Classifications

• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
  • G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
  • G16C20/30—Prediction of properties of chemical compounds, compositions or mixtures
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
  • G01N27/02—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance
  • G01N27/04—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance by investigating resistance
  • G01N27/12—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance by investigating resistance 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
  • G01N27/122—Circuits particularly adapted therefor, e.g. linearising circuits
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
  • G06N20/00—Machine learning
  • G06N20/10—Machine learning using kernel methods, e.g. support vector machines [SVM]
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
  • G16C60/00—Computational materials science, i.e. ICT specially adapted for investigating the physical or chemical properties of materials or phenomena associated with their design, synthesis, processing, characterisation or utilisation
• • B—PERFORMING OPERATIONS; TRANSPORTING
  • B82—NANOTECHNOLOGY
  • B82Y—SPECIFIC USES OR APPLICATIONS OF NANOSTRUCTURES; MEASUREMENT OR ANALYSIS OF NANOSTRUCTURES; MANUFACTURE OR TREATMENT OF NANOSTRUCTURES
  • B82Y30/00—Nanotechnology for materials or surface science, e.g. nanocomposites
• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
  • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
  • G01N33/0004—Gaseous mixtures, e.g. polluted air
  • G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
  • G01N33/0027—General constructional details of gas analysers, e.g. portable test equipment concerning the detector
  • G01N33/0031—General constructional details of gas analysers, e.g. portable test equipment concerning the detector comprising two or more sensors, e.g. a sensor array
  • G01N33/0034—General constructional details of gas analysers, e.g. portable test equipment concerning the detector comprising two or more sensors, e.g. a sensor array comprising neural networks or related mathematical techniques
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
  • G06N3/00—Computing arrangements based on biological models
  • G06N3/004—Artificial life, i.e. computing arrangements simulating life
  • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
  • G06N7/00—Computing arrangements based on specific mathematical models
  • G06N7/01—Probabilistic graphical models, e.g. probabilistic networks
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
  • G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
  • G16C20/70—Machine learning, data mining or chemometrics

Definitions

• This invention relates broadly to a computerized method and a sensor device for determining respective presence and concentrations of multiple gases and/or volatile organic compounds, and to a method of training the sensor device.
• chemical fingerprints of each gas or VOC are derived from a unique arrayed pattern formed by a combination of electrical responses acquired from individual sensing elements, each crafted differently in sensitivity, cross sensitivity and specificity, in a sensor array when it is exposed to an analyte.
• a chemical fingerprint can be a unique combinatorial variation in the mean current from an array of 9 sensors which are arranged in a 3-by-3 row and column format, whereby the 3 cells at the top right corner would always be higher than the lower 3 cells in the bottom left corner when exposed to a particular type of analyte and not others. This also increases the complexity and cost of such sensors.
• the method disclosed in the abovementioned paper still relies on the overall incremental change in the conductivity of the chemiresistor when it is under exposure from multiple gases/VOCs in the environment for the measurement of the concentration level therefore, one would also not be able to tell what percentage of changes each individual gas/VOCs has contributed to the overall incremental change detected in the conductivity of the chemiresistor.
• the input features used for machine learning in existing studies/devices contain individual electrical responses from respective multiple sensors placed in proximity and exposed to the same analyte. This increases the complexity of such sensors.
• Embodiments of the present invention seek to address one or more of the above- mentioned needs.
• a computerized method of determining respective presence and concentrations of multiple gases and/or volatile organic compounds comprising the steps of exposing one or more sensing elements with the same chemical and physical properties to the multiple gases and/or volatile organic compounds; measuring electrical time series data of the one or more sensing elements during the exposure; analyzing the electrical time series data and Lorentzian noise information of the electrical time series data by an Artificial Intelligence (AI) system; and determining the respective presence and concentrations of the multiple gases and/or volatile organic compounds based on the analysis of the electrical time series data and the Lorentzian noise information of the electrical time series data.
• AI Artificial Intelligence
• a sensor device capable of determining respective presence and concentrations of multiple gases and/or volatile organic compounds
• the sensor device comprising one or more sensing elements made from a substantially identical sensing material; and an Artificial Intelligence (AI) system; wherein the AI system is configured to analyze electrical time series data of the one or more sensing elements and Lorentzian noise information of the electrical time series data to determine the respective presence and/or concentrations of multiple gases and/or volatile organic compounds based on the analysis of the electrical time series data and the Lorentzian noise information of the electrical time series data.
• AI Artificial Intelligence
• a method of training a sensor device of the second aspect to be capable of determining respective presence and/or concentrations of multiple gases and/or volatile organic compounds.
