US20200064291A1

US20200064291A1 - Pattern recognition algorithm for identifying and quantifying single and mixed contaminants in air with an array of nanomaterial-based gas sensors

Info

Publication number: US20200064291A1
Application number: US16/547,498
Authority: US
Inventors: Alexey Varganov; Sundip R. DOSHI
Original assignee: Aernos Inc
Current assignee: Aernos Inc
Priority date: 2018-08-22
Filing date: 2019-08-21
Publication date: 2020-02-27
Also published as: CA3110472A1; KR20210035322A; WO2020041642A1; EP3841376A1; US20200064324A1; CN112789500A; JP2021534432A; US20200064321A1; AU2019325607A1; US20200064293A1; US11215594B2; EP3841376A4; US20210247368A1; US20200064294A1; US20200064290A1; US20200064323A1

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.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent Application No. 62/721,289, filed Aug. 22, 2018, U.S. Provisional Patent Application No. 62/721,293, filed Aug. 22, 2018, U.S. Provisional Patent Application No. 62/721,296, filed Aug. 22, 2018, U.S. Provisional Application No. 62/721,302, filed Aug. 22, 2018, U.S. Provisional Patent Application No. 62/721,306, filed Aug. 22, 2018, U.S. Provisional Patent Application No. 62/721,309, filed Aug. 22, 2018, U.S. Provisional Application No. 62/721,311, filed Aug. 22, 2018, U.S. Provisional Patent Application No. 62/799,466, filed Jan. 31, 2019, the contents of which are incorporated herein by reference.

BACKGROUND

1. Technical Field

The embodiments described herein relate generally to systems and methods for measuring an analyte gas and mixtures in air, and more particularly, to systems and methods for simultaneous gas mixture concentrations measurement with an array of nanomaterial-based gas sensors.

2. Related Art

Commercially available gas sensors can be cumbersome to use, expensive and limited in performance (e.g. accuracy, selectivity, lowest detection limit, etc.). In addition, other major drawbacks may include inability to detect different types of gases at the same time, inability to measure absolute concentration of individual gases, the requirement for frequent re-calibration, a size incompatible with integration into small form factor systems such as wearable devices, the reliance on power-hungry techniques such as heating or on technologies not well suited to manufacturing in very high volume.
The ability to accurately detect multiple gases at the same time, often at parts-per-billion (PPB) sensitivity is becoming crucial to a growing number of industries as well as to the world-wide expansion of air quality monitoring initiatives aiming to address household and urban air pollution challenges.

SUMMARY

A nano gas sensor architecture that delivers key fundamental attributes required for the broad deployment of sensors capable of low detection limits (PPB) in support of highly granular collection of gas information in ambient air is described herein.
According to one aspect, a method for the selective detection of a target gas and measuring the concentration values comprising: taking resistance values of 8, 16, 32, 64, or 128 channels of nanohybrid gas sensors sampled every 80, 120, 160, or 200 milliseconds; filtering out the high frequency noise using an exponential average low pass filter; computing the rate of sensor response change; and evaluating sensor response with respect to other sensor channels including the temperature sensor.
According to another aspect, a method for tracking null reference baseline using multiple-channel time series signal from a hybrid nanostructure gas sensor, comprising: taking resistance values of multiple channels of nanohybrid gas sensors; comparing them against the reference resistance values benchmarked in ambient atmosphere with known concentrations of contributing gases; and adjusting the starting values for target gas concentrations using the deviations from benchmarked values for at least some of temperature, humidity and multiple channels of nanohybrid gas sensors.

BRIEF DESCRIPTION OF DRAWINGS

These and other features, aspects, and embodiments are described below in the section entitled “Detailed Description.”

FIG. 1 illustrates the basic principles to construct a gas sensor;

FIG. 2 is a prospective view of a physical implementation of a hybrid nanostructure gas sensing element in accordance with one embodiment;

FIG. 3 is a diagram illustrating an embodiment of a gas sensor array that can be included in the hybrid nanostructure gas sensing element of FIG. 2;

FIG. 4 is a block diagram of the hybrid nanostructure gas sensor system that incorporates the hybrid nanostructure gas sensing element of FIG. 2 in accordance with one embodiment;

FIG. 5 is a chart showing the flow of gas information through the hybrid nanostructure gas sensor system of FIG. 4;

FIG. 6 is an exploded view of an example wearable product built around a PCB embodiment of the hybrid nanostructure gas sensor system of FIG. 4;

FIG. 7 is a block diagram illustrating an example wired or wireless system that can be used in connection with various embodiments described herein;

FIG. 8 is a graph illustrating the filtering out of high frequency noise using an exponential average low pass filter in accordance with one embodiment; and

FIG. 9 is a diagram illustrating an example process for predicting settled resistance value for transient material response to changing gas concentration in accordance with one embodiment.

