Systems and methods for avoiding the influence of ozone on gas sensors

Abstract

Embodiments of the present disclosure relate to a system and method for avoiding the influence of ozone on a gas sensor. In some embodiments, a method of operating a gas sensor includes: setting the power of a heater in contact with an MOx sensor to provide a temperature below a threshold temperature; maintaining the temperature below the threshold temperature for a period of time to reduce the ozone concentration in a gas sample in contact with the MOx sensor; increasing the power of the heater to raise the temperature of the MOx sensor to an operating temperature; obtaining resistance data from the MOx sensor at the operating temperature; and processing the resistance data to provide a result related to the gas sample.

Description

[0001] Cross - Reference to Related Applications

[0002] This application claims the benefit of U.S. Provisional Application No. 62 / 952,841, filed on Dec. 23, 2019, which is incorporated herein by reference in its entirety. TECHNICAL FIELD

[0003] Embodiments of the present invention relate to position sensors and, in particular, to systems and methods for avoiding the effects of ozone in gas sensors. BACKGROUND OF THE INVENTION

[0004] Due to the widespread presence of volatile organic compounds (VOCs) and the consequent need to monitor air quality for these VOCs, measuring the presence of VOCs has become increasingly important. In particular, indoor air quality (IAQ) can be affected by a large number of air pollutants, including those from cooking, exhaust materials, scented candles, perfumes and other cosmetics, and waste gas emissions. Gas sensors can generally be used for indoor air quality (IAQ) monitoring or air quality monitoring, including estimated CO2 (eCO2) monitoring and total volatile organic compounds (TVOC).

[0005] MEMs-based sensors have been developed, which are expected to make gas sensors smaller in size, have fewer processes, and have embedded signal regulators for easy use and integration. Such gas sensors can allow gas sensing technology to be integrated into various devices, including mobile devices, wearable devices, and Internet of Things (IoT) devices, and do not require specialized experience to operate. These gas sensing technologies can include heater elements and metal oxide (MOx) chemoresistors. The circuit system controls the sensor temperature and measures the MOx conductivity, which is a function of the gas concentration.

[0006] Typically, the MOx resistor is heated to the operating temperature. The resistance of the MOx resistor is sensitive to the concentration of impurity gases in the air. Depending on the baseline resistance change, based on the gas type and concentration, it can increase or decrease depending on the type of MOx material used and the type of impurity gas present. For example, the resistance range increases significantly in the presence of oxidizing gases such as ozone, while it decreases when exposed to VOCs. During an ozone event, the increase in resistance can lead to: competing reactions for the desired VOC response, or cause baseline changes, false alarms during ozone events (IAQ / TVOC), and incorrect algorithm reports after ozone (IAQ / TVOC / eCO2). For example, an ozone event may occur during air exchange with outdoor air rich in ozone.

[0007] Therefore, there is a need to develop better sensing designs for gas sensors that are more resistant to ozone events. Summary of the Invention

[0008] In some embodiments, a method of operating a gas sensor includes: setting a power of a heater in contact with a MOx sensor to provide a temperature below a threshold temperature; maintaining the temperature below the threshold temperature for a period of time to reduce an ozone concentration in a gas sample in contact with the MOx sensor; increasing the power of the heater to raise the temperature of the MOx sensor to an operating temperature; obtaining resistance data from the MOx sensor at the operating temperature; and processing the resistance data to provide a result related to the gas sample.

[0009] In some embodiments, a gas sensor includes: a MOx sensor; a heater in contact with the MOx sensor; a heater driver configured to control the heater; and a processor configured to receive resistance data from the MOx sensor, control the heater driver to control the temperature of the MOx sensor, and further configured to execute instructions to: set a power of the heater in contact with the MOx sensor to provide a temperature below a threshold temperature; maintain the temperature below the threshold temperature for a period of time to reduce an ozone concentration in a gas sample to below a threshold concentration; increase the power of the heater to raise the temperature to the operating temperature; obtain resistance data from the MOx sensor at the operating temperature; decrease the power of the heater to lower the temperature below the threshold temperature; and process the resistance data to provide a result related to the gas sample.

