exposure. The sum is taken over all
channels of various materials j contributing to the algorithm
input.
[0046] C.sup.i.sub.0 is defined as the target gas i concentration
right before the exposure, C.sup.i is defined as the target gas i
concentration right after exposure. For every target gas i each
material j channel contains certain material-gas coefficient value
.alpha..sub.j.sup.i.
[0047] Preprocessed signals from nanohybrid gas sensor channels can
then be grouped into segments each representing a specific material
deposited on sensor channel. Multiple segments can be used in
engaging a single target gas model. Multiple model concurrently
executed in the algorithm predicting concentration values for
gases, such as: NO2, CO, O3, CH2O, CH4, etc.
[0048] Response of a sensor is a result of exposure to multiple gas
constituents in the atmosphere as well as the reaction of the
sensor to various environmental factors such as humidity,
temperature, pressure, and air flow. The algorithm resolves this
cross-sensitivity complexity via an over-constrained system of
modeling equations. Compensation coefficients to account for
environmental factors are: i. humidity compensation coefficient; ii
temperature compensation coefficient; and iii pressure and air flow
compensation coefficient.
[0049] The optimal solution to the system of equations is the
output of the algorithm containing the concentration values for
target gases.
[0050] In certain embodiments, a method for tracking null reference
baseline using multiple-channel time series signal from a hybrid
nanostructure gas sensor comprises taking resistance values of
multiple channels of nanohybrid gas sensors and comparing them
against the reference resistance values benchmarked in ambient
atmosphere with known concentrations of contributing gases. The
deviations from benchmarked values can then be used to adjust the
starting values for target gas concentrations. The adjustment
process uses inputs from temperature, humidity and multiple
channels of nanohybrid gas sensors.
[0051] The first five elements together constitute the hybrid
nanostructure gas sensor 400 and provide all the functionality
necessary to detect multiple gases 108 in ambient air at the same
time and to report their absolute concentrations. The sensing
capability of the hybrid nanostructure sensor array 305 is always
"on" and the gas detection and measurement algorithm makes it
possible for the sensor 400 to require no special calibration step
before use and to remain self-calibrating through its operational
life.
[0052] The sixth element is the Cloud Data Platform that enables a
virtually unlimited number of sensors 400 deployed as part of a
virtually unlimited number of applications to be hosted in a global
database where big data techniques can be used to analyze, query
and visualize the information to infer actionable insight. The use
of a Cloud-based environment provides all the necessary flexibility
to customize how the data can be partitioned, organized, protected
and accessed based on the rights of individual tenants.
[0053] The Cloud data platform provides another layer of
sophistication to the system by allowing Cloud applications to
operate on the data set. For instance, sensors 400 that are located
in the same vicinity would typically report consistent gas values
thus allowing errant results to be identified and a possible
malfunction of one node of a network of sensors investigated.
[0054] The continuous collection of highly granular gas information
by a multitude of connected devices (IoT--Internet Of Things) is
critical to go beyond monitoring to generate actionable insight
from large amount of collected data (Big Data Analytics, Artificial
Intelligence).
[0055] A few application examples are highlighted below.
Example 1
[0056] We take 20,000 breaths every day and the air we breathe
impacts our health--the science is already clear on this--but we
rarely know what is in the air we breathe. To take meaningful
action, consumers, scientists, public officials and business owners
need the ability to measure air pollution at a personal, local and
granular level which has, before this invention, been impossible
due to the limitations of commercially available gas sensors
mentioned above.
[0057] Mounting evidence suggests that prenatal and early life
exposure to common environmental toxins, such as air pollution from
fossil fuels, can cause lasting damage to the developing human
brain. These effects are especially pronounced in highly vulnerable
fetuses, babies, and toddlers as most of the brain's structural and
functional architecture is established during these early
developmental periods. These disruptions to healthy brain
development can cause various cognitive, emotional, and behavioral
problems in later infancy and childhood.