DETAILED DESCRIPTION

Embodiments for a hybrid nanostructure gas sensing system are described herein. The disclosure and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments and examples that are described and/or illustrated in the accompanying drawings and detailed in the following. It should be noted that the features illustrated in the drawings are not necessarily drawn to scale, and features of one embodiment may be employed with other embodiments as the skilled artisan would recognize, even if not explicitly stated herein. Descriptions of well-known components and processing techniques may be omitted so as to not unnecessarily obscure the embodiments of the disclosure. The examples used herein are intended merely to facilitate an understanding of ways in which the disclosure may be practiced and to further enable those of skill in the art to practice the embodiments of the disclosure. Accordingly, the examples and embodiments herein should not be construed as limiting the scope of the disclosure. Moreover, it is noted that like reference numerals represent similar parts throughout the several views of the drawings.
The architecture embodied in the hybrid nanostructure gas sensing system described hrein achieves the basic requirement of selectively identifying the presence of a gas analyte in diverse mixtures of ambient air but it is also designed to identify multiple gases at the same time, to be compatible in terms of size and power with very small form factors (including for mobile and wearable applications), to be easy to Integrate in IoT applications and to be self-calibrating, thus unshakling the application and/or the service provider from the burden and expense of regular re-calibration.
FIG. 1 describes the basic ingredients for a successful gas sensor 100. As can be seen, such a sensor includes a sensing element 102 that is created by depositing a sensitive layer 104 over a substrate 106. The sensing element 102 can then interact with gaseous chemical compounds 108 altering one or more electrical properties of the sensing element 102. The change in electrical properties can be detected by feeding the sensor raw signals 110 through specially designed signal processing electronics 112. The resulting response signals 114 can be measured and quantified directly or through the application of pattern recognition techniques.
The embodiments described herein comprise six basic elements. The first is the basic sensor element or sensing channel, which combines a structural component, built on a substrate suitable for reliable high-volume manufacturing, with a deposited electrolyte containing hybrid nano structures in suspension. The formulation of the electrolyte is specific to a particular gas or family of gases. A silicon substrate 106 and the structural component can be built using a MEMS manufacturing process. The structural component is essentially an unfinished electrical circuit between two electrodes. The deposition of the electrolyte completes the electrical circuit and, when biased and exposed to gas analytes, changes to one or more of the electrical characteristics of the circuit are used to detect and measure gases.
The second element is the arrangement of multiple sensing channels into an array structure specifically designed and optimized to interface with data acquisition electronics 112. The array structure, combined with the use of pattern recognition algorithms, makes it possible to detect multiple gases at the same time with a single sensor by customizing one or more of the individual sensing channels in the array for a specific gas or family of gases while using other sensing channels to facilitate such critical functions as selectivity.
FIG. 2 is a conceptual view of a hybrid nanostructure physical sensing element 102 in accordance with one example embodiment. Different materials can be used for the substrate 106 on which the rest of the sensing element 102 is constructed. But from the perspective of very high volume manufacturing, silicon technology can be preferred and specifically MEMS technology, which provides the necessary foundation for a customer-defined set of manufacturing steps with the flexibility to modulate the complexity of the process based on the sophistication of the sensor chip being built, e.g., to support further innovation or to address special product needs. Silicon technology also provides access to time-proven test methods and multiple sources of Automated Test Equipment that can be customized to fit the needs of gas sensing technology.
The sensing element 102 is made of an incomplete or “open” electrical circuit between two electrodes 202, which is then completed or “closed” by depositing, a molecular formulation electrolyte 204 with hybrid nanostructures 208 in suspension. The process is compatible with several commonly used deposition techniques but does require specially customized equipment and proprietary techniques to achieve the desired quality and reproducibility in a high-volume manufacturing environment. In certain embodiments, the sensing element 102 can be specially patterned to support efficient deposition of nanomaterial in pico-litter amounts and to facilitate incorporation of multiple elements into an array to enables the design of multi-gas sensors.
Electrodes 202 can then be bonded to bonding pads 206 in order to communicate signals 110 to the rest of the system.
One or more molecular formulations may be necessary to completely and selectively identify a particular gas. Combining multiple sensing elements 102, each capable of being “programmed” with a unique formulation, into a sensor array provides the flexibility necessary to detect and measure multiple gases at the same time. It also enables rich functional options such as for instance measuring humidity, an important factor to be accounted for in any gas sensor design, directly on the sensor chip (after all water vapor is just another gas). Another example is the combination for the same gas or family of gases of a formulation capable of very fast reaction to the presence of the gas while another formulation, slower acting, may be used for accurate concentration measurement; this would be important in applications where a very fast warning to the presence of a dangerous substance is required but actual accurate concentration measurement may not be needed at the same time (e.g. first responders in an industrial emergency situation).