[0010] These and other embodiments are discussed below with reference to the following figures. Brief Description of the Drawings

[0011] Figures 1A to 1C Aspects of a gas sensor on which embodiments of the present disclosure may be implemented are shown;

[0012] Figure 2 An example process for operating a gas sensor in accordance with aspects of the present disclosure is shown;

[0013] Figure 3A A conventional temperature profile for operation of a gas sensor is shown;

[0014] Figure 3B and Figure 3C A temperature profile in accordance with aspects of the present disclosure is shown;

[0015] Figure 4 A test system for testing a gas sensor such as Figures 1A to 1C shown is shown;

[0016] Figure 5 Resistance measurements performed in a gas sensor using a standard operation such as Figure 3A shown are shown;

[0017] Figure 6 shows the air quality measurement performed in a gas sensor using an existing algorithm such as Figure 3A shown under standard operation;

[0018] Figure 7 shows the air quality measurement using an algorithm according to an embodiment of the present invention;

[0019] Figure 8A and Figure 8B shows the resistance measurement of the gas sensor when ozone is introduced with and without an arithmetic algorithm according to the present disclosure;

[0020] Figure 9A and 9B shows the ozone reaction in air quality measurement with and without an arithmetic algorithm according to some embodiments of the present disclosure;

[0021] Figures 10A to 10C shows the operation of the gas sensor using a low-temperature algorithm during the presence of ozone and VOC according to some embodiments of the present disclosure;

[0022] Figure 11 shows the change of resistance over time in the presence of various ozone concentrations and other gases;

[0023] Figure 12 shows the operation according to an embodiment of the present invention; and

[0024] Figure 13 shows the sensitivity to ozone when using an embodiment of the present invention compared to a standard algorithm.

[0025] These and other aspects of embodiments of the present invention are further discussed below. Detailed Description

[0026] In the following description, specific details of some embodiments of the present invention are set forth. However, it will be apparent to those skilled in the art that some embodiments may be practiced without some or all of these specific details. The specific embodiments disclosed herein are intended to be illustrative and not limiting. Those skilled in the art may recognize other elements (although not specifically described herein) within the scope and spirit of the present disclosure.

[0027] This description shows aspects of the invention, and the embodiments should not be considered restrictive, as the claims define the protected invention. Various changes may be made without departing from the spirit and scope of this specification and the claims. In some cases, well-known structures and techniques are not shown or described in detail so as not to obscure the present invention.

[0028] According to embodiments of the present invention, sequences with a low-temperature step are provided to allow ozone reaction, while other gases do not react at that temperature. As a result, this low-temperature step can greatly reduce the interference with gas concentration measurement due to the influence of ozone concentration. The process according to the present invention is advantageous because actual measurements can be made without additional filtration, or an algorithm can be utilized that can adjust the signal based on the last reasonable resistance value, ignoring the high resistance caused by the presence of ozone.

[0029] Figure 1A FIG. shows a gas sensor 100 on which aspects of the present disclosure can be implemented. As Figure 1A shown, the gas sensor 100 includes a metal oxide (MOx) sensor 102 thermally coupled to a heater 104. As described above, the conductivity of the MOx sensor 102 is sensitive to the presence of various gases, particularly volatile organic compounds (VOCs). VOCs refer to a series of chemical substances with high vapor pressure and low water solubility that are typically generated by manufacturing, combustion, degassing, or cleaning materials. These VOCs often pose health risks, and thus, there is great interest in monitoring VOCs to provide measurements of air quality. VOCs include, for example, industrial solvents, fuel oxygenates, chlorination by-products in water treatment, and are components of petroleum fuels, hydraulic oils, paint thinners, dry cleaners, and other such products. As described above, the conductivity of the MOx sensor 102 depends on the presence of these materials in the sampled gas. Thus, the gas sensor 100 including the MOx sensor 102 can be configured to detect total VOC (TVOC), can be used to estimate CO2, and can be used to monitor indoor air quality (IAQ).

[0030] However, the operation of the MOx sensor 102 is affected by the presence of oxidizing gases, including the presence of ozone. The presence of ozone affects the detection baseline and accuracy of VOCs in an air sample. Aspects of the present disclosure provide a gas sensor 100 that can address the adverse aspects of any ozone that may be present in the sampled gas.

[0031] As Figure 1A shown, the MOx sensor 102 is mounted on the heater 104, and the heater 104 controls the operating temperature of the MOx sensor 102. The heater 104 can be formed, for example, by silicon micromachining to form a robust thermally isolated microhotplate (MHP) on which the MOx sensor 102 is formed. The operating temperature T can vary over a wide range and affects the sensitivity of the MOx sensor 102. In some embodiments, the operating temperature can vary, for example, between 100 °C and 500 °C.

