WO2021217090A1

WO2021217090A1 - Graphene-based chemical sensing device and system using piezoresistivity and resistivity

Info

Publication number: WO2021217090A1
Application number: PCT/US2021/028976
Authority: WO
Inventors: Prasad Panchalan; Sanjiv Bhatt; Alberto Vidal; Amit Lal
Original assignee: Culvert Engineering Solutions, Llc
Priority date: 2020-04-23
Filing date: 2021-04-23
Publication date: 2021-10-28

Abstract

In certain embodiments, selectable, repeatable, and reliable chemical sensing may be facilitated. In some embodiments, a fluid sample may be received at a sensing device having one or more chemical sensitivities. Voltage and pressure may be applied to the sensing device. A reaction of the sensing device to a chemical in the fluid sample may be detected based on the one or more chemical sensitivities of the sensing device and the applied voltage and pressure. For example, a sensing unit within the sensing device having a particular chemical sensitivity may react with a chemical in the fluid sample. In some embodiments, a reaction may be a change in resistivity or piezoresistivity. One or more chemicals in the fluid sample associated with the reaction of the sensing device may be identified. In some embodiments, machine learning models or neural networks may facilitate the identification of chemicals associated with the reaction.

Description

GRAPHENE-BASED CHEMICAL SENSING DEVICE AND SYSTEM USING PIEZORESISTIVITY AND RESISTIVITY

INCORPORATION BY REFERENCE

[001] The present application claims priority to U.S. Provisional Patent Application No. 63/014,428 filed on April 23, 2020 entitled “GRAPHENE-BASED CHEMICAL SENSING DEVICE AND SYSTEM USING PIEZORESISTIVITY AND RESISTIVITY,” the contents of which are herein incorporated by reference in their entirety.

FIELD OF THE INVENTION

[002] The invention relates to chemical sensing using piezoresistivity and resistivity of a graphene-based sensing device.

BACKGROUND OF THE INVENTION

[003] Advances in sensor, computing and software technologies have made it possible for computers to detect and identify smells or chemicals in the environment. However, these technologies are limited in their selectivity, repeatability, and reliability. For example, graphene-based sensing systems may be highly sensitive but often have selectivity problems, as they may exhibit similar responses to different types of gases. This drawback may lead to false detecting of various chemicals. Non-repeatability is another drawback, as preparation of sensing materials, construction of gas sensors, building of experimental platforms, and characterization of parameters all contribute to the non-repeatability of current chemical sensing devices. Problems with reliability stem from degradation of manufactured sensors over time.

[004] Many existing sensors have been demonstrated to have high sensitivity but poor repeatability and reliability. For example, metal oxide semiconductor sensors have high operating temperatures and high power consumption and are sensitive to sulfur poisoning. Metal oxide semiconductor field effect transistor sensors exhibit baseline drift and require a controlled environment. Calorimetric sensors have high operating temperatures and risk of catalyst poisoning. Optical sensors have complex circuitry and low portability and suffer from photobleaching. Quartz crystal microbalance sensors have complex circuitry and are sensitive to humidity and temperature. Surface acoustic wave sensors have complex circuitry and are sensitive to humidity and temperature. Carbon nanofiber based sensors are expensive and difficult to fabricate and lack precision. Conducting polymer sensors are sensitive to humidity and temperature and may suffer from baseline drift and saturation. Carbon particle based sensors are sensitive to humidity and temperature and may suffer from baseline drift. Due to the drawbacks of current sensing systems, as described above, a sensing system that is selective, repeatable, and reliable is needed. These and other drawbacks exist.

SUMMARY OF THE INVENTION

[005] Aspects of the invention relate to methods, apparatuses, or systems for graphene- based sensing of chemicals using piezoresistivity and resistivity.

[006] Some aspects include a sensitive, selective, repeatable, and reliable sensing device. The sensing device is able to differentiate between similar molecules, produce the same results as other identical devices, and maintain its properties over time. A graphene-based sensing system which identifies chemicals by measuring changes in piezoresistivity and resistivity in response to an interaction with the chemicals may be used to achieve the aforementioned objectives.

[007] Some other aspects include a sensing system which is able to quickly identify chemicals with low power consumption. The small size of the sensing system described herein (e.g., chip scale) may expand the applications for which the system may be used.