FIG. 3 illustrates the preferred embodiment of a multichannel, gas sensor array 305 where a silicon substrate 302 is used with a MEMS manufacturing process to build the structure of the sensing channels on which the molecular formulations 204 can be deposited. For illustration purposes the size of the individual sensor die 304 is shown as being much larger than achievable in practice; a single 8″ wafer 300 will typically yield several thousand multi-gas capable sensor chips. An array 305 of sensing elements 102 is implemented on a single die 304 and each wafer 300 yields several thousand dies, or chips 304. Each sensing element 102 can then be functionalized by depositing a specific molecular formulation 204 thereon.
Thus, after MEMS manufacturing, additional steps are required to complete the fabrication of each sensing element 102. First, molecular formulations 204 are deposited and cured using specialized equipment. This happens at wafer level and the equipment is designed in a modular fashion to allow for the scaling of the output of a manufacturing facility by duplicating modules and fabrication processes in a copy-exactly fashion. After completion of the manufacturing steps, the wafers 300 must be singulated using a clean dicing technology in order to prevent damage to the sensing elements 102. An example of such technology is Stealth dicing.
The third element is the electronic transducer that detects changes in the electrical characteristics of the sensor array 305, provides signal conditioning and converts the analog signal from the sensor elements 102 into a digital form usable by the data acquisition system, described in more detail below. The transducer can be a low voltage analog circuit that provides biasing to the array of sensing channels and two functional modes: parking and measurement. Sensing channels are in parking mode either when not in measurement mode or when not used/enabled at all for a given application. The circuitry is designed to maintain the sensing channels in a linear region of operation, to optimize power consumption, to enable any combination of channels in either parking or measurement modes and to provide a seamless transition between modes.
FIG. 5 shows the basic flow of information through a complete nano gas sensor system, such as system 400 described in more detail below. When the sensor array 305 is exposed to the mixture of gas analytes 108 in its environment, in step 502, the sensitive layers 104 of the materials deposited on the sensor elements 102, or sensing channels react, according to their formulation 202, to the presence of specific component gases in the mixture. The reaction causes a change in the electrical characteristics of the sensing channels 102, which is captured by the transducer in the electronics sub-system, in step 504, and then analyzed by the pattern recognition system programmed in the sub-system MCU, in step 506. The output is an absolute value of the concentration of the gases being detected. This is then combined, in step 508, with other desirable meta-data such as time or geo-location into a digital record. This digital record (or a portion of it) can optionally be displayed locally in step 510 (for example, in the case of a wearable application where the sensor is paired to a phone, the data can be further manipulated and displayed by a specially written mobile application running on the phone). More importantly the data is uploaded, via a mechanism that is dependent on the application, to a Cloud data platform in step 512, where the data can be normalized in step 514 and accessed via various application in step 516.
The fourth element is a MCU-based data acquisition and measurement engine, which also provides additional functions such as overall sensor system management and communication, as necessary with encryption, to and from a larger system into which the sensor is embedded.
The third and fourth elements are designed to work together and to form a complete electronic sub-system specifically tuned to work with the array of sensing channels 305 implemented as a separate component. The transducer 404 is firmware configurable to provide optimal A/D conversion for a pattern recognition system running on the MCU 406 and implementing the gas detection and measurement algorithm(s).
The electronic sub-system 402 is suitable for implementation in a variety of technologies depending on target use model and technical/cost trade-offs. PCB implementations will enable quick turn-around and the declination of a family of related products (for instance with different communication interfaces) to support multiple form factors and applications with the same core electronics. When size and power/performance trade-offs are critical, the electronic sub-system 402 is implemented as a System On a Chip (SoC), which can then be integrated together with a MEMS chip carrying the array of sensing channels 305 into a System In a Package (SIP).
The sensor die 304 must then be assembled with the sensor's electronic sub-system to complete the hybrid nanostructure gas sensor 400 for which a functional block diagram is shown in FIG. 4.
The electronic sub-system can be implemented as a PCB or as a SoC. If the PCB route is followed the sensor die 304 can be either wire-bonded to the electronic sub-system 402 board after completion of the PCB Assembly (PCBA) step or, if the sensor die 304 has itself been individually assembled in a SMT package, it can be soldered on the board as part of PCBA. If the SoC route is followed, the sensor die together with the SoC die of the electronic sub-system 402 can be stacked and assembled together into a single package (System In a Package) or each can possibly be assembled into individual packages.
Either assembled into its own package or assembled into a SIP, the sensor chip 304 must be exposed to ambient air. Therefore, the package lid must include a hole of sufficient size over the sensor.
Testing happens at various points of the sensor manufacturing process.
After sensor functionalization (deposition of the molecular formulations 204), certain handling precautions must be followed for the rest of the product manufacturing flow to prevent accidental damage to the sensor chip 304 (e.g. a pick and place tool must not make contact with the surface of the sensing elements).