[0032] As Figure 1AFurther shown, heater 104 and MOx sensor 102 are coupled to controller 106. Controller 106 includes a combination of analog and digital circuitry to monitor the conductivity of MOx sensor 102 while controlling the temperature of MOx sensor 102 through controller 106. Controller 106 may include a memory and a processor that processes data received from MOx sensor 102. Additionally, controller 106 may provide the processed data to a post-processor 108 coupled to gas sensor 100. As described below, controller 106 includes a memory and a processor that is capable of operating the algorithms discussed herein. Post-processor 108 receives the processed data from controller 106. Post-processor 108 may also provide data and instructions that may be executed within controller 106 to control heater 104 and measure and process signals from MOx sensor 102.

[0033] As Figure 1A Further shown, power supply 110 provides power to post-processor 108 and gas sensor 100. In some embodiments, gas sensor 100, post-processor 108, and power supply 110 may be mounted on a single printed circuit board. However, in some embodiments, these components may be separated.

[0034] Figure 1B Gas sensor 100 is shown in more detail. As Figure 1B shown in the example shown, controller 106 of gas sensor 100 includes a converter 114, a heater driver 116, an analog-to-digital converter 118, a processor 122, a memory 124, and a communication interface 128. Converter 114 determines the conductivity of MOx sensor 102 and provides a voltage V that indicates the resistance of MOx sensor 102. In some embodiments, the resistance R may be determined by providing a specific current through MOx sensor 102 and measuring the voltage V across MOx sensor 102.

[0035] Heater driver 116 is coupled to control heater 104. Heater driver 116 is driven by processor 122 to control heater 104 to control the temperature of MOx sensor 102. In some embodiments, a temperature sensor 112 may be provided to determine the temperature T of MOx sensor 102. In some embodiments, the temperature T of MOx sensor 102 may be determined by the power supplied to heater 104, and there may be a determined relationship between the power and the temperature T of MOx sensor 102.

[0036] The analog-to-digital converter (ADC) 118 of the controller 106 digitizes signals for input to the processor 122. In some embodiments, the ADC 118 may include multiple ADCs to digitize various signals. The ADC 118 may include an ADC of any resolution suitable for the sensitivity of the gas sensor 100. In Figure 1B the example shown, the signals are input to the multiplexer 120, and the processor 122 selects the signals to be digitized in the ADC 118. As Figure 1B shown, the voltage indicating the resistance of the MOx sensor 102 from the converter 114, the temperature T from the temperature sensor 112, and the power indication P from the heater driver 116 may be input to the multiplexer 120 for digitization by the ADC 118 and input to the processor 122.

[0037] The processor 122 may include any microcontroller, microcomputer, microprocessor, or other digital processing circuit capable of executing the instructions described herein. The processor 122 is coupled to the memory 124. The memory 124 includes volatile and non-volatile memory sufficient to store the data and instructions to be executed by the processor 122. The processor 122 is coupled to select the signals to be digitized in the multiplexer 120, is coupled to receive the digitized signals from the ADC 118, and is further coupled to control the power of the heater 104. In some embodiments, the processor 122 may include a combination of a digital processor as described above and an analog circuit as described below to perform calculations according to some aspects of the present disclosure.

[0038] As Figure 1B further shown, the processor 122 may be coupled to the interface 128. The interface 128 may include any protocol such as I2C, GPIO, USB, etc., or other protocols that utilize external devices such as Figure 1A the post-processor 108 shown for transmitting and receiving digital data.

[0039] As Figure 1B further shown, the power block 126 receives the voltage Vdd and provides power supply for the gas sensor 100. In some embodiments, the gas sensor 100 may be formed on a single integrated circuit (IC). In some embodiments, the gas sensor 100 may be discrete components.

[0040] In some embodiments in accordance with the present disclosure, neural network 142 may be implemented in processor 122 to process data to provide results related to the gas sample at MOx sensor 102. Preprocessing 140 may receive parameters such as a voltage signal V indicative of the resistance of MOx sensor 102 and an indication of the operating temperature T of MOx sensor 102, and process these parameters to provide gas-related results such as TVOC concentration, estimated CO2 concentration, indoor air quality, and even individual VOC concentrations. Figure 1C An example of a processor 122 with neural network processing is shown. As Figure 1C shown, processor 122 may execute instructions to provide preprocessing 140, which receives raw data and preprocesses the data (e.g., provides digital filtering, initial calculations, or other processing) to provide the data to AI processing or neural network 142. As Figure 1C shown, the AI processing may be neural network 142. The main components of neural network 142 are input nodes 148 that receive data from preprocessing 140, nodes in hidden layer 146 where the data is processed by successively applying weighted functions to process the data and pass the data to the next layer. Finally, output node 150 provides the results. The weighted parameters for each function executed in each node may be set by a training algorithm that depends on a training data set. Memory 124 is large enough to store the weighted parameters for the operation of neural network 142. In some cases, neural network 142 may be at least partially trained with a supervised learning method, where gas sensor 100 processes a set of known gas samples to set the weighted parameters. In either case, neural network 142 can provide more reliable results for the gas-related results output by gas sensor 100.