• Fig. 1 shows diagrams illustrating the fabrication process of black phosphorus (bP), which is a two-dimensional material (2DM), chemiresistor, according to an example embodiment.
• bP black phosphorus
• 2DM two-dimensional material
• Fig. 2 shows the output characteristic (drain current, ID, VS drain voltage, VD) of the chemiresistor, according to an example embodiment.
• Fig. 3 shows the experimental setup of a wireless all-in-one gas sensor node 300 according to an example embodiment for relative humidity (RH) measurement, according to an example embodiment.
• RH relative humidity
• Fig. 4 shows a block diagram of a sensor node according to an example embodiment.
• Fig. 5 shows a flow diagram illustrating the data collection process according to a preferred embodiment in training a single 2DM-based chemiresistor to classify and quantify multiple species of gases/vapours that are present in the same environment.
• Fig. 6 shows the statistical information (i.e. mean, median, and standard deviation) of 68 samples collected for the first 4 days, according to an example embodiment.
• Fig. 7 shows variations in the measured current at similar RH, according to an example embodiment.
• Fig. 8 shows the experimental setup for the training and testing of an all-in-one gas sensor according to an example embodiment for CO2, N2O, and RH.
• Fig. 9 shows the experimental setup for the training and testing of an all-in-one gas sensor according to an example embodiment for no plant, plant without VOC emission, and plant with VOCs submission.
• Fig. 10 illustrates the process of training a single 2DM-based chemiresistor via machine learning (ML) for classifying and quantifying multiple sources of gases/VOCs that are present simultaneously in the same environment, according to an example embodiment.
• Fig. 11 shows the signal flow diagram illustrating prediction for Multi-gas/VOCs sensing according to an example embodiment.
• Fig. 12 describes briefly the system implementation block-diagram of an all-in-one gas sensor according to an example embodiment as an air quality sensor in a standalone consumer product for tracking health or as a climate monitoring sensing node in a wireless sensor network for production control in a factory or industrial safety application.
• Fig. 13 shows the characteristic Lorentzian frequencies extracted from 3 different ambient environments and can be seen as the first proof of principle for using characteristic Lorentzian frequencies to distinguish multiple gases from a single 2DM-based chemiresistor according to an example embodiment.
• Fig. 14 shows the characteristic Lorentzian frequencies extracted from 3 other different ambient environments of another ambient type-0, where there is no plant in the enclosure, another ambient type-1 where there is a plant that is known to not emit any VOCs in the enclosure, and finally another ambient type-2 where there is a plant that is known to emit strong VOCs in the enclosure, according to an example embodiment.
• Fig. 15 shows the prediction accuracy of an all-in-one sensor according to an example embodiment for RH.
• Fig. 16 shows the prediction accuracy of an all-in-one sensor according to an example embodiment for CO2 gas.
• Fig. 17 shows the prediction accuracy of an all-in-one sensor according to an example embodiment for N2O gas.
• Fig. 18 summarizes the classification and regression results achieved from two different 2DM-based sensors according to example embodiments.
• Figure 19 shows a flow chart illustrating a computerized method of determining respective presence and concentrations of multiple gases and/or volatile organic compounds, according to an example embodiment.
• Figure 20 shows a schematic diagram illustrating a sensor device capable of determining respective presence and concentrations of multiple gases and/or volatile organic compounds, according to an example embodiment.
• Embodiments of the present invention provide an all-in-one smart sensor device that can simultaneously detect the presence and quantify the concentration of multiple types of gases and VOCs wirelessly using only a single physical sensor, or an array of sensors with the same chemical and physical properties.
• the all-in-one sensor device uses two-dimensional layered materials that are highly sensitive to a broad variety of gases/VOCs and functionalizes its selectivity to different gases or VOCs through respective AI models (i.e. AI Engines), developed by exhaustive machine-learning (ML) in real environment settings.
• AI models i.e. AI Engines
• ML machine-learning
• the wireless all-in-one sensor smart sensor can be used on an Internet of Thing (IoT) platform in tracking the quality of health, product, and produce.
• IoT Internet of Thing
• Embodiments of the present invention have wide applications in environmental monitoring and emission control, personal and military safety, production control in agriculture, manufacturing and medical diagnostics.
• Embodiments of the present invention can also be adopted for heating, ventilation, and air conditioning, e.g heating, ventilation, and air conditioning (HVAC), to reduce energy consumption by better control of the air conditioning.
• HVAC heating, ventilation, and air conditioning
• the sensing material used according to example embodiments include two- dimensional layered inorganic materials (2DMs) that are highly sensitive to a broad variety of gases and VOCs (i.e. non- selective) by physical nature, while existing studies/devices use either highly selective materials that will only respond to a particular type of gases/VOCs, or perform chemical modifications or functionalization to the sensing material to create specificity to a particular analyte.