The fifth element is the gas detection and measurement algorithm. The algorithm implements a method for predicting target gas concentration by reading the hybrid nanostructure sensor array's multivariate output and processing it inside the algorithm. The algorithm analyzes sensor signals in real time and outputs estimated values for concentrations of target gases. The algorithm development is based on models that are specific to the materials deposited on the sensing channels of the sensor array. These models are trained based on the collection of an abundant volume of data in the laboratory (multiple concentrations of target gases, combinations of gases, various values of temperature, relative humidity and other environmental parameters). Sophisticated supervised modeling techniques are used to attain the best possible agreement between true and predicted values of target gas concentrations. Prior to deployment, extensive lab and field testing is carried out to optimize model performance and finalize sensor validation.
In certain embodiments, the algorithm can use exponential average low pass filtering to ensure efficient memory management and fast processing speeds. FIG. 8 is a graph illustrating the filtering of the high frequency noise using the exponential average low pass filter. The high frequency component is depicted as plot 802, while the filtered signal is plotted as line 804.
FIG. 9 is a diagram illustrating the computation of settled resistance value estimate for transient material response to changing gas concentration. First, in step 902, the resistance rate for each channel is computed. The value of resistance rate for each channel is then taken as a byproduct of the exponential average low pass filter and multiplied by the material time constant to evaluate the transient resistance in step 904. The time constant is the measured property of the material response to the target gas. The settled resistance estimate, which is a sum of the transient resistance and a current resistance value is then determined in step 906.
Thus, in certain embodiments, a method for the selective detection of a target gas and measuring the concentration values comprises processing the resistance values of 8, 16, 32, 64, or 128 channels of nanohybrid gas sensors sampled every 80, 120, 160, or 200 milliseconds and filtering out the high frequency noise using the exponential average low pass filter illustrated in FIG. 8. This is then followed by signal processing such as: computing the rate of sensor response change; and evaluating sensor response in relation to other sensor channels including a temperature sensor channel.
Predicting settled sensor resistance values, as shown in FIG. 9, can then be used to estimate algorithm input values when sensor output values are in transition following the change in gas concentration values. This is done in order to accelerate target gas concentration predictions without the need for waiting a long time to reach equilibrium in interaction between the sensor material and changing gas.
A gas model can then be used to relate change in resistance of material segments to target gas concentration via model coefficients. The relation between sensor response and change in target gas concentration is described by the equation:
C ⁱ=Σ_jα_j ⁱ(R ^j −R ^j ₀)/R ^j ₀ +C ⁱ ₀.
R^j ₀is defined as the channel resistance for material j right before the exposure, R^jis defined as the resistance right after the exposure. The sum is taken over all channels of various materials j contributing to the algorithm input.
Cⁱ ₀is defined as the target gas i concentration right before the exposure, Cⁱis defined as the target gas i concentration right after exposure. For every target gas i each material j channel contains certain material-gas coefficient value α_j ⁱ.
Preprocessed signals from nanohybrid gas sensor channels can then be grouped into segments each representing a specific material deposited on sensor channel. Multiple segments can be used in engaging a single target gas model. Multiple model concurrently executed in the algorithm predicting concentration values for gases, such as: NO2, CO, O3, CH2O, CH4, etc.
Response of a sensor is a result of exposure to multiple gas constituents in the atmosphere as well as the reaction of the sensor to various environmental factors such as humidity, temperature, pressure, and air flow. The algorithm resolves this cross-sensitivity complexity via an over-constrained system of modeling equations. Compensation coefficients to account for environmental factors are: i. humidity compensation coefficient; ii temperature compensation coefficient; and iii pressure and air flow compensation coefficient.
The optimal solution to the system of equations is the output of the algorithm containing the concentration values for target gases.
In certain embodiments, a method for tracking null reference baseline using multiple-channel time series signal from a hybrid nanostructure gas sensor comprises taking resistance values of multiple channels of nanohybrid gas sensors and comparing them against the reference resistance values benchmarked in ambient atmosphere with known concentrations of contributing gases. The deviations from benchmarked values can then be used to adjust the starting values for target gas concentrations. The adjustment process uses inputs from temperature, humidity and multiple channels of nanohybrid gas sensors.
The first five elements together constitute the hybrid nanostructure gas sensor 400 and provide all the functionality necessary to detect multiple gases 108 in ambient air at the same time and to report their absolute concentrations. The sensing capability of the hybrid nanostructure sensor array 305 is always “on” and the gas detection and measurement algorithm makes it possible for the sensor 400 to require no special calibration step before use and to remain self-calibrating through its operational life.
The sixth element is the Cloud Data Platform that enables a virtually unlimited number of sensors 400 deployed as part of a virtually unlimited number of applications to be hosted in a global database where big data techniques can be used to analyze, query and visualize the information to infer actionable insight. The use of a Cloud-based environment provides all the necessary flexibility to customize how the data can be partitioned, organized, protected and accessed based on the rights of individual tenants.
The Cloud data platform provides another layer of sophistication to the system by allowing Cloud applications to operate on the data set. For instance, sensors 400 that are located in the same vicinity would typically report consistent gas values thus allowing errant results to be identified and a possible malfunction of one node of a network of sensors investigated.
The continuous collection of highly granular gas information by a multitude of connected devices (IoT—Internet Of Things) is critical to go beyond monitoring to generate actionable insight from large amount of collected data (Big Data Analytics, Artificial Intelligence).
A few application examples are highlighted below.