[0058] The sensor technology described herein allows researchers to
gather highly detailed, accurate data about pregnant women's
exposure to environmental air pollution and the resulting effects
on the developing brain. The availability of this technology will
represent a profound advance on current methods and efforts in the
field that will have far-reaching consequences for improving
newborn and child health throughout the world.
[0059] More generally, personal air monitoring and local indoor and
outdoor monitoring will be a breakthrough for scientific research,
healthcare interventions, personal preventive actions, advocacy and
more.
[0060] The sensor technology described herein can deliver complete
processing and gas results to a broad spectrum of smart systems
under development for the Smart Cities of tomorrow. The sensor is
designed for Plug and Play integration into IoT devices and the
small form factor is compatible with a multitude of devices from
LED lights to smart meters, to standalone monitoring stations, to
non-stationary devices (drones, public vehicles, wearables, phones,
etc.).
Example 2
[0061] The sensor technology described herein can be used in smart
appliances such as connected refrigerators, that will help
customers monitor food freshness, detect spoilage and the presence
of harmful pesticide residues. The simultaneous, multi-gas, sensing
capability of the invention will enable sensors that can recognize
the gas patterns associated with the condition of specific
foods.
Example 3
[0062] A network or grid of the sensors 400 described herein, can
be integrated into industrial areas such as petrochemical complexes
and oil refineries to allow companies to monitor the sites during
regular operation (e.g. for leaks) or in the event of natural or
human-made disasters. The sensors can also be installed in drones
for data collection in hard to reach or potentially dangerous area.
The ability of the technology to be deployed in wearables and in
fixed and mobile networks will provide both personal protection and
granular data across large area, allow the constant monitoring of a
facility for preventive measures to be taken in a timely fashion,
save critical time when urgent decision making is required and
provide invaluable information to protect workers and emergency
personnel.
[0063] The same technology can place powerful new tools in the
hands of first responders and officials responsible for public
safety and homeland security.
[0064] FIG. 6 shows an example product 600, in this case a
battery-powered wearable device, with the sensor 400 implemented as
a small PCB. The sensor technology lends itself to integration into
any number of IoT devices. While the sensor does not need the
active creation of an airflow to function, the sensitive layers 104
at the surface of the sensor must be exposed to ambient air and at
the same time provided a reasonable amount of protection from dust
and fluids. This is usually achieved by designing an air interface
that ensures that the sensor 400 is behind a perforated shield
(e.g. the lid of an enclosure) with a thin membrane (PTFE, 0.5 um
mesh) being used to provide splash and dust protection. Outdoor
applications may require the design of a more complicated air
interface to meet the weather-proofing requirements.
[0065] FIG. 7 is a block diagram illustrating an example wired or
wireless system 550 that can be used in connection with various
embodiments described herein. For example the system 550 can be
used as or in conjunction with one or more of the platforms,
devices or processes described above, and may represent components
of a device, such as sensor 400, the corresponding backend or cloud
server(s), and/or other devices described herein. The system 550
can be a server or any conventional personal computer, or any other
processor-enabled device that is capable of wired or wireless data
communication. Other computer systems and/or architectures may be
also used, as will be clear to those skilled in the art.
[0066] The system 550 preferably includes one or more processors,
such as processor 560. Additional processors may be provided, such
as an auxiliary processor to manage input/output, an auxiliary
processor to perform floating point mathematical operations, a
special-purpose microprocessor having an architecture suitable for
fast execution of signal processing algorithms (e.g., digital
signal processor), a slave processor subordinate to the main
processing system (e.g., back-end processor), an additional
microprocessor or controller for dual or multiple processor
systems, or a coprocessor. Such auxiliary processors may be
discrete processors or may be integrated with the processor 560.
Examples of processors which may be used with system 550 include,
without limitation, the Pentium.RTM. processor, Core i7.RTM.
processor, and Xeon.RTM. processor, all of which are available from
Intel Corporation of Santa Clara, Calif. Example processor that can
be used in system 400 include the ARM family of processors and the
new open source RISC-V processor architecture.