[008] Some other aspects include a remote machine learning computer system for matching chemical reactions of a sample in a cartridge to a library or model of other chemical reactions: a wireless or wired communications system that sends sample measurements to the remote computer system, and receives processed results and outcomes.

[009] Various other aspects, features, and advantages of the invention will be apparent through the detailed description of the invention and the drawings attached hereto. It is also to be understood that both the foregoing general description and the following detailed description are examples and not restrictive of the scope of the invention. As used in the specification and in the claims, the singular forms of “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise. In addition, as used in the specification and the claims, the term “or” means “and/or” unless the context clearly dictates otherwise. BRIEF DESCRIPTION OF THE DRAWINGS

[010] FIG. 1 shows a system for facilitating sensitive, repeatable, and reliable chemical sensing, in accordance with one or more embodiments.

[Oil] FIG. 2 shows graphene deposited on a silicon test chip on a pressure sensor, in accordance with one or more embodiments.

[012] FIG. 3 shows a device fabrication process, in accordance with one or more embodiments.

[013] FIG. 4 shows a sensing unit and a sensing device, in accordance with one or more embodiments.

[014] FIG. 5 shows a machine learning model configured to facilitate sensitive, repeatable, and reliable chemical sensing, in accordance with one or more embodiments.

[015] FIG. 6 shows an exposed measurement structure, in accordance with one or more embodiments.

[016] FIG. 7 shows simultaneous excitation of an isolated reference structure and an exposed measurement structure, in accordance with one or more embodiments.

[017] FIG. 8 shows a stress function with an isolated reference structure and an exposed measurement structure, in accordance with one or more embodiments.

[018] FIG. 9 shows a plane view of a sensor, in accordance with one or more embodiments. [019] FIG. 10 shows a flowchart of a method of facilitating sensitive, repeatable, and reliable chemical sensing, in accordance with one or more embodiments.

DESCRIPTION OF THE INVENTION

[020] In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the invention. It will be appreciated, however, by those having skill in the art that the embodiments of the invention may be practiced without these specific details or with an equivalent arrangement. In other cases, well-known structures and devices are shown in block diagram form in order to avoid unnecessarily obscuring the embodiments of the invention. [021] FIG. 1 shows a system 100 for facilitating sensitive, repeatable, and reliable chemical sensing, in accordance with one or more embodiments. As shown in FIG. 1, system 100 may include computer system 102, client device(s) 104 (or client devices 104a-104n), database(s) 130, or other components. Computer system 102 may include identification subsystem 110 or other components. Each client device 104 may include sensing subsystem 120, identification subsystem 122, user interface subsystem 124, display subsystem 126, or other components. Each client device 104 may include any type of mobile terminal, fixed terminal, or other device. By way of example, client device(s) 104 may include a desktop computer, a notebook computer, a tablet computer, a smartphone, a wearable device (e.g., augmented reality glasses or goggles), a handheld device, a device attachment, or another client device. Users may, for instance, utilize one or more client devices 104 to interact with one another, one or more servers, or other components of system 100. It should be noted that, while one or more operations are described herein as being performed by particular components of computer system 102, those operations may, in some embodiments, be performed by other components of computer system 102 or other components of system 100. As an example, while one or more operations are described herein as being performed by components of computer system 102, those operations may, in some embodiments, be performed by components of client device(s) 104.

[022] In some embodiments, system 100 may facilitate sensitive, repeatable, and reliable chemical sensing. System 100 may specifically improve upon repeatability and reliability or prior systems. Background FIG. 2 shows graphene deposited on a silicon test chip 200 on a pressure sensor 4040250, in accordance with one or more embodiments. As shown in background FIG. 2, various materials may be deposited on silicon test chips on a pressure sensor, and pressure sensor sensitivity (e.g., measured in /mmHg) may be different for each material. According to chart 252, graphene has the highest pressure sensor sensitivity of the materials shown (e.g., 323 /mmHg) and may thus contribute to a chemical sensing system with high sensitivity.

[023] Background FIG. 3 shows a device fabrication process 300, in accordance with one or more embodiments. For example, sensing devices may be processed using various etching, doping, transfer, liftoff, deposition, and other processes. The sensing devices described herein may be manufactured using device fabrication process 300, other processes, or any combination therein.