Example 1

We take 20,000 breaths every day and the air we breathe impacts our health—the science is already clear on this—but we rarely know what is in the air we breathe. To take meaningful action, consumers, scientists, public officials and business owners need the ability to measure air pollution at a personal, local and granular level which has, before this invention, been impossible due to the limitations of commercially available gas sensors mentioned above.
Mounting evidence suggests that prenatal and early life exposure to common environmental toxins, such as air pollution from fossil fuels, can cause lasting damage to the developing human brain. These effects are especially pronounced in highly vulnerable fetuses, babies, and toddlers as most of the brain's structural and functional architecture is established during these early developmental periods. These disruptions to healthy brain development can cause various cognitive, emotional, and behavioral problems in later infancy and childhood.
The sensor technology described herein allows researchers to gather highly detailed, accurate data about pregnant women's exposure to environmental air pollution and the resulting effects on the developing brain. The availability of this technology will represent a profound advance on current methods and efforts in the field that will have far-reaching consequences for improving newborn and child health throughout the world.
More generally, personal air monitoring and local indoor and outdoor monitoring will be a breakthrough for scientific research, healthcare interventions, personal preventive actions, advocacy and more.
The sensor technology described herein can deliver complete processing and gas results to a broad spectrum of smart systems under development for the Smart Cities of tomorrow. The sensor is designed for Plug and Play integration into IoT devices and the small form factor is compatible with a multitude of devices from LED lights to smart meters, to standalone monitoring stations, to non-stationary devices (drones, public vehicles, wearables, phones, etc.).

Example 2

The sensor technology described herein can be used in smart appliances such as connected refrigerators, that will help customers monitor food freshness, detect spoilage and the presence of harmful pesticide residues. The simultaneous, multi-gas, sensing capability of the invention will enable sensors that can recognize the gas patterns associated with the condition of specific foods.