[0041] Neural network 142 may be implemented digitally in processor 122 or may be implemented in an analog circuit in processor 122. In some embodiments, the weighting factors to each node may be digitized, and the nodes of neural network 142 may be implemented by an analog circuit. Whether the processing in each node is performed digitally or by an analog circuit, the weighting factors are stored in memory 124 and coupled to input node 148, each node of hidden layer 146, and output node 150.

[0042] As described above, through heater driver 116, processor 122 controls the temperature of MOx sensor 102 by controlling heater 104. According to an embodiment of the present disclosure, the temperature on MOx sensor 102 is controlled to have a low temperature portion where the temperature of MOx sensor 102 is held at a temperature below a threshold temperature for a period of time sufficient for ozone to react and reduce the ozone concentration to below a threshold concentration, and then the temperature is raised to the operating temperature.

[0043] The resistance of the MOx sensor 102 can generally be modeled as a function of the gas concentration at the sensor. In particular, the resistance of the MOx sensor 102 can follow a power-law behavior as shown by the following equation:

[0044] R MOx =Ac -α

[0045] where R MOx is the resistance of the MOx sensor 102, c is the gas impurity concentration, and A and α are material-related parameters. The sensitivity of the resistance of the MOx to various VOC concentrations can depend on the operating temperature of the MOx sensor 102. IAQ determination can be made based on the gas concentration, for example, by mapping the concentration of the VOC to a specific IAQ value.

[0046] Figure 2 FIG. shows a process 200 for operating a gas sensor 100 according to some embodiments of the present disclosure. As Figure 2 shown, the process 200 begins at step 202, in which the temperature of the MOx sensor 102 has been set to a low temperature. The low temperature is a temperature below a threshold temperature such that the ozone reaction reduces the ozone concentration in the gas in contact with the MOx sensor 102. In some embodiments, the threshold temperature can be between about 15°C and 150°C, although higher or lower threshold temperatures can be used in some embodiments. The threshold temperature is set low enough to effectively reduce the ozone concentration in the gas sample exposed to the MOx sensor 102, for example, to less than 100°C. As described above, the low temperature is obtained by appropriately setting the power of the heater 104 to achieve a low temperature at the MOx sensor 102. In some embodiments, the temperature of the MOx sensor 102 can be monitored separately, for example, using a temperature sensor 112. In some embodiments, the temperature of the MOx sensor 102 can be calibrated using the power supplied to the heater 104 such that the temperature can be known based on the power setting from the heater driver 116.

[0047] In step 204, the low temperature is maintained for a period of time sufficient to reduce the ozone concentration in the gas in contact with the MOx sensor 102. In some embodiments, the period of time is sufficient to reduce the ozone concentration below a threshold concentration. In some embodiments, the time is sufficient to reduce the ozone concentration below a threshold concentration (e.g., 10 ppb). In some embodiments, the time is sufficient to reduce the ozone concentration by a specific percentage amount. In some embodiments, the period of time can be between 0.1 second and 100 seconds, for example, 4 seconds.

[0048] In step 206, the temperature of the MOx sensor 102 is raised to the operating temperature. In some embodiments, the operating temperature can be between, for example, 100 °C and 500 °C. In some embodiments, a single operating temperature is used. In some embodiments, data over a series of operating temperatures is acquired as the temperature of the MOx sensor 102 traverses the data collection range.

[0049] In step 208, the operating temperature and resistance data of the MOx sensor 102 are acquired. In some embodiments, multiple data points are acquired during the data acquisition step 208. In some embodiments, a single or a small number of data points can be acquired. In step 210, after the resistance data has been acquired, the temperature can be lowered to a low temperature as described above. In step 212, the resistance data is processed to provide a result related to the gas sample. The result can include one or more of a TVOC determination, an eCO2 determination, or an IAC determination. As described above, in some embodiments, the result can be obtained by being processed by AI as Figure 1C shown.

[0050] In steps 204, 206, 208, and 210, process 200 can follow a temperature profile that controls the temperature at the MOx sensor 102. According to an embodiment of the present disclosure, the temperature profile followed in steps 204, 206, 208, and 210 includes a low-temperature period during which the ozone concentration of the gas sample measured at the MOx sensor 102 is reduced.