• 2DMs two- dimensional layered inorganic materials
• Chemical fingerprints of various gases or VOCs are derived from unique patterns detected using a machine learning algorithm on information gathered from the low-frequency noise spectra of a single chemiresistor type via ML training and deployment methodology.
• Embodiments of the present invention advantageously extract information not previously accessible from the FFT spectrum analyzer used in existing studies/devices via MF training and deployment methodology.
• Embodiments of the present invention use Forentzian frequencies that relate to the kinetics of the adsorption and desorption of the molecules as a result of vapor exposure, which corresponds to a much lower characteristic frequencies compared to the Forentzian frequencies that relate to the charge traps created as a result of vapor exposure as in existing studies/devices.
• the input features used for machine learning according to example embodiments do not contain individual electrical responses from respective multiple sensors placed in proximity and exposed to the same analyte. Instead, the input features used according to example embodiments contain information such as:
• time dependent electrical responses i.e. channel resistance
• Forentzian frequencies i.e. peaks with the maximum power spectral density
• their respective power spectral density from the plot of noise spectral density multiplied by frequency vs frequency (i.e. Forentzian noise spectral).
• the gas molecules can create specific traps and scattering centers in graphene, which lead to either fluctuation in the number of carriers due to the fluctuations of traps occupancy or to the mobility fluctuations due to fluctuations of the scattering cross sections.
• the kinetics of the adsorption and desorption of molecules from exposure would also contribute to noise.
• the characteristic time scale for the adsorption and desorption of vapours is reported to be in several hundreds of seconds.
• R channel resistance measured from a multi-meter and dR is the change in magnitude before exposure and after exposure.
• dR/R concentration of ethanol
• Embodiments of the present invention can instead advantageously provide a multi gas/vapour sensor using a single sensing material configured to not only determine the types of gas/vapour species present but also their respective concentration levels.
• Fig. 1 shows diagrams illustrating the fabrication process of a black phosphorus (bP), which is a two-dimensional material (2DM), chemiresistor for sensing relative humidity, according to example embodiments.
• a photoresist layer 100 is deposited on a substrate, here a glass substrate 102, to provide patterns 104, 106 for two electrical terminals 108, 110, here Au/Ni (30 nm/1 nm), by using a standard lift-off process after metal 112 deposition.
• an AJA ATC-2200 UHV Sputter was used for the metal 112 deposition and a laser writer LW405B was used for patterning of the photoresist layer 100.
• the electrical channel, here an exfoliated bP flake 114, of the chemiresistor 116 is then deposited across the two terminals 108, 110 by a dry transfer technique using polydimethylsiloxane (PDMS) in this example embodiment.
• PDMS polydimethylsiloxane
• the channel length and width of the chemiresistor is ⁇ 1 mm by 0.25 mm in a non-limiting example, and its Raman spectrum 118 shows the typical signature of a black phosphorus at 363 cm 1 , 440 cm 1 , and 467 cm 1 .
• the output characteristic (drain current, ID, VS drain voltage, VD) of the chemiresistor 116 is shown in Fig. 2. As there is no noticeable hysteresis loop in the measured current in the double voltage sweep measurement, there are no significant number of hidden traps to impede the chemiresistor’ s 116 current flow, allowing the bP chemiresistor 116 according to example embodiments to operate in low voltage range. It is noted that for large scale operation, commercial printing process will preferably be used to prepare chemiresistors according to various example embodiments, instead of dry transfer technique using polydimethylsiloxane (PDMS).
• PDMS polydimethylsiloxane
• Fig. 3 shows the experimental setup of a wireless all-in-one gas sensor node 300 according to an example embodiment for relative humidity (RH) measurement.
• the sensor node 300 comprises has a Bluetooth enabled tablet 302 to wirelessly receive the measured voltage across the chemiresistor 304 from a Bluetooth low energy microcontroller unit (TI CC2541) on a PCB 305, in this example embodiment.
• the relative humidity is controlled by the desiccator 306 filled with silica gel and the reference RH sensor 308 provides the reference data used in the machine learning according to an example embodiment.
• the detailed functions circuitry on the PCB 305 which includes an analog front-end unit (AFE), analog-to-digital converter (ADC), the microcontroller unit (MCU), and battery booster (here TI TPS61220 & LM4120) will be described with reference to the block diagram shown in Fig. 4.