[024] FIG. 4 shows a sensing unit 400 and a sensing device 450, in accordance with one or more embodiments. For example, sensing device 450 may have one or more sensing units 458, each corresponding to one or more chemical sensitivities. The sensing units may have one or more coatings which provide the sensing units with one or more properties. In some embodiments, the sensing units may be combined to create a sensing device 450 having particular chemical sensitivities. In some embodiments, the combination of sensing units 458 in sensing device 450 may correspond to a particular application for which the sensing device is to be used. In some embodiments, system 100 may receive a fluid sample at the sensing device. In some embodiments, the one or more chemical sensitivities may cause one or more sensing units 458 of sensing device 450 to react to a chemical in the fluid sample. In some embodiments, system 100 may identify one or more chemicals associated with the reactions of the sensing units. For example, system 100 may compare the reactions to a database comprising reactions based on chemical sensitivities and chemicals associated with the reactions based on the chemical sensitivities. In some embodiments, system 100 may utilize a machine learning model (e.g., generic edge ML platform 454, as shown in FIG. 4, or ML model 502, as shown in FIG. 5) or a neural network in order to identify the chemicals based on the reaction of the sensing device.

[025] In some embodiments, sensing units 458 may be coated with carbon or an allotrope of carbon, such as graphene. In some embodiments, graphene may be used due to its sensitive properties and ability to bond with chemicals (e.g., smells). Graphene manufacturing processes have allowed CVD graphene to be scalable and integrated with ubiquitous CMOS technology, for example, via growth on deposited copper thin film catalysts on standard silicon/silicon oxygen wafers (e.g., 100-200mm). Monolayer graphene coverage of over 95% is achieved on 100-200mm wafer substrates with negligible effects (e.g., confirmed by extensive Raman mappings). Graphene functionalization occurs via attachment at the defect site. CVD processes and the geometric pattern of a graphene layout allow for negligible surface defects and known quantified edges (e.g., perimeter lengths). In some embodiments, functional groups (e.g., described below) atach to the edges of the graphene layout. In some embodiments, a self-selected assembly environment will produce repeatable functional sites and density. This may contribute to a repeatable sensing device.

[026] In some embodiments, graphene may be applied as a layer onto sensing units 458, inserted as a filler into the sensing units, placed within a cavity of the sensing units, or otherwise applied to the sensing units. In some embodiments, the sensing units may additionally be coated with a chemical functionality dopant. For example, the dopant may be an impurity element which is added to the sensing unit in order to alter its properties. In some embodiments, the chemical functionality dopant may determine the type of sensing unit. As shown in FIG. 4, sensing unit 400 may include a graphene layer 402, functionalizable layer moieties 404, and functional groups 406.

[027] As discussed above, the functional group of a particular sensing unit may determine the type of sensing unit, for example, type A, type B, type C, etc. For example, a particular chemical functionality dopant may be applied to sensing unit 400, thereby adding a first chemical functionality sensing unit 400. Sensing unit 400 may thereafter be a first type (e.g., type A) of sensing units. In some embodiments, different chemical functionality dopants may be applied to different series of sensing units such that multiple types of sensing units are manufactured and may be included in a single sensing device (e.g., as shown by sensing units 458). In some embodiments, additional coatings or layers may be applied to the sensing units. For example, dielectric materials, which may insulate the sensing units from electric conduction, may be applied to the sensing units. Metal oxide, DNA dopants, or other layers may be applied to the sensing units to provide the sensing units with various properties. In some embodiments, each coating or layer may be applied using heat (e.g., in a furnace), with pipetes, or using other application techniques.

[028] In some embodiments, sensing device 450 may include a batery 452. In some embodiments, sensing device 450 may include an air pump 460. (e.g., or fluid pump, suction pump, or other type of pump), for example, to pump a gas sample 408 across one or more sensing units. In some embodiments, air pump 460 may pump gas sample 408 across one or more sensing units of a sensing device. In some embodiments, air pump 460 may activate when a request for a measurement is received and may deactivate once the measurement has been taken. In some embodiments, sensing device 450 may include a voltage generator, an analog-to-digital converter (ADC) 456, or some other means by which to apply a voltage to the device.

[029] Returning to FIG. 1, sensing subsystem 120 may include various components shown in FIG. 4. For example, sensing subsystem 120 may include sensing units such as sensing unit 400, as shown in FIG. 4. Sensing subsystem 120 may include sensing device 450, as shown in FIG. 4. In some embodiments, sensing subsystem 120 may comprise a communication link to user interface subsystem 124 or to other components of system 100 (e.g., via network 150). In some embodiments, user interface subsystem 124 may be configured to provide an interface between system 100 and the user or other users through which the user or other users may provide information to and receive information from system 100. This enables data, cues, preferences, or instructions and any other communicable items, collectively referred to as “information,” to be communicated between the user and the various components of system 100.