[0051] Figure 3A A conventional temperature profile 300 for the operation of the gas sensor 100 is shown. In this case, the temperature of the MOx sensor 102 starts at room temperature RT where the gas sensor 100 is off. Then the temperature T is raised to the operating temperature TO and remains constant throughout the operation of the gas sensor 100. When the gas sensor 100 is turned off, the temperature returns to room temperature. Using the temperature profile 300 allows the ozone concentration to affect the resulting measurement.

[0052] Figure 3B A temperature profile 302 according to some embodiments of the present disclosure is shown. The temperature profile 302 is generally referred to as a low-temperature algorithm. As Figure 3B shown, the gas sensor 100 repeatedly cycles between a low temperature TL and an operating temperature TO. As Figure 3BAs shown by the temperature profile 302 therein, during a data acquisition cycle, at the first time t1 of the data collection period 308, the temperature of the MOx sensor 102 rises to the operating temperature TO with a temperature rise 306. The data collection period 308 is between time t1 and t2, and resistance data from the MOx sensor 102 is acquired between time t1 and t2. As described above, the operating temperature TO can be set, for example, between 100 °C and 500 °C. At time t2, the temperature of the MOx sensor 102 decreases to the low temperature TL with the profile period 310. In some embodiments, this can be achieved by simply turning off the power of the heater 104 and allowing the MOx sensor 102 to cool until the low temperature TL is reached. As described above, TL can be any temperature below the threshold temperature TH, below which the ozone concentration is reduced. At the low temperature TL, ozone reacts and the ozone concentration in the gas sample in contact with the MOx sensor 102 decreases. During the period 304 between time t3 and t4, the low temperature TL is maintained. The period 304 has sufficient time to allow the ozone concentration to decrease, for example, to a level below the threshold ozone concentration or to decrease by a specific percentage amount. At time t4, at the end of the period 304, a new data acquisition cycle is started by raising the temperature of the MOx sensor 102 to the operating temperature TO.

[0053] Figure 3C shows another temperature profile 320 according to some embodiments. As Figure 3C shown, during the period 322, the temperature of the MOx sensor 102 is maintained at the low temperature TL below the threshold temperature TH, as described above. At the start of the data collection group, at t1, the temperature of the MOx sensor 102 rises through the data collection range 328 during the period 324. Specifically in the temperature profile 320, during the period 322, the temperature of the MOx sensor 102 is maintained at the temperature TL. At time t1, the data collection cycle starts with the heating period 324. When the temperature of the MOx sensor 102 reaches the low operating temperature TOL, the data collection range 328 ends. In step 208, resistance data and temperature data are acquired at multiple temperatures, while the operating temperature TO rises from TOL at time t2 to the high operating temperature TOH at time t3. After traversing the data collection range 328. As Figure 3C shown, when the MOx sensor 102 cools between time t4 and t5, the temperature of the MOx sensor 102 traverses the data collection range 328 again. The gas sensor 100 can also acquire other data points at multiple temperatures during the time t4 to t5. At time t6, the temperature of the MOx sensor 102 cools again to the low temperature TL in order to reduce the ozone concentration for the next data collection cycle.

[0054] As shown in the figure, Figure 3B the temperature profiles 302 shown and Figure 3CThe temperature profile 320 shown cycles periodically such that the air in contact with the MOx sensor 102 is sampled periodically. In some embodiments, the period of the temperature profile 302 and the temperature profile 320 can be between 0.1 seconds and 100 seconds, such as 1 second to 100 seconds. In certain cases, it includes a longer low-temperature phase and a shorter data acquisition phase. For example, in some embodiments, the temperature profile includes 90 seconds of low temperature, with 1 second out of a 91-second period for data acquisition. The periodicity and temperature combination can provide a low-temperature phase of any length, followed by a data acquisition phase. In some embodiments, using Figure 3C the temperature profile 320 shown, the resistance-versus-temperature data obtained within the data collection region 328 can allow determination of the individual gas concentrations in the air sample. Otherwise, better calculations for IAQ, TVOC, or eCO2 can be obtained as described above.

[0055] In the following description, gas sensors are tested to demonstrate aspects of the present disclosure. Generally, the gas sensor 100 can be housed in a measurement chamber, and gases with various ozone concentrations and VOC concentrations can be input. The gas concentrations can be determined by the gas sensor 100 using a conventional temperature profile and a temperature profile according to embodiments of the present disclosure. The gas concentrations can be determined by other means.