Example 3

A network or grid of the sensors 400 described herein, can be integrated into industrial areas such as petrochemical complexes and oil refineries to allow companies to monitor the sites during regular operation (e.g. for leaks) or in the event of natural or human-made disasters. The sensors can also be installed in drones for data collection in hard to reach or potentially dangerous area. The ability of the technology to be deployed in wearables and in fixed and mobile networks will provide both personal protection and granular data across large area, allow the constant monitoring of a facility for preventive measures to be taken in a timely fashion, save critical time when urgent decision making is required and provide invaluable information to protect workers and emergency personnel.
The same technology can place powerful new tools in the hands of first responders and officials responsible for public safety and homeland security.
FIG. 6 shows an example product 600, in this case a battery-powered wearable device, with the sensor 400 implemented as a small PCB. The sensor technology lends itself to integration into any number of IoT devices. While the sensor does not need the active creation of an airflow to function, the sensitive layers 104 at the surface of the sensor must be exposed to ambient air and at the same time provided a reasonable amount of protection from dust and fluids. This is usually achieved by designing an air interface that ensures that the sensor 400 is behind a perforated shield (e.g. the lid of an enclosure) with a thin membrane (PTFE, 0.5 um mesh) being used to provide splash and dust protection. Outdoor applications may require the design of a more complicated air interface to meet the weather-proofing requirements.
FIG. 7 is a block diagram illustrating an example wired or wireless system 550 that can be used in connection with various embodiments described herein. For example the system 550 can be used as or in conjunction with one or more of the platforms, devices or processes described above, and may represent components of a device, such as sensor 400, the corresponding backend or cloud server(s), and/or other devices described herein. The system 550 can be a server or any conventional personal computer, or any other processor-enabled device that is capable of wired or wireless data communication. Other computer systems and/or architectures may be also used, as will be clear to those skilled in the art.
The system 550 preferably includes one or more processors, such as processor 560. Additional processors may be provided, such as an auxiliary processor to manage input/output, an auxiliary processor to perform floating point mathematical operations, a special-purpose microprocessor having an architecture suitable for fast execution of signal processing algorithms (e.g., digital signal processor), a slave processor subordinate to the main processing system (e.g., back-end processor), an additional microprocessor or controller for dual or multiple processor systems, or a coprocessor. Such auxiliary processors may be discrete processors or may be integrated with the processor 560. Examples of processors which may be used with system 550 include, without limitation, the Pentium® processor, Core i7® processor, and Xeon® processor, all of which are available from Intel Corporation of Santa Clara, Calif. Example processor that can be used in system 400 include the ARM family of processors and the new open source RISC-V processor architecture.
The processor 560 is preferably connected to a communication bus 555. The communication bus 555 may include a data channel for facilitating information transfer between storage and other peripheral components of the system 550. The communication bus 555 further may provide a set of signals used for communication with the processor 560, including a data bus, address bus, and control bus (not shown). The communication bus 555 may comprise any standard or non-standard bus architecture such as, for example, bus architectures compliant with industry standard architecture (ISA), extended industry standard architecture (EISA), Micro Channel Architecture (MCA), peripheral component interconnect (PCI) local bus, or standards promulgated by the Institute of Electrical and Electronics Engineers (IEEE) including IEEE 488 general-purpose interface bus (GPIB), IEEE 696/S-100, and the like.
System 550 preferably includes a main memory 565 and may also include a secondary memory 570. The main memory 565 provides storage of instructions and data for programs executing on the processor 560, such as one or more of the functions and/or modules discussed above. It should be understood that programs stored in the memory and executed by processor 560 may be written and/or compiled according to any suitable language, including without limitation C/C++, Java, JavaScript, Pearl, Visual Basic, .NET, and the like. The main memory 565 is typically semiconductor-based memory such as dynamic random access memory (DRAM) and/or static random access memory (SRAM). Other semiconductor-based memory types include, for example, synchronous dynamic random access memory (SDRAM), Rambus dynamic random access memory (RDRAM), ferroelectric random access memory (FRAM), and the like, including read only memory (ROM).
The secondary memory 570 may optionally include an internal memory 575 and/or a removable medium 580, for example a floppy disk drive, a magnetic tape drive, a compact disc (CD) drive, a digital versatile disc (DVD) drive, other optical drive, a flash memory drive, etc. The removable medium 580 is read from and/or written to in a well-known manner. Removable storage medium 580 may be, for example, a floppy disk, magnetic tape, CD, DVD, SD card, etc.
The removable storage medium 580 is a non-transitory computer-readable medium having stored thereon computer executable code (i.e., software) and/or data. The computer software or data stored on the removable storage medium 580 is read into the system 550 for execution by the processor 560.
In alternative embodiments, secondary memory 570 may include other similar means for allowing computer programs or other data or instructions to be loaded into the system 550. Such means may include, for example, an external storage medium 595 and an interface 590. Examples of external storage medium 595 may include an external hard disk drive or an external optical drive, or and external magneto-optical drive.
Other examples of secondary memory 570 may include semiconductor-based memory such as programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable read-only memory (EEPROM), or flash memory (block oriented memory similar to EEPROM). Also included are any other removable storage media 580 and communication interface 590, which allow software and data to be transferred from an external medium 595 to the system 550.
System 550 may include a communication interface 590. The communication interface 590 allows software and data to be transferred between system 550 and external devices (e.g. printers), networks, or information sources. For example, computer software or executable code may be transferred to system 550 from a network server via communication interface 590. Examples of communication interface 590 include a built-in network adapter, network interface card (NIC), Personal Computer Memory Card International Association (PCMCIA) network card, card bus network adapter, wireless network adapter, Universal Serial Bus (USB) network adapter, modem, a network interface card (NIC), a wireless data card, a communications port, an infrared interface, an IEEE 1394 fire-wire, or any other device capable of interfacing system 550 with a network or another computing device.
Communication interface 590 preferably implements industry promulgated protocol standards, such as Ethernet IEEE 802 standards, Fiber Channel, digital subscriber line (DSL), asynchronous digital subscriber line (ADSL), frame relay, asynchronous transfer mode (ATM), integrated digital services network (ISDN), personal communications services (PCS), transmission control protocol/Internet protocol (TCP/IP), serial line Internet protocol/point to point protocol (SLIP/PPP), and so on, but may also implement customized or non-standard interface protocols as well.
Software and data transferred via communication interface 590 are generally in the form of electrical communication signals 605. These signals 605 are preferably provided to communication interface 590 via a communication channel 600. In one embodiment, the communication channel 600 may be a wired or wireless network, or any variety of other communication links. Communication channel 600 carries signals 605 and can be implemented using a variety of wired or wireless communication means including wire or cable, fiber optics, conventional phone line, cellular phone link, wireless data communication link, radio frequency (“RF”) link, or infrared link, just to name a few.