[030] In some embodiments, user interface subsystem 124 may be or be included in a computing device, such as a desktop computer, a laptop computer, a smartphone, a tablet computer, a wearable device, an augmented device, or other computing devices. Such computing devices may run one or more electronic applications having graphical user interfaces configured to provide information to or receive information from users. In some embodiments, user interface subsystem 124 may include or communicate with display subsystem 126. For example, one or more test results or other displays may be presented to the user via user interface subsystem 124 or display subsystem 126. It should be noted that although sensing subsystem 120, identification subsystem 122, user interface subsystem 124, and display subsystem 126 are shown in FIG. 1 within a single client device 104a, this is not intended to be limiting. For example, each subsystem may exist together or separately within one or more client device(s) 104.

[031] In some embodiments, identification subsystem 110 or identification subsystem 122 may identify a chemical associated with a reaction in the sensing device using a machine learning model. FIG. 5 shows a machine learning model 500 configured to facilitate sensitive, repeatable, and reliable chemical sensing, in accordance with one or more embodiments. As an example, the machine learning model may include one or more neural networks, although the techniques described in this disclosure are not limited to any particular machine learning model or algorithm. Neural networks may be advantageous in at least certain embodiments because neural networks may be based on a large collection of neural units (or artificial neurons). Neural networks may loosely mimic the manner in which a biological brain works (e.g., via large clusters of biological neurons connected by axons). Each neural unit of a neural network may be connected with many other neural units of the neural network. Such connections can be enforcing or inhibitory in their effect on the activation state of connected neural units. In some embodiments, each individual neural unit may have a summation function which combines the values of all its inputs together. In some embodiments, each connection (or the neural unit itself) may have a threshold function such that the signal must surpass the threshold before it propagates to other neural units. These neural network systems may be self-learning and trained, rather than explicitly programmed, and can perform significantly better in certain areas of problem solving, as compared to traditional computer programs. In some embodiments, neural networks may include multiple layers (e.g., where a signal path traverses from front layers to back layers). In some embodiments, back propagation techniques may be utilized by the neural networks, where forward stimulation is used to reset weights on the “front” neural units. In some embodiments, stimulation and inhibition for neural networks may be more free flowing, with connections interacting in a more chaotic and complex fashion.

[032] In some embodiments, the prediction model may update its configurations (for example, weights, biases, or other parameters) based on its assessment of the predictions. Database 130 (e.g., as shown in FIG. 1) may include training data and one or more trained prediction models.

[033] As an example, with respect to FIG. 5, machine learning model 502 may take inputs 504 and provide outputs 506. For example, in some embodiments, inputs 504 may comprise training data comprising reactions based on chemical sensitivities (e.g., changes in resistance). In some embodiments, inputs 504 may include labels indicating chemicals associated with the reactions. In this example, outputs 506 may comprise predictions based on the training data. For example, the predictions may comprise predicted chemicals associated with the reactions in the training data. In one use case, outputs 506 may be fed back (for example, active feedback) to machine learning model 502 as input to train machine learning model 502 (e.g., alone or in conjunction with user indications of the accuracy of outputs 506, labels associated with the inputs, or with other reference feedback information). In another use case, machine learning model 502 may update its configurations (e.g., weights, biases, or other parameters) based on its assessment of its prediction (e.g., outputs 506) and reference feedback information (e.g., user indication of accuracy, reference labels, or other information). In another use case, where machine learning model 502 is a neural network, connection weights may be adjusted to reconcile differences between the neural network’s prediction and the reference feedback. In a further use case, one or more neurons (or nodes) of the neural network may require that their respective errors are sent backward through the neural network to them to facilitate the update process (e.g., backpropagation of error). Updates to the connection weights may, for example, be reflective of the magnitude of error propagated backward after a forward pass has been completed. In this way, for example, the machine learning model 502 may be trained to generate better predictions.