[0056] Figure 4 A test system 400 used to test a gas sensor 100 such as that shown in FIG. 1 is shown. A clean air generator 402 can provide a source of clean air. An ozone generator 404 can provide a source of ozone. A gas mixing unit 406 can supply other gases, such as VOCs, to the gas mixture. The mixed gas can be supplied to a gas measurement chamber 408 in which the gas sensor 100 is disposed. Then, the gas can be supplied to a Fourier transform infrared spectrometer 410, which can provide a highly accurate measurement of the gas concentration. A processing unit 412, which can be a computer system, receives the processed data from the gas sensor 100 in the gas measurement chamber 408 and receives the accurate gas concentration data from the FTIR instrument 410. Additionally, in some embodiments, the processing unit 412 can control the operation of the clean air generator 402, the ozone generator 404, and the gas mixing unit 406 to provide testing of the gas sensor 100. Figure 4 The test system 400 shown can be used to supply gases to test the response of the gas sensor 100 under conventional operation and processes according to embodiments of the present disclosure.

[0057] Figure 5 Shown is in Figure 3AThe resistance measurement in the gas sensor 100 placed in the gas measurement chamber 408 of the test system 400 under standard operation as shown by the temperature profile. In particular, Figure 5 shows the measured resistance of the MOx sensor 102 as a function of time 500: there is clean air generated by the clean air generator 402 (region 502), and there is 70 ppb ozone added by the ozone generator 404 (region 504), and 170 ppb ozone added by the ozone generator 404 (region 506). As Figure 5 shown, when 70 ppb of ozone is added in region 504, the resistance of the MOx sensor 102 increases by up to three orders of magnitude, and when the ozone concentration in region 506 increases to 170 ppb, the resistance increases by up to four orders of magnitude.

[0058] Figure 6 shows the air quality measurement of the gas sensor 100 as a function of time when tested in the gas measurement chamber 408 of the test system 400 under standard operation with the temperature profile as Figure 3A shown. As shown, as a function of time, the IAQ rises from 1 in region 602 in the presence of clean air to 4 in region 604 in the presence of ozone.

[0059] Figure 7 shows the air quality measurement varying with time in the gas sensor 100 when the algorithm according to an embodiment of the present invention is tested in the gas measurement chamber 408 of the test system 400 under a temperature profile such as Figure 3B or as shown in 3C. As shown, the air quality measurement at an IAQ of 1 remains unchanged even in the presence of ozone, which is injected during the test.

[0060] Figure 8A shows the variation of the resistance of the gas sensor 100 in the gas measurement chamber 408 of the test system 400 with time when using low - power operation with and without ozone. As shown, in region 802, clean air enters the gas measurement chamber 408, and in region 804, ozone is mixed with the clean air and enters the gas measurement chamber 408. As a result of the presence of ozone, the resistance of the MOx sensor 102 increases, thus affecting the operation of the gas sensor 100, as described above.

[0061] Figure 8B shows the operation results according to aspects of the present disclosure. Figure 8B shows the temperature profile according to aspects of the present disclosure using a low - temperature step (e.g., Figure 3BThe function of the resistance of gas sensor 100 in gas measurement chamber 408 of test system 400 over time (or the temperature profile shown in 3C). As shown, trace 806 shows the pre-measurement step at low temperature, where ozone can react in the low-power early stage. Trace 808 shows the measurement step after the pre-measurement step.

[0062] Figure 9A and 9B show the air quality measurements performed by gas sensor 100 in gas measurement chamber 408 of test system 400 using a standard low-power algorithm (e.g., having a temperature profile such as Figure 3A shown) and an algorithm according to an embodiment of the present disclosure (e.g., having a temperature profile such as Figure 3B or as shown in 3C). As shown, in Figure 9A , the existing algorithm results in cancellation of the air quality measurement, while the algorithm according to the present invention as Figure 9B shown does not show such cancellation.

[0063] Figure 10A , 10B and 10C further show tests of gas sensor 100 using a low-power algorithm according to some embodiments such as Figure 3B shown. Gas sensor 100 is placed in gas measurement chamber 408 of test system 400, and clean air, ozone, and VOC can be provided in gas measurement chamber 408. Figure 10A shows the change in the resistance of MOx sensor 102 over time. Figure 10B shows the change in TVOC concentration over time. Figure 10C shows the change in IAQ over time. In each of Figure 10A , 10B and 10B, clean air, clean air with 8 ppm ethanol, clean air with ozone, and clean air with a mixture of ozone and 8 ppm ethanol are provided. In particular, in the first time period 1002, clean air is provided to gas sensor 100; and in time period 1004, clean air with 8 ppm ethanol is provided to gas sensor 100; in time period 1006, clean air is provided to gas sensor 100; in time period 1008, clean air with 70 ppb ozone is provided to gas sensor 100; in time period 1010, clean air with 70 ppb ozone and 8 ppm ethanol is provided to gas sensor 100; in time period 1012, clean air and 70 ppb ozone are provided to gas sensor 100; in time period 1014, clean air is provided to gas sensor 100. Compared with the conventional algorithm, the influence of ozone on the resulting measurement can be reduced.