• AFE analog front-end unit
• ADC analog-to-digital converter
• MCU microcontroller unit
• battery booster here TI TPS61220 & LM4120
• the chemiresistor 400 is connected across the control electrode (CE) and working electrode (WE) terminals of the AFE 402 (here TI LM91000).
• the bias voltage to the chemiresistor 400 is supplied by a 3 V coin-cell battery 404 (here CR-2032) and is preset to the lowest value of 25 mV.
• the battery booster unit 406 regulates this battery source and provides a stable reference voltage of 2.5 V to the control amplifier (Al) 407, the transimpedance amplifier (TIA) 409 and the analog-to-digital converter
• ADC found inside the micro controller unit and radio frequency system-on-chip (here MCU + RF SoC CC2541) 412.
• the reference electrode (RE) terminal of the AFE 402 is shorted to CE to ensure a constant 25 mV is held across CE and WE at all time.
• the AFE 402 operates like a potentiostat and any changes in the resistance of the chemiresistor 400 will be reflected in the current flowing across the terminals 408, 410 and be transformed into a voltage by the TIA 409.
• the ADC in the MCU 412 will then convert this analog voltage into a digital signal and the Bluetooth transmitter 414 will transmit this digital information to the receiver (Bluetooth enabled Tablet 302, Fig.
• Fig. 5 shows a flow diagram illustrating the data collection process according to a preferred embodiment in training a single 2DM-based chemiresistor to classify and quantify multiple species of gases/vapours that are present in the same environment.
• step 1 in an enclosed environment, expose a single 2DM-based chemiresistor to the desired number N and types of gases/VOCs in a controlled manner and decide which gases/VOCs to measure as well as the range of the concentration level to adjust.
• step 2 systematically vary the concentration of one gas/VOC and measure the corresponding channel resistance of the chemiresistor. Collect a dataset of measurements at different concentration levels, preferably ensuring the measurement points are equally distributed in the decided adjustment range. This dataset is classified as ambient type 0 (increment this number by one for each new dataset for the respective different gases/VOCs).
• step 3a repeat from step 1 and choose another gas/VOCs type until the concentration of all gases/VOCs have been varied.
• step 3b repeat from step 1 and choose two gas/VOCs type for simultaneous variation of concentration until the concentration of all two-gas/VOCs- combinations have been varied.
• step 3c repeat from step 1 and each time increase the number of gas/VOCs types that is simultaneously varied (i.e. three-gas/VOCs-combination, four- gas/VOCs-combinations etc.) until the concentration of all possible N-gas/VOCs-combinations have been varied. Data collection is then complete and machine training is started.
• the naming of the ambient type does not have to be based on how many parameters were varied during the training, as described above with reference to Figure 5.
• the naming of the ambient type may be based on when the training is carried out in a chronological manner.
• the trained models are suitable for use according to various example embodiments.
• Ambient Type 0 Variation in relative humidity, RH, and CO2 only.
• the desiccator 306 shown in Fig. 3 is first filled with freshly baked silica gel under the perforated plate with the battery powered wireless bP sensor 304. The desiccator 306 is then closed to allow the silica gel to reduce the RH in the desiccator 306. Once the readings on reference RH sensor 308 become stable, data collection is started by allowing the current flowing across the bP chemiresistor 304 to be recorded wirelessly on tablet 300 at an interval of 0.3 s for 3 mins.
• each sample reading of the RH value would consist of 600 data points representing the measured current flowing across the bP chemiresistor 304 during this 3 mins period. Datasets with any changes in the RH value within the 3 mins recording time would be discarded.
• the experiment is conducted across 5 months and the only time the environment of desiccator 306 is disrupted is to power on-and-off the wireless sensor (to conserve battery energy) at the start and end of each day.
• the ambient concentration of N2O is assumed to be not changing for this ambient type 0 but ambient CO2 concentration in the desiccator is found to be decreasing via diffusion throughout all the experiment of this ambient type 0.
• Fig. 6 shows the statistical information (i.e. mean, median, and standard deviation) of 68 (note: only 34 are shown in the figure for clarity purposes) samples collected for the first 4 days. From the figure, one can see the gradual decrease in the mean current of each sample with time, irrespective of RH. This is a sign that the bP chemiresistor is suffering from the effect of residual vapor adsorption on its surface This is further illustrated in Fig. 7, i.e. that the bP chemiresistor has not recovered to its original state, as the mean currents for the same RH taken at 3 different days are different. Nevertheless, it will be shown later that these negative effects have no bearings on the test score of the machine-trained gas sensing system according to example embodiments.
• Ambient Type 1 Variation in CO2 only.