[034] In some embodiments, machine learning model 500 may be located on client device 104, computer system 102, or another location in network 150. For example, machine learning model 500 may be trained locally on client device 104, remotely on computer system 102, or in both locations. For example, machine learning model 500 may initially be trained remotely using datasets retrieved from database 130. Machine learning model 500 may then be used for fluid tests of the user once it has been trained. In some embodiments, machine learning model 500 may be further trained locally on client device 104 using fluid tests conducted by the user. Machine learning model 500 may thus improve its predictions of associated chemicals as the user conducts fluid tests. In some embodiments, updates to machine learning model 500 may be uploaded to computer system 102 when client device 104 is connected to network 150. In some embodiments, machine learning model 500 may be continuously updated using fluid tests conducted by multiple users with multiple client devices 104a-104n.

[035] System 100 may implement any embodiments described herein or in relation to U.S. Provisional Application No. 62/979834, entitled “Graphene-Based Chemical Sensing Device and System,” filed February 21, 5020, which is hereby incorporated by reference in its entirety.

[036] In some embodiments, system 100 may increase accuracy of the processes described herein by measuring resistivity amplified by applied mechanical strain at a known frequency. Piezoresistivity may describe a change of resistance in a semiconductor due to applied stress. For example, in semiconducting materials (e.g., germanium, poly crystalline silicon, amorphous silicon, silicon carbide, and single crystal silicon), changes in inter-atomic spacing responding from strain affect bandgaps (e.g, energy ranges in a solid where no electronic states can exist). This makes it easier for electrons to be raised into the conduction band of such solids. This results in a change in resistivity of the material. Within a certain range of strain, the relationship between the strain and the change of resistivity is linear. The piezoresistive coefficient, r_s, is therefore defined as: r_s = — (-) . where dp is change in resistivity, p is original resistivity, and e is strain. The piezoresistive coefficient of semiconducting materials can be several orders of magnitude larger than the geometrical effect of the strain. Semiconductor strain gauges with a very high coefficient of sensitivity can thus be built. Applying this same principle, by measuring changes in resistivity of a layer of functionalized graphene under mechanical strain, a highly sensitive chemical sensor can be built, since the changes in resistivity and rheological properties of the graphene composite are proportional to the number of target molecules bonded to the functional groups in the graphene.

[037] For example, FIG. 6 shows an exposed measurement structure 600, in accordance with one or more embodiments. In some embodiments, exposed measurement structure 600 may include aluminum nitride bimorphs 602, graphene layer 604, one or more vias 606, and other components, as shown in FIG. 6. In some embodiments, various components shown in FIG. 6 may correspond to components shown in FIGS. 4 and 5. In some embodiments, exposed measurement structure 600 may include a cavity 608, which may allow graphene layer 604 to vibrate using aluminum nitride bimorphs 602. In some embodiments, a voltage and a frequency may be applied to exposed measurement structure 600. Resistance may be measured across exposed measurement structure 600 as the applied voltage and frequency are varied. In some embodiments, resistivity may be measured based on variations in the applied voltage and piezoresistivity may be measured based on variations in the applied frequency. The shift in resistance (e.g.. measured in ppm) may produce a feature vector. In some embodiments, the feature vector may be used to classify one or more chemicals in a fluid sample.

[038] For example, a fluid sample may pass through or over exposed measurement structure 600. In some embodiments, as the fluid sample passes through the sensing device, the fluid sample may come into contact with various components of the sensing device. For example, the fluid sample may come into contact with the graphene coating. In some embodiments, certain chemicals (e.g., DNA strands) in the fluid sample may bond with the graphene. In some embodiments, the reactions between chemicals in the fluid sample and the graphene of the sensing device may depend on the type of chemical sensitivity of the particular sensing units. For example, the fluid sample may cause different reactions with the sensing units based on the chemical functionality dopants applied to that particular sensing unit. In some embodiments, the sensing units for a particular sensing device may be selected for a particular application. For example, when testing for a particular chemical (e.g., chemical X), sensing units which react with chemical X (e.g., due to the chemical functionality dopants applied to those sensing units) may be selected for the sensing device.