[0064] Figure 10A shows the change of resistance over time during low-power operation in the presence and absence of ozone and ethanol as described above. Figure 3B As shown, in the presence of 8 ppm ethanol, the resistance decreases during period 1004. During period 1006, the resistance returns to the resistance level shown in period 1002. During period 1008, with the introduction of ozone, the resistance increases. When ethanol is provided again during period 1010, the resistance decreases again. During period 1012, when ethanol is removed, the resistance returns to the resistance of period 1008. When ozone is removed during period 1014, the resistance returns to the resistance level provided in period 1002. The following table provides the sensitivity to ethanol of a plurality of gas sensors such as gas sensor 100 in the presence and absence of ozone. These measurements show that the presence of ozone provides a constant resistance compensation in the gas sensor.

[0065]

[0066]

[0067] Figure 10B shows the TVOC detection during low-power operation in the presence and absence of ozone during the same period as described above, such as Figure 3B shown. As Figure 10B shown, the TVOC measurement is about 4 mg / m3 during period 1002 and increases to 10 - 12 during period 1004. The TVOC concentration begins to recover to 4 during period 1006, but significantly decreases during period 1008 when ozone is introduced. During period 1010, with the introduction of ethanol, the TVOC measurement increases again, and during period 1012, the TVOC measurement returns to near 0. During period 1014, when ozone is turned off again, the concentration returns to 4. The following table provides the measured ethanol sensitivity of many different gas sensors such as gas sensor 100 in the presence or absence of ozone. It can be seen that ozone causes false negatives for ethanol concentration, which may be due to the reduction of VOC.

[0068]

[0069] Figure 10C shows another TVOC detection using low-power operation in the presence and absence of ozone according to some embodiments. The ethanol and ozone concentrations in periods 1002, 1004, 1006, 1008, 1010, 1012, and 1014 are defined above. The following table provides the measured ethanol sensitivity in the presence and absence of ozone. Again, this shows that ozone causes false negatives.

[0070]

[0071]

[0072] Figure 11 shows the change in resistance over time under various ozone concentrations and with the addition of ethanol. In particular, in Figure 11 , during period 1102, 0.5 ppm of ethanol was introduced into the gas measurement chamber 408 where the gas sensor 100 was placed. Conventional cryogenic treatment was performed on the gas sensor 100 using neural network processing. In period 1104 that overlaps with period 1102, 80 ppb of ozone was introduced. In period 1106 that overlaps with period 1102, 100 ppb of ozone was introduced. In period 1108 that overlaps with period 1102, 200 ppb of ozone was introduced. In period 1110 after period 1102, 200 ppb of ozone was introduced. This illustrates the function of the neural network sequence, which helps to mitigate the impact of ozone. As shown, the resistance decreases during the TVOC concentration. In addition, the presence of ozone provides cancellation (even lower), but TVOC can still be detected in each case. However, due to the presence of ozone, there is still resistance cancellation.

[0073] Figure 12 shows the operation according to an embodiment of the present invention. Specifically outlines the ozone response in resistance during standard operation 1202, neural network operation method 1204, and low-power operation method 1206 according to some embodiments. As shown, according to some embodiments, both using neural network processing and applying a low-power algorithm reduce the impact of ozone on the operation of the gas sensor 100.

[0074] Figure 13 shows the sensitivity to ozone of an embodiment of the present invention compared to a standard algorithm.

[0075] In summary, during an ozone event, the standard operation method results in a large increase in the MOx resistance. However, embodiments of the new algorithm according to the present invention allow control of the baseline during an ozone event, resulting in more accurate TVOC readings after the ozone event. In addition, VOC can still be detected during an ozone event (if present). These embodiments provide correct algorithm outputs for IAQ, TVOC, and eCO2 measurements after an ozone event.

[0076] Thus, as described above, the lower temperature steps in the sequence will result in ozone reactions, thereby reducing the amount of ozone present in the gas sample. For a low-temperature process, a sequence including a period of low-temperature operation can be provided instead of operating the gas sensor at a constant operating temperature. For example, a typical MOx gas sensor operates between 250 °C and 350 °C. In the low-temperature portion of the sequence, the temperature can be reduced to approximately ambient temperature (e.g., approximately 150 °C) for a period sufficient to substantially complete the reaction.