• a photograph of an experimental setup for machine training a wireless 2DM-based gas sensing system for relative humidity and CO2 measurement is as shown in Fig. 8 (noting that the N2O reference sensor 803 is not used, i.e. like oxygen and nitrogen, N2O is part of the ambient air present but their concentration level is very low at around 0.00003%).
• the valve of the gas inlet is closed, and the CO2 can diffuse gradually through the edges of the door on the setup.
• the current flowing across the bP chemiresistor during this time is then measured and recorded wirelessly on a tablet at an interval of 0.3 s for 3 mins.
• the ambient temperature as well as the concentration levels, from the reference CO2 and the RH sensors 804, 806, respectively are also recorded, once at the start of the 3 -mins current measurement and then again at the end of the 3 -min measurement.
• the variations in the concentration levels, ambient temperatures and the measured current would then be passed on to the machine learning algorithm for training.
• Multiple set of data are collected at ambient condition across a wide range of CO2 concentration levels (e.g. 544 ppm to 1909 ppm).
• the senor is trained according to this example embodiment in ambient condition, where ambient means RH is present in the environment. Since H2O/RH is one of the gases the sensor is trained to measure, its relatively constant concentration level is being recorded to train the model to spot the difference in the conductivity between the situations when only CO2 is changing and when RH is changing. Both RH and CO2 can each cause a change in the conductivity of the chemiresistor. By varying only one component at a time and keeping the other(s) present but constant, one can train the sensor according to example embodiments to do concentration prediction for multiple gases/VOCs. This advantageously overcomes the difficulties in predicting multiple gases from a single overall incremental change in the conductivity of the chemiresistor when it is exposed under multiple sources of gases/VOCs.
• Ambient Type 2 Variation in CO2 and N2O.
• the experimental setup is as shown in Fig. 8.
• the valve of the gas inlet is closed and the C0 2 is allowed to diffuse gradually through the edges of the door on the setup.
• the current flowing across the bP chemiresistor during this time is then measured and recorded wirelessly on our tablet at an interval of 0.3 s for 3 mins.
• ambient temperature as well as the concentration levels from the reference N2O, CO2 and the RH meters, respectively, are also recorded, once at the start of the 3 -mins current measurement and then again at the end of the 3-min measurement.
• Multiple set of data are collected at ambient condition across a wide range of CO2 concentration levels (e.g. 27 ppm to 874 ppm) and N2O concentration levels (e.g. 19 ppm to 945 ppm).
• Fig. 9 shows the experimental setup for classifying VOCs emitted by plants.
• the aromatic plant is a Basil plant and it is known in the literature that they emit the following
• the gases that were actively monitored for change in the experiment according to this example embodiment are CO2 and RH only. It is noted that if it is desired to not only classify the ambient type, i.e. the presence of the emitted VOCs, but also to predict the concentration level of each individual VOC emitted by the Basil plant, one would additionally obtain the reference concentration value(s) of each VOCs for regression training. However, in this VOC detection example embodiment, the intention is to provide a proof of concept that example embodiments of the present invention can also be sensitive to VOCs.
• VOCs Similar to e.g. CO2 or RH, VOCs also consists of molecules in the gas state except one is organic in nature while the other is inorganic (i.e. CO2). Having demonstrated that an example embodiment responds to this type of vapour, the same ML technique can be applied, and a similar prediction accuracy can be expected, as will be appreciated by a person skilled in the art.
• Fig. 10 illustrates the process of training a single 2DM-based chemiresistor via machine learning (ML) for classifying and quantifying multiple sources of gases/VOCs that are present simultaneously in the same environment, according to an example embodiment.
• ML machine learning
• step 1 the training and testing data collection is performed as described above with reference to Figure 5.
• step 2 Fourier Transform is applied to the various collected time domain/series electrical responses of the 2DM-based chemiresistor to obtain the low frequency noise profile of the exposure and locate the Lorentzian components associated in it. From the noise profiles, all required input features are computed. Altogether 15 features were used according to the example embodiments described herein:
• the operating conditions such as the ambient temperature and run time. i.e. features 2 & 3 above
• the characteristic Lorentzian desorption time i.e. the inverse of the Lorentzian characteristic frequency
• feature 6 the characteristic Lorentzian desorption time (i.e. the inverse of the Lorentzian characteristic frequency), i.e. feature 6
• the Kurt, Skew, median of the Lorentzian noise spectral i.e. features 7, 8 & 9
• the power spectral density ratio of the characteristic Lorentzian frequency to the median of the Lorentzian noise spectral i.e. feature 10.
• step 3a using the measured channel resistance time series, their Lorentzian derivatives and the operating conditions as input features and all the associated type of ambient environments as target labels, train a classification model with any established ML algorithm (e.g. from scikit-lean) to predict the type of ambient environment from the input features. Repeat step 1 if the accuracy is not satisfactory.