[039] In some embodiments, sensing subsystem 120 may measure resistance of each structure while voltage and frequency are applied to the structures. For example, an ADC (e.g., ADC 456, as shown in FIG. 4) may measure the piezoresistivity and resistivity of each sensing unit while the fluid sample is passing through the sensing device or after the fluid has passed through the sensing device. In some embodiments, a reaction between chemicals in the fluid sample and the graphene of the sensing device may cause a change to the piezoresistivity or resistivity of a particular sensing unit. For example, a reaction of graphene with a particular chemical may cause structures within the graphene to break down or may cause molecules within the fluid sample to attach to graphene layer 604, thereby changing the piezoresistivity or resistivity of the graphene. For example, graphene layer 604 may stretch as stress is applied, and the amount that graphene layer 604 stretches may depend on the frequency at which the stress is applied or the resistance of the graphene layer 604 (e.g., due to the stretching of the graphene layer or due to molecules attaching to the graphene layer). Stress may be applied at various frequencies applied by piezoelectric plate in order to allow the sensing system to detect chemicals that are accessible at those frequencies (e.g., as discussed in greater detail in relation to FIG. 8). The ADC may detect a particular sensing unit which has reacted with the fluid sample. In some embodiments, the ADC may convert the resistance measurements into digital signals. In some embodiments, information relating to the measurements may be processed at client device 104 or may be sent to computer system 102 for processing. For example, information relating to voltage, frequency, changes in piezoresistivity or resistivity, fluid samples, and chemical sensitivities of sensing units which reacted to the fluid samples may be processed by identification subsystem 122 of client device 104 or identification subsystem 110 of computer system 102.

[040] In some embodiments, based on one or more chemical sensitivities of the particular sensing unit, identification subsystem 110 or identification subsystem 122 may identify one or more chemicals in a fluid sample associated with a reaction in the sensing device. For example, if identification subsystem 122 identifies the chemical locally at client device 104, identification subsystem 122 may compare the sensing units which reacted to the fluid sample to a remote or locally-stored database. For example, identification subsystem 122 may compare the chemical sensitivities (e.g., based on the chemical functionality dopants applied to the sensing unit), the reaction to the fluid sample (e.g., changes in resistance), and other information about the sensing unit to a remote or locally-stored database. The databases may comprise entries having chemical sensitivities, reactions (e.g., changes in resistance), associated chemicals, and other information. For example, identification subsystem 122 may compare chemical sensitivities and a resistance measurement of sensing unit 400, as shown in FIG. 4, to the one or more databases. Identification subsystem 122 may identify a match for the properties and changes of in piezoresistivity and resistivity in one or more database. The database entry may additionally comprise an identification of the chemical or chemicals in the fluid sample which caused the reaction with the sensing unit. Identification subsystem 122 may thereby identify the chemical in the fluid sample.

[041] FIG. 7 shows simultaneous excitation 700 of an isolated reference structure 702 and an exposed measurement structure 704, in accordance with one or more embodiments. In some embodiments, system 100 may improve repeatability and manufacturing variability of the processes described herein by simultaneously exiting an isolated reference structure (e.g., isolated reference structure 702) and an exposed measurement structure (e.g., exposed measurement structure 704). In some embodiments, exposed measurement structure 704 may correspond to exposed measurement structure 600, as shown in FIG. 6.

[042] FIG. 8 shows a stress function 800 with an isolated reference structure 802 and an exposed measurement structure 804, in accordance with one or more embodiments. In some embodiments, isolated reference structure 802 may correspond to isolated reference structure 702, as shown in FIG. 7, and exposed measurement structure 804 may correspond to exposed measurement structure 600, as shown in FIG. 6, or exposed measurement structure 704, as shown in FIG. 7. Stress function 800 shows stress as a function of time with dynamic (e.g., sinusoidal) applied strain. In some embodiments, the stress function reflects the frequency response from both isolated reference structure 802 and an exposed measurement structure 804, which are excited simultaneously. In some embodiments, a modulus of graphene and composite graphene (e.g., graphene with functional groups) may be extracted from the frequency response. The change in moduli are proportional to the attachments or coatings or dopants on the graphene. This modulus may be used to create a repeatable structure, for example, by verifying functional density and by matching quantified functional density to a specific sensor response. In some embodiments, this may improve repeatability and manufacturing variability of the processes described herein. The modulus can also be used as an in-process quality measurement to ensure a repeatable composite graphene structure and to validate the change in resistivity measurements. Furthermore, the resonant frequencies are known to raise the temperature of the graphene, which can be used for refreshing the graphene to shed the detected species attached to the functional groups on the graphene and make the site available for the next attachment and or detection.