Computer executable code (i.e., computer programs or software) is stored in the main memory 565 and/or the secondary memory 570. Computer programs can also be received via communication interface 590 and stored in the main memory 565 and/or the secondary memory 570. Such computer programs, when executed, enable the system 550 to perform the various functions of the present invention as previously described.
In this description, the term “computer readable medium” is used to refer to any non-transitory computer readable storage media used to provide computer executable code (e.g., software and computer programs) to the system 550. Examples of these media include main memory 565, secondary memory 570 (including internal memory 575, removable medium 580, and external storage medium 595), and any peripheral device communicatively coupled with communication interface 590 (including a network information server or other network device). These non-transitory computer readable mediums are means for providing executable code, programming instructions, and software to the system 550.
In an embodiment that is implemented using software, the software may be stored on a computer readable medium and loaded into the system 550 by way of removable medium 580, I/O interface 585, or communication interface 590. In such an embodiment, the software is loaded into the system 550 in the form of electrical communication signals 605. The software, when executed by the processor 560, preferably causes the processor 560 to perform the inventive features and functions previously described herein.
In an embodiment, I/O interface 585 provides an interface between one or more components of system 550 and one or more input and/or output devices. Example input devices include, without limitation, keyboards, touch screens or other touch-sensitive devices, biometric sensing devices, computer mice, trackballs, pen-based pointing devices, and the like. Examples of output devices include, without limitation, cathode ray tubes (CRTs), plasma displays, light-emitting diode (LED) displays, liquid crystal displays (LCDs), printers, vacuum florescent displays (VFDs), surface-conduction electron-emitter displays (SEDs), field emission displays (FEDs), and the like.
The system 550 also includes optional wireless communication components that facilitate wireless communication over a voice and over a data network. The wireless communication components comprise an antenna system 610, a radio system 615 and a baseband system 620. In the system 550, radio frequency (RF) signals are transmitted and received over the air by the antenna system 610 under the management of the radio system 615.
In one embodiment, the antenna system 610 may comprise one or more antennae and one or more multiplexors (not shown) that perform a switching function to provide the antenna system 610 with transmit and receive signal paths. In the receive path, received RF signals can be coupled from a multiplexor to a low noise amplifier (not shown) that amplifies the received RF signal and sends the amplified signal to the radio system 615.
In alternative embodiments, the radio system 615 may comprise one or more radios that are configured to communicate over various frequencies. In one embodiment, the radio system 615 may combine a demodulator (not shown) and modulator (not shown) in one integrated circuit (IC). The demodulator and modulator can also be separate components. In the incoming path, the demodulator strips away the RF carrier signal leaving a baseband receive audio signal, which is sent from the radio system 615 to the baseband system 620.
If the received signal contains audio information, then baseband system 620 decodes the signal and converts it to an analog signal. Then the signal is amplified and sent to a speaker. The baseband system 620 also receives analog audio signals from a microphone. These analog audio signals are converted to digital signals and encoded by the baseband system 620. The baseband system 620 also codes the digital signals for transmission and generates a baseband transmit audio signal that is routed to the modulator portion of the radio system 615. The modulator mixes the baseband transmit audio signal with an RF carrier signal generating an RF transmit signal that is routed to the antenna system and may pass through a power amplifier (not shown). The power amplifier amplifies the RF transmit signal and routes it to the antenna system 610 where the signal is switched to the antenna port for transmission.
The baseband system 620 is also communicatively coupled with the processor 560. The central processing unit 560 has access to data storage areas 565 and 570. The central processing unit 560 is preferably configured to execute instructions (i.e., computer programs or software) that can be stored in the memory 565 or the secondary memory 570. Computer programs can also be received from the baseband processor 610 and stored in the data storage area 565 or in secondary memory 570, or executed upon receipt. Such computer programs, when executed, enable the system 550 to perform the various functions of the present invention as previously described. For example, data storage areas 565 may include various software modules (not shown).
Various embodiments may also be implemented primarily in hardware using, for example, components such as application specific integrated circuits (ASICs), or field programmable gate arrays (FPGAs). Implementation of a hardware state machine capable of performing the functions described herein will also be apparent to those skilled in the relevant art. Various embodiments may also be implemented using a combination of both hardware and software.
Furthermore, those of skill in the art will appreciate that the various illustrative logical blocks, modules, circuits, and method steps described in connection with the above described figures and the embodiments disclosed herein can often be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled persons can implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the invention. In addition, the grouping of functions within a module, block, circuit or step is for ease of description. Specific functions or steps can be moved from one module, block or circuit to another without departing from the invention.
Moreover, the various illustrative logical blocks, modules, functions, and methods described in connection with the embodiments disclosed herein can be implemented or performed with a general purpose processor, a digital signal processor (DSP), an ASIC, FPGA or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor can be a microprocessor, but in the alternative, the processor can be any processor, controller, microcontroller, or state machine. A processor can also be implemented as a combination of computing devices, for example, a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.
Additionally, the steps of a method or algorithm described in connection with the embodiments disclosed herein can be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module can reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium including a network storage medium. An exemplary storage medium can be coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium can be integral to the processor. The processor and the storage medium can also reside in an ASIC.
Any of the software components described herein may take a variety of forms. For example, a component may be a stand-alone software package, or it may be a software package incorporated as a “tool” in a larger software product. It may be downloadable from a network, for example, a website, as a stand-alone product or as an add-in package for installation in an existing software application. It may also be available as a client-server software application, as a web-enabled software application, and/or as a mobile application.
While certain embodiments have been described above, it will be understood that the embodiments described are by way of example only. Accordingly, the systems and methods described herein should not be limited based on the described embodiments. Rather, the systems and methods described herein should only be limited in light of the claims that follow when taken in conjunction with the above description and accompanying drawings.