• any established ML algorithm e.g. from scikit-lean
• step 3b once classification modelling is completed, categorize each set of input features according to their classification label (i.e. ambient type). Then train a regression model for every ambient type with any established ML algorithm using feature set with the same ambient type as inputs and their associated concentration levels as target values. The number of trained regression models should be equal to the number of ambient types. Repeat step 1 if the accuracy is not satisfactory.
• classification label i.e. ambient type
• the training methodology according to example embodiments of the present invention is very versatile as it depends on how the sensor would be used. E.g. if a user knows for certain the operating conditions would not experience any change in one gas/V OC, e.g. RH, then one can eliminate the variation cycle for RH in the training loop and still use the sensor. That is, the accuracy of the prediction is to be considered together with the associated ambient condition it is trained under. For example, a particular prediction accuracy can be valid for an ambient where CO2 is changing but RH and N2O are assumed to be constant, as shown in Figure 16, which will be described in more detail below.
• RH gas/V OC
• the training loop described with reference to Fig. 5 can be continued/completed to obtain a 3 -gases sensor that can operate under all operating conditions according to a preferred embodiment, be it whether 1, 2, or 3 gases are simultaneously changing or not.
• Fig. 11 shows the signal flow diagram illustrating prediction for Multi-gas/VOCs sensing according to an example embodiment.
• the 2DM chemiresistor is exposed in an environment with multiple sources of gas/VOCs.
• the channel resistance is recorded at on interval of 0.3 seconds for 3 minutes and the measured time series data is used to create the required input features (compare classification modelling described above with refence to Figure 10).
• the trained classification model is applied to determine the corresponding ambient type from the input features and reveal what types of gas/VOCs are present.
• the concentration level of each gas/VOCs type is then predicted by the respective trained regression models for the determined ambient type (compare regression modelling described above with refence to Figure 10).
• Fig. 12 describes briefly the system implementation block-diagram of an all-in-one gas sensor 900 according to an example embodiment as an air quality sensor in a standalone consumer product for tracking health or as a climate monitoring sensing node in a wireless sensor network for production control in a factory or industrial safety application.
• the computation of the output (indicated at block 902) can either be done on a mobile device(s) or via the Cloud for more automation and complex data analytic computation.
• block 904 indicates that the gases steal/donate electrons which in turn results in changes on the resistance, indicated as block 906.
• data representing the change in resistance is wirelessly transmitted to a mobile device(s)/the Cloud, indicated as block 902.
• Data representing the computed statistics and other rates of change are transmitted (e.g. wirelessly) to the A. I. model block 910 of the all-in-one gas sensor 900, for making a prediction of the gas type and concentration level, indicated at block 912.
• Fig. 13 shows the characteristic Lorentzian frequencies and associated statistical information (compare also feature list described above with reference to Fig. 10) extracted from 3 different ambient environments (namely type 0, 1, and 2 described above for an example embodiment) and can be seen as the first proof of principle for using characteristic Lorentzian frequencies to distinguish multiple gases from a single 2DM-based chemiresistor according to an example embodiment, in this case, RH, CO2, and N2O.
• Fig. 14 shows the characteristic Lorentzian frequencies and associated statistical information (compare also feature list described above with reference to Fig.
• SVR support vector machine regression
• the response time 1.5 mins.
• Fig. 17 the predicted N2O by support vector machine regression (data points e.g.) vs actual N2O (curve) are shown.
• the N2O training and testing was conducted while CO2 gas concentration is dropping at the same time albeit at a faster rate than N2O, at a relatively constant RH.
• the response time 1.5 mins.
• an all-in-one portable sensor can be provided for detecting air quality in the form of, by way of example, volatile gases, carbon dioxide, carbon monoxide, pollen, or toxins in the air, in addition to relative humidity, oxygen and nitrogen concentration.
• Data can be displayed in a software application installed on a mobile device or stationary terminal with actionable feeds at a higher intelligent level for the user to act upon according to various embodiments.
• new sensing capability can also be added to the all-in-one sensor according to various embodiments on demand in real time without any change in the hardware within various embodiments, with reinforcement learning (another type of machine learning technique), calibration of the sensor can either be eliminated or reduced in frequency.
• Fig. 18 summarizes the classification and regression results achieved from two different 2DM-based sensors according to example embodiments, one of which is based on black phosphorous (bP), Devi 1-bP and the other is based on a Tellurene (Te), DevOl-Te.