[0067] The processor 560 is preferably connected to a communication
bus 555. The communication bus 555 may include a data channel for
facilitating information transfer between storage and other
peripheral components of the system 550. The communication bus 555
further may provide a set of signals used for communication with
the processor 560, including a data bus, address bus, and control
bus (not shown). The communication bus 555 may comprise any
standard or non-standard bus architecture such as, for example, bus
architectures compliant with industry standard architecture (ISA),
extended industry standard architecture (EISA), Micro Channel
Architecture (MCA), peripheral component interconnect (PCI) local
bus, or standards promulgated by the Institute of Electrical and
Electronics Engineers (IEEE) including IEEE 488 general-purpose
interface bus (GPIB), IEEE 696/S-100, and the like.
[0068] System 550 preferably includes a main memory 565 and may
also include a secondary memory 570. The main memory 565 provides
storage of instructions and data for programs executing on the
processor 560, such as one or more of the functions and/or modules
discussed above. It should be understood that programs stored in
the memory and executed by processor 560 may be written and/or
compiled according to any suitable language, including without
limitation C/C++, Java, JavaScript, Pearl, Visual Basic, .NET, and
the like. The main memory 565 is typically semiconductor-based
memory such as dynamic random access memory (DRAM) and/or static
random access memory (SRAM). Other semiconductor-based memory types
include, for example, synchronous dynamic random access memory
(SDRAM), Rambus dynamic random access memory (RDRAM), ferroelectric
random access memory (FRAM), and the like, including read only
memory (ROM).
[0069] The secondary memory 570 may optionally include an internal
memory 575 and/or a removable medium 580, for example a floppy disk
drive, a magnetic tape drive, a compact disc (CD) drive, a digital
versatile disc (DVD) drive, other optical drive, a flash memory
drive, etc. The removable medium 580 is read from and/or written to
in a well-known manner. Removable storage medium 580 may be, for
example, a floppy disk, magnetic tape, CD, DVD, SD card, etc.
[0070] The removable storage medium 580 is a non-transitory
computer-readable medium having stored thereon computer executable
code (i.e., software) and/or data. The computer software or data
stored on the removable storage medium 580 is read into the system
550 for execution by the processor 560.
[0071] In alternative embodiments, secondary memory 570 may include
other similar means for allowing computer programs or other data or
instructions to be loaded into the system 550. Such means may
include, for example, an external storage medium 595 and an
interface 590. Examples of external storage medium 595 may include
an external hard disk drive or an external optical drive, or and
external magneto-optical drive.
[0072] Other examples of secondary memory 570 may include
semiconductor-based memory such as programmable read-only memory
(PROM), erasable programmable read-only memory (EPROM),
electrically erasable read-only memory (EEPROM), or flash memory
(block oriented memory similar to EEPROM). Also included are any
other removable storage media 580 and communication interface 590,
which allow software and data to be transferred from an external
medium 595 to the system 550.
[0073] System 550 may include a communication interface 590. The
communication interface 590 allows software and data to be
transferred between system 550 and external devices (e.g.
printers), networks, or information sources. For example, computer
software or executable code may be transferred to system 550 from a
network server via communication interface 590. Examples of
communication interface 590 include a built-in network adapter,
network interface card (NIC), Personal Computer Memory Card
International Association (PCMCIA) network card, card bus network
adapter, wireless network adapter, Universal Serial Bus (USB)
network adapter, modem, a network interface card (NIC), a wireless
data card, a communications port, an infrared interface, an IEEE
1394 fire-wire, or any other device capable of interfacing system
550 with a network or another computing device.
[0074] Communication interface 590 preferably implements industry
promulgated protocol standards, such as Ethernet IEEE 802
standards, Fiber Channel, digital subscriber line (DSL),
asynchronous digital subscriber line (ADSL), frame relay,
asynchronous transfer mode (ATM), integrated digital services
network (ISDN), personal communications services (PCS),
transmission control protocol/Internet protocol (TCP/IP), serial
line Internet protocol/point to point protocol (SLIP/PPP), and so
on, but may also implement customized or non-standard interface
protocols as well.
