# US 2020/0064291 A1 · Pattern recognition algorithm for identifying and quantifying single and mixed contaminants in air with an array of nanomaterial-based gas sensors

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- Number: US 2020/0064291 A1
- Document ID: US20200064291A1
- Status: Published application
- Kind: A1
- Application number: 16/547,498
- Filed: 2019-08-21
- Published: 2020-02-27
- Earliest claimed: 2018-08-22
- Assignees: Aernos, Inc.
- Applicants: AerNos, Inc.
- Inventors: Alexey Varganov; Sundip R. Doshi
- Web page: https://patentgrep.com/patent/US20200064291A1

## CPC

- G01N 33/0031: {comprising two or more sensors, e.g. a sensor array}
- B01J 21/185: {Carbon nanotubes (carbon nanotubes per se C01B32/15)}
- B01J 23/00: Catalysts comprising metals or metal oxides or hydroxides, not provided for in group B01J21/00 (B01J21/16 takes precedence)
- B01J 31/1691: {Coordination polymers, e.g. metal-organic frameworks [MOF] (preparation of metal complexes containing carboxylic acid moieties C07C51/418; MOF's per se C07F)}
- G01N 27/046: {provided with temperature compensation}
- G01N 27/12: of a solid body in dependence upon absorption of a fluid; of a solid body in dependence upon reaction with a fluid {, for detecting components in the fluid}
- G01N 27/121: {for determining moisture content, e.g. humidity, of the fluid (moisture content of the tested material G01N27/048)}
- G01N 27/122: {Circuits particularly adapted therefor, e.g. linearising circuits}
- G01N 27/125: {Composition of the body, e.g. the composition of its sensitive layer}
- G01N 27/127: {comprising nanoparticles}
- G01N 27/128: {Microapparatus}
- G01N 27/226: {Construction of measuring vessels; Electrodes therefor}
- G01N 27/227: {Sensors changing capacitance upon adsorption or absorption of fluid components, e.g. electrolyte-insulator-semiconductor sensors, MOS capacitors (G01N27/225 takes precedence)}
- G01N 27/228: {Circuits therefor (measuring capacitance per se G01R27/26)}
- G01N 27/4075: {Composition or fabrication of the electrodes and coatings thereon, e.g. catalysts}
- G01N 33/0011: {Sample conditioning (preparing specimens for investigation G01N1/28)}
- G01N 33/0022: {using a number of analysing channels}
- G01N 33/0037: {NOx}
- G01N 33/004: {CO or CO2}
- G01N 33/0042: {SO2 or SO3}
- G01N 33/0047: {Organic compounds}
- G05B 19/042: using digital processors (G05B19/05 takes precedence)
- B82Y 30/00: Nanotechnology for materials or surface science, e.g. nanocomposites
- B82Y 40/00: Manufacture or treatment of nanostructures
- G05B 2219/25127: Bus for analog and digital communication
- G05B 2219/25257: Microcontroller
- Y02A 50/20: Air quality improvement or preservation, e.g. vehicle emission control or emission reduction by using catalytic converters
- G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q)
- B01J: CHEMICAL OR PHYSICAL PROCESSES, e.g. CATALYSIS OR COLLOID CHEMISTRY; THEIR RELEVANT APPARATUS
- G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G)
- B82Y: SPECIFIC USES OR APPLICATIONS OF NANOSTRUCTURES; MEASUREMENT OR ANALYSIS OF NANOSTRUCTURES; MANUFACTURE OR TREATMENT OF NANOSTRUCTURES
- Y02A: TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE

## 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.

## Claims

### Claim 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.

### Claim 2 (depends on 1)

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.

### Claim 3 (depends on 2)

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:
Ci=Σjαji(Rj−Rj0)/Rj0+Ci0;
Wherein Rj0 is defined as the channel resistance for material j right before the exposure, Rj 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
Ci0 is defined as the target gas i concentration right before the exposure, Ci 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 αji.

### Claim 4 (depends on 1)

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.

### Claim 5 (depends on 4)

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.

### Claim 6 (depends on 1)

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.

### Claim 7 (depends on 6)

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.

### Claim 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.