[0077] In some embodiments, a neural network approach can be used to further refine the sequence. The neural network temperature profile can be used to affect the present invention. The baseline (highest resistance in clean air) can be limited to low megaohm values, and a damping filter can be used to check for the presence of ozone. When ozone is present, the metal oxide resistance will immediately rise from kiloohms to megaohms and gigaohms. By this method, ozone events can be filtered to provide a more accurate reading after an ozone event.

[0078] The above detailed description is provided to illustrate specific embodiments of the present invention and is not intended to be limiting. Many variations and modifications within the scope of the present invention are possible. The present invention is set forth in the appended claims.

Claims

1. A method of operating a gas sensor, comprising: Setting the power of a heater in contact with a MOx sensor to provide a low temperature below a threshold temperature, below which ozone concentration decreases; Maintaining the temperature at the low temperature below the threshold temperature for a period of time to reduce the ozone concentration in a gas sample in contact with the MOx sensor; Increasing the power of the heater to raise the temperature of the MOx sensor to an operating temperature; Obtaining resistance data from the MOx sensor at the operating temperature; and Processing the resistance data to provide a result related to the gas sample.

2. The method according to claim 1, wherein the threshold temperature is between 15 °C and 50 °C.

3. The method according to claim 1, wherein the period of time is between 0.1 second and 100 seconds.

4. The method according to claim 1, wherein the operating temperature is a single temperature between 100 °C and 500 °C, and obtaining resistance data includes measuring one or more data points at the single temperature.

5. The method according to claim 1, wherein the operating temperature is a temperature range between a low operating temperature and a high operating temperature, and obtaining resistance data includes measuring one or more data points while the operating temperature is within the range.

6. The method according to claim 5, wherein obtaining resistance data includes measuring the one or more data points while the temperature is rising and while the temperature is falling.

7. The method according to claim 1, wherein the result is one or more of the following: the concentration of volatile organic compounds (VOC), the concentration of total volatile organic compounds (TVOC), the concentration of carbon dioxide (eCO2), and indoor air quality (IAQ).

8. The method according to claim 7, wherein processing the resistance data includes processing the result through a neural network to provide a conditioned result.

9. The method according to claim 8, further comprising providing the conditioned result to an external device.

10. The method according to claim 1, wherein the influence of ozone on the result is reduced.

11. A gas sensor, comprising: A MOx sensor; A heater in contact with the MOx sensor; A heater driver configured to control the heater; And A processor configured to receive resistance data from the MOx sensor, control the heater driver to control the temperature of the MOx sensor, and further configured to execute instructions to: Set the power of the heater in contact with the MOx sensor to provide a low temperature below a threshold temperature, below which ozone concentration decreases; Maintain the temperature at the low temperature below the threshold temperature for a period of time sufficient to reduce the ozone concentration in the gas sample to below a threshold concentration; Increase the power of the heater to raise the temperature to the operating temperature; Obtain resistance data from the MOx sensor, wherein the temperature is at the operating temperature; Reduce the power of the heater to lower the temperature to the low temperature below the threshold temperature; and Process the resistance data to provide a result related to the gas sample.

12. The gas sensor according to claim 11, wherein the threshold temperature is between 15°C and 150°C.

13. The gas sensor according to claim 11, wherein the period of time is between 0.1 second and 100 seconds.

14. The gas sensor according to claim 11, wherein the operating temperature is a single temperature between 100°C and 500°C, and obtaining the resistance data includes measuring one or more data points at the single temperature.

15. The gas sensor according to claim 11, wherein the operating temperature is a temperature range between a low operating temperature and a high operating temperature, and obtaining the resistance data includes measuring one or more data points while the operating temperature is within the range.

16. The gas sensor according to claim 15, wherein obtaining the resistance data includes measuring the one or more data points while the temperature is increasing and while the temperature is decreasing.

17. The gas sensor according to claim 11, wherein the result is one or more of the following: concentration of volatile organic compound VOC, concentration of total volatile organic compound TVOC, concentration of carbon dioxide eCO2, and indoor air quality IAQ.

18. The gas sensor according to claim 17, wherein processing the resistance data includes processing the result through a neural network to provide a conditioned result.

19. The gas sensor according to claim 18, further comprising providing the conditioned result to an external device.

20. The gas sensor according to claim 11, wherein the influence of ozone on the result is reduced.

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