[0075] Software and data transferred via communication interface
590 are generally in the form of electrical communication signals
605. These signals 605 are preferably provided to communication
interface 590 via a communication channel 600. In one embodiment,
the communication channel 600 may be a wired or wireless network,
or any variety of other communication links. Communication channel
600 carries signals 605 and can be implemented using a variety of
wired or wireless communication means including wire or cable,
fiber optics, conventional phone line, cellular phone link,
wireless data communication link, radio frequency ("RF") link, or
infrared link, just to name a few.
[0076] Computer executable code (i.e., computer programs or
software) is stored in the main memory 565 and/or the secondary
memory 570. Computer programs can also be received via
communication interface 590 and stored in the main memory 565
and/or the secondary memory 570. Such computer programs, when
executed, enable the system 550 to perform the various functions of
the present invention as previously described.
[0077] In this description, the term "computer readable medium" is
used to refer to any non-transitory computer readable storage media
used to provide computer executable code (e.g., software and
computer programs) to the system 550. Examples of these media
include main memory 565, secondary memory 570 (including internal
memory 575, removable medium 580, and external storage medium 595),
and any peripheral device communicatively coupled with
communication interface 590 (including a network information server
or other network device). These non-transitory computer readable
mediums are means for providing executable code, programming
instructions, and software to the system 550.
[0078] In an embodiment that is implemented using software, the
software may be stored on a computer readable medium and loaded
into the system 550 by way of removable medium 580, I/O interface
585, or communication interface 590. In such an embodiment, the
software is loaded into the system 550 in the form of electrical
communication signals 605. The software, when executed by the
processor 560, preferably causes the processor 560 to perform the
inventive features and functions previously described herein.
[0079] In an embodiment, I/O interface 585 provides an interface
between one or more components of system 550 and one or more input
and/or output devices. Example input devices include, without
limitation, keyboards, touch screens or other touch-sensitive
devices, biometric sensing devices, computer mice, trackballs,
pen-based pointing devices, and the like. Examples of output
devices include, without limitation, cathode ray tubes (CRTs),
plasma displays, light-emitting diode (LED) displays, liquid
crystal displays (LCDs), printers, vacuum florescent displays
(VFDs), surface-conduction electron-emitter displays (SEDs), field
emission displays (FEDs), and the like.
[0080] The system 550 also includes optional wireless communication
components that facilitate wireless communication over a voice and
over a data network. The wireless communication components comprise
an antenna system 610, a radio system 615 and a baseband system
620. In the system 550, radio frequency (RF) signals are
transmitted and received over the air by the antenna system 610
under the management of the radio system 615.
[0081] In one embodiment, the antenna system 610 may comprise one
or more antennae and one or more multiplexors (not shown) that
perform a switching function to provide the antenna system 610 with
transmit and receive signal paths. In the receive path, received RF
signals can be coupled from a multiplexor to a low noise amplifier
(not shown) that amplifies the received RF signal and sends the
amplified signal to the radio system 615.
[0082] In alternative embodiments, the radio system 615 may
comprise one or more radios that are configured to communicate over
various frequencies. In one embodiment, the radio system 615 may
combine a demodulator (not shown) and modulator (not shown) in one
integrated circuit (IC). The demodulator and modulator can also be
separate components. In the incoming path, the demodulator strips
away the RF carrier signal leaving a baseband receive audio signal,
which is sent from the radio system 615 to the baseband system
620.
[0083] If the received signal contains audio information, then
baseband system 620 decodes the signal and converts it to an analog
signal. Then the signal is amplified and sent to a speaker. The
baseband system 620 also receives analog audio signals from a
microphone. These analog audio signals are converted to digital
signals and encoded by the baseband system 620. The baseband system
620 also codes the digital signals for transmission and generates a
baseband transmit audio signal that is routed to the modulator
portion of the radio system 615. The modulator mixes the baseband
transmit audio signal with an RF carrier signal generating an RF
transmit signal that is routed to the antenna system and may pass
through a power amplifier (not shown). The power amplifier
amplifies the RF transmit signal and routes it to the antenna
system 610 where the signal is switched to the antenna port for
transmission.