• the bP-based sensor was shown to be able to classify 3 different ambient environment with 3 gases, namely RH, CO2, and N2O, and also quantify their respective concentration levels in the environment, while the Te-based sensor is shown to be able to classify 3 different ambient environments, one of which has no plant in it but with CO2 rising and RH decreasing via normal diffusion through the gap of the door, one with a non-aromatic plant but with CO2 hovering around 595-615 ppm and RH hovering between 72-74 %, and lastly, one with an aromatic plant, a CO2 concentration that hovers around 605-615 ppm, and a RH that rises from 65% to a max of 76% before settling back to 72-73%.
• 3 gases namely RH, CO2, and N2O
• Figure 19 shows a flow chart 1900 illustrating a computerized method of determining respective presence and concentrations of multiple gases and/or volatile organic compounds, according to an example embodiment.
• one or more sensing elements with the same chemical and physical properties are exposed to the multiple gases and/or volatile organic compounds.
• electrical time series data of the one or more sensing elements is measured during the exposure.
• the electrical time series data and Lorentzian noise information of the electrical time series data is analyzed by an Artificial Intelligence (AI) system.
• AI Artificial Intelligence
• the respective presence and/or concentrations of the multiple gases and/or volatile organic compounds are determined based on the analysis of the electrical time series data and the Lorentzian noise information of the electrical time series data.
• the method may further comprise analyzing an ambient temperature by the Artificial Intelligence (AI) system and determining the respective presence and/or concentrations of the multiple gases and/or volatile organic compounds is further based on the analysis of the ambient temperature.
• AI Artificial Intelligence
• the Lorentzian information may comprise features selected from a group consisting of the characteristic Lorentzian peak with the maximum power spectral density, their respective power spectral density, the characteristic Lorentzian desorption time the Kurt, Skew, median of the Lorentzian noise spectral, the power spectral density ratio of the characteristic Lorentzian frequency to the median of the Lorentzian noise spectral, the numbers of Lorentzian peaks found in the Lorentzian noise spectral, the median of the Lorentzian peaks, the frequencies associated to the Lorentzian peaks, the full width half maximum and full width full maximum of the characteristic Lorentzian peak.
• the AI system may be a classification or regression model AI system or a reinforcement learning AI system.
• Each sensing element may comprise a two-dimensional sensing material.
• the two-dimensional sensing material may be configured as a chemiresistor and the electrical time series data may comprise resistance time series data.
• the two-dimensional sensing material may comprise one or a group consisting of black Phosphorous (bP), Tellurene, reduced graphene oxide, graphene, and Transition metal dichalcogenides, or any two-dimensional allotropes of various elements or compounds with a carrier mobilities that are comparable to black Phosphorous for low- power operation.
• FIG 20 shows a schematic diagram illustrating a sensor device 2000 capable of determining respective presence and concentrations of multiple gases and/or volatile organic compounds, according to an example embodiment.
• the sensor device 2000 comprises one or more sensing elements 2002 with the same chemical and physical properties and an Artificial Intelligence (AI) system 2004, wherein the AI system 2004 is configured to analyze the electrical time series data and Lorentzian noise information of electrical time series data and to determine the respective presence and/or concentrations of multiple gases and/or volatile organic compounds based on the analysis of the electrical time series data and the Lorentzian noise information of the electrical time series data.
• AI Artificial Intelligence
• the AI system 2004 may further configured to analyze an ambient temperature and to determine the respective presence and/or concentrations of multiple gases and/or volatile organic compounds further based on the analysis of the ambient temperature.
• the Lorentzian information may comprise features selected from a group consisting of the characteristic Lorentzian peak with the maximum power spectral density, their respective power spectral density, the characteristic Lorentzian desorption time the Kurt, Skew, median of the Lorentzian noise spectral, the power spectral density ratio of the characteristic Lorentzian frequency to the median of the Lorentzian noise spectral, the numbers of Lorentzian peaks found in the Lorentzian noise spectral, the median of the Lorentzian peaks, the frequencies associated to the Lorentzian peaks, the full width half maximum and full width full maximum of the characteristic Lorentzian peak.
• the AI system 2004 may be a classification or regression model AI system or a reinforcement learning AI system.
• Each sensing element may comprise a two-dimensional sensing material.
• the two-dimensional sensing material may be configured as a chemiresistor and the electrical time series data may comprise resistance time series data.
• the two-dimensional sensing material may comprise one or a group consisting of black Phosphorous (bP), Tellurene, reduced graphene oxide, graphene, and Transition metal dichalcogenides, or any two-dimensional allotropes of various elements or compounds with a carrier mobilities that are comparable to black Phosphorous for low- power operation.