Claims

What is claimed is:

1. A method for the selective detection of a target gas and measuring the concentration values comprising:

taking resistance values of 8, 16, 32, 64, or 128 channels of nanohybrid gas sensors sampled every 80, 120, 160, or 200 milliseconds;

filtering out the high frequency noise using the exponential average low pass filter;

computing the rate of sensor response change; and

evaluating sensor response with respect to other sensor channels including the temperature sensor.

2. The method of claim 1, further comprising predicting settled sensor resistance values to estimate algorithm input values when sensor output values are in transition following the change in gas concentration values.

3. The method of claim 2, further comprising using a gas model that relates change in resistance of material segments to target gas concentration via model coefficients, wherein the relation between sensor response and change in target gas concentration described by equation:

C ⁱ=Σ_jα_j ⁱ(R ^j −R ^j ₀)/R ^j ₀ +C ⁱ ₀;

Wherein R^j ₀is defined as the channel resistance for material j right before the exposure, R^jis 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

Cⁱ ₀is defined as the target gas i concentration right before the exposure, Cⁱis 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 α_j ⁱ.

4. The method of claim 1, wherein preprocessed signals from nanohybrid gas sensor channels are grouped into segments each representing a specific material deposited on sensor channel, and wherein multiple segments are used in engaging a single target gas model.

5. The method of claim 4, wherein multiple models are concurrently executed in the algorithm predicting concentration values for gases, including at least one of NO2, SO2, CO, CO2, O3, CH2O, CH4, NH3, N20, organic compounds such as Acetone and Ethanol, and various Hydrocarbons.

6. The method of claim 1, wherein a response of a sensor is a result of exposure to multiple gas constituents in the atmosphere as well as the reaction of the sensor to various environmental factors such as humidity, temperature, pressure and air flow, and further comprising resolving the cross-sensitivity complexity via an over-constrained system of modeling equations.

7. The method of claim 6, wherein the compensation coefficients to account for environmental factors are a combination of: humidity compensation coefficient, temperature compensation coefficient, and pressure and air flow compensation coefficient.

8. A method for tracking null reference baseline using multiple-channel time series signal from a hybrid nanostructure gas sensor, comprising:

taking resistance values of multiple channels of nanohybrid gas sensors;

comparing them against the reference resistance values benchmarked in ambient atmosphere with known concentrations of contributing gases; and

adjusting the starting values for target gas concentrations using the deviations from benchmarked values for at least some of temperature, humidity and multiple channels of nanohybrid gas sensors.