[0084] The baseband system 620 is also communicatively coupled with
the processor 560. The central processing unit 560 has access to
data storage areas 565 and 570. The central processing unit 560 is
preferably configured to execute instructions (i.e., computer
programs or software) that can be stored in the memory 565 or the
secondary memory 570. Computer programs can also be received from
the baseband processor 610 and stored in the data storage area 565
or in secondary memory 570, or executed upon receipt. Such computer
programs, when executed, enable the system 550 to perform the
various functions of the present invention as previously described.
For example, data storage areas 565 may include various software
modules (not shown).
[0085] Various embodiments may also be implemented primarily in
hardware using, for example, components such as application
specific integrated circuits (ASICs), or field programmable gate
arrays (FPGAs). Implementation of a hardware state machine capable
of performing the functions described herein will also be apparent
to those skilled in the relevant art. Various embodiments may also
be implemented using a combination of both hardware and
software.
[0086] Furthermore, those of skill in the art will appreciate that
the various illustrative logical blocks, modules, circuits, and
method steps described in connection with the above described
figures and the embodiments disclosed herein can often be
implemented as electronic hardware, computer software, or
combinations of both. To clearly illustrate this interchangeability
of hardware and software, various illustrative components, blocks,
modules, circuits, and steps have been described above generally in
terms of their functionality. Whether such functionality is
implemented as hardware or software depends upon the particular
application and design constraints imposed on the overall system.
Skilled persons can implement the described functionality in
varying ways for each particular application, but such
implementation decisions should not be interpreted as causing a
departure from the scope of the invention. In addition, the
grouping of functions within a module, block, circuit or step is
for ease of description. Specific functions or steps can be moved
from one module, block or circuit to another without departing from
the invention.
[0087] Moreover, the various illustrative logical blocks, modules,
functions, and methods described in connection with the embodiments
disclosed herein can be implemented or performed with a general
purpose processor, a digital signal processor (DSP), an ASIC, FPGA
or other programmable logic device, discrete gate or transistor
logic, discrete hardware components, or any combination thereof
designed to perform the functions described herein. A
general-purpose processor can be a microprocessor, but in the
alternative, the processor can be any processor, controller,
microcontroller, or state machine. A processor can also be
implemented as a combination of computing devices, for example, a
combination of a DSP and a microprocessor, a plurality of
microprocessors, one or more microprocessors in conjunction with a
DSP core, or any other such configuration.
[0088] Additionally, the steps of a method or algorithm described
in connection with the embodiments disclosed herein can be embodied
directly in hardware, in a software module executed by a processor,
or in a combination of the two. A software module can reside in RAM
memory, flash memory, ROM memory, EPROM memory, EEPROM memory,
registers, hard disk, a removable disk, a CD-ROM, or any other form
of storage medium including a network storage medium. An exemplary
storage medium can be coupled to the processor such the processor
can read information from, and write information to, the storage
medium. In the alternative, the storage medium can be integral to
the processor. The processor and the storage medium can also reside
in an ASIC.
[0089] Any of the software components described herein may take a
variety of forms. For example, a component may be a stand-alone
software package, or it may be a software package incorporated as a
"tool" in a larger software product. It may be downloadable from a
network, for example, a website, as a stand-alone product or as an
add-in package for installation in an existing software
application. It may also be available as a client-server software
application, as a web-enabled software application, and/or as a
mobile application.
[0090] While certain embodiments have been described above, it will
be understood that the embodiments described are by way of example
only. Accordingly, the systems and methods described herein should
not be limited based on the described embodiments. Rather, the
systems and methods described herein should only be limited in
light of the claims that follow when taken in conjunction with the
above description and accompanying drawings.
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