• a method of training a sensor device of the embodiments described above with reference to Fig. 20 to be capable of determining respective presence and concentrations of multiple gases and/or volatile organic compounds is provided.
• the method may comprise data collections steps of i) exposing the one or more sensing elements to a desired number and type of gases and/or volatile organic compounds in a controlled environment and measuring a first dataset of the electrical time series data; ii) varying a concentration of one of the gases and/or volatile organic compounds and measuring a second dataset of the electrical time series data; and iii) repeating step ii) over a desired range of concentrations.
• the method may further comprise data collection steps of iv) varying respective concentrations of two of the gases and/or volatile organic compounds and measuring a further dataset of the electrical time series data; and v) repeating step iv) over a desired ranges of combinations of respective concentrations of the two of the gases and/or volatile organic compounds.
• the method may further comprise data collection steps of vi) repeating steps iv) and v), wherein in each repetition, an additional one of the gases and/or volatile organic compounds is added in steps iv) and v).
• the method may further comprise performing machine training on the datasets collected in the data collection steps.
• Performing the machine learning may comprise training a classification model to predict the number and type of gases and/or volatile organic compounds, and training a regression model to predict the respective concentrations of the gases and/or volatile organic compounds.
• Embodiments of the present invention can have one or more of the following features and associated benefits/advantages:
• Embodiments of the present invention have application as gas sensors, for example as an air quality sensor in a standalone consumer product for tracking health or as a climate monitoring sensing node in a wireless sensor network for production control in a factory or industrial safety application.
• a gas sensor is a device that can detect the presence and quantify the concentration of a specific gas in the atmosphere such as water vapor (humidity), organic vapors and hazardous gases. They are widely employed in environmental monitoring and emission control, personal and military safety, production control in agriculture and industry and medical diagnostics.
• the wireless sensor node is small (i.e. thumb-size) and can last more than, for example, 12 months on a 3V lithium button cell battery.
• the AI models developed for the sensor according to example embodiments can also be used to eliminate physical calibration requirement or reduce the calibration frequency, e.g. by using reinforcement learning.
• Computer-readable media in which such formatted data and/or instructions may be embodied include, but are not limited to, non-volatile storage media in various forms (e.g., optical, magnetic or semiconductor storage media) and carrier waves that may be used to transfer such formatted data and/or instructions through wireless, optical, or wired signaling media or any combination thereof.
• Examples of transfers of such formatted data and/or instructions by carrier waves include, but are not limited to, transfers (uploads, downloads, e-mail, etc.) over the internet and/or other computer networks via one or more data transfer protocols (e.g., HTTP, FTP, SMTP, etc.).
• data transfer protocols e.g., HTTP, FTP, SMTP, etc.
• a processing entity e.g., one or more processors
• aspects of the systems and methods described herein may be implemented as functionality programmed into any of a variety of circuitry, including programmable logic devices (PLDs), such as field programmable gate arrays (FPGAs), programmable array logic (PAF) devices, electrically programmable logic and memory devices and standard cell-based devices, as well as application specific integrated circuits (ASICs).
• PLDs programmable logic devices
• FPGAs field programmable gate arrays
• PAF programmable array logic
• ASICs application specific integrated circuits
• microcontrollers with memory such as electronically erasable programmable read only memory (EEPROM)
• embedded microprocessors firmware, software, etc.
• aspects of the system may be embodied in microprocessors having software -based circuit emulation, discrete logic (sequential and combinatorial), custom devices, fuzzy (neural) logic, quantum devices, and hybrids of any of the above device types.
• the underlying device technologies may be provided in a variety of component types, e.g., metal-oxide semiconductor field-effect transistor (MOSFET) technologies like complementary metal-oxide semiconductor (CMOS), bipolar technologies like emitter-coupled logic (ECL), polymer technologies (e.g., silicon-conjugated polymer and metal-conjugated polymer-metal structures), mixed analog and digital, etc.
• MOSFET metal-oxide semiconductor field-effect transistor
• CMOS complementary metal-oxide semiconductor
• bipolar technologies like emitter-coupled logic (ECL)
• polymer technologies e.g., silicon-conjugated polymer and metal-conjugated polymer-metal structures
• mixed analog and digital etc.
• the material is a two-dimensional material with high carrier mobility and large surface area-to- volume ratio, such as any two-dimensional allotropes of various elements or compounds with a carrier mobilities that are comparable to black Phosphorous for low-power operation.
• the terms used should not be construed to limit the systems and methods to the specific embodiments disclosed in the specification and the claims, but should be construed to include all processing systems that operate under the claims. Accordingly, the systems and methods are not limited by the disclosure, but instead the scope of the systems and methods is to be determined entirely by the claims.

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