[043] In some embodiments, FIG. 8 illustrates stress applied and a resultant strain that amplifies resistivity. An ability to apply a range of stresses and oscillate at various frequencies may allow the sensing system to detect chemicals that are accessible at those frequencies. Additionally, given the ability to oscillate at various frequencies, the sensing system may be able to regenerate a sensor after measurements have been taken (e.g., heat- induced removal of the surface attachments or mechanical vibration-induced removal). In addition to the resistivity changes, the applied stress and strain response of the functional groups on the graphene surface (e.g., due to their mass) may also provide a way to detect species on the surface.

[044] FIG. 9 shows a plane view of a sensor 900, in accordance with one or more embodiments. In some embodiments, sensor 900 may correspond to exposed measurement structure 600, as shown in FIG. 6. In some embodiments, sensor 900 may include graphene 902, aluminum nitride bimorph 904, functionalized ssDNA/metal oxide 906, CR/Au electrodes 908, cavity 910, and any additional components. In some embodiments, the structure of sensor 900, as shown in FIG. 9, maximizes the predictable formation of graphene defects. For example, this structure has an increased number of edges with a known perimeter, which creates probable sites for functional groups to attach repeatedly and reliably and enables control over a number of functional groups that can be attached (e.g., functional density). To an extent this preserves the graphene surface from functionalization, preserving the quality of electrical conduction over the surface and thereby allowing sensor 900 to obtain additional feature vectors for classifying chemicals.

[045] FIG. 10 is an example flowchart of processing operations of methods that enable the various features and functionality of the system as described in detail above. The processing operations of each method presented below are intended to be illustrative and non-limiting. In some embodiments, for example, the methods may be accomplished with one or more additional operations not described, and/or without one or more of the operations discussed. Additionally, the order in which the processing operations of the methods are illustrated (and described below) is not intended to be limiting.

[046] In some embodiments, the methods may be implemented in one or more processing devices (e.g., a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and/or other mechanisms for electronically processing information). The processing devices may include one or more devices executing some or all of the operations of the methods in response to instructions stored electronically on an electronic storage medium. The processing devices may include one or more devices configured through hardware, firmware, and/or software to be specifically designed for execution of one or more of the operations of the methods.

[047] FIG. 10 shows a flowchart 1000 of a method of facilitating chemical sensing, in accordance with one or more embodiments. In an operation 1002, a fluid sample may be received at a sensing device having one or more chemical sensitivities. Operation 1002 may be performed by a subsystem that is the same as or similar to sensing subsystem 120. In an operation 1004, voltage and pressure may be applied to the sensing device. Operation 1004 may be performed by a subsystem that is the same as or similar to sensing subsystem 120. [048] In an operation 1006, a reaction of the sensing device to a chemical in the fluid sample may be detected. In some embodiments, the reaction may be detected based on the one or more chemical sensitivities and the applied voltage and pressure. Operation 1006 may be performed by a subsystem that is the same as or similar to sensing subsystem 120. In an operation 1008, the chemical in the fluid sample associated with the reaction of the sensing device may be identified. Operation 1008 may be performed by a subsystem that is the same as or similar to identification subsystem 110 or identification subsystem 122.

[049] In some embodiments, the various computers and subsystems illustrated in FIG. 1 may include one or more computing devices that are programmed to perform the functions described herein. The computing devices may include one or more electronic storages (e.g., database(s) 130 or other electronic storages), one or more physical processors programmed with one or more computer program instructions, and/or other components. The computing devices may include communication lines or ports to enable the exchange of information within a network (e.g., network 150) or other computing platforms via wired or wireless techniques (e.g., Ethernet, fiber optics, coaxial cable, Wi-Fi, Bluetooth, near field communication, or other technologies). The computing devices may include a plurality of hardware, software, and/or firmware components operating together. For example, the computing devices may be implemented by a cloud of computing platforms operating together as the computing devices.

[050] The electronic storages may include non-transitory storage media that electronically stores information. The storage media of the electronic storages may include one or both of (i) system storage that is provided integrally (e.g., substantially non-removable) with servers or client devices or (ii) removable storage that is removably connectable to the servers or client devices via, for example, a port (e.g., a USB port, a firewire port, etc.) or a drive (e.g., a disk drive, etc.). The electronic storages may include one or more of optically readable storage media (e.g., optical disks, etc.), magnetically readable storage media (e.g., magnetic tape, magnetic hard drive, floppy drive, etc.), electrical-charge-based storage media (e.g., EEPROM, RAM, etc.), solid-state storage media (e.g., flash drive, etc.), and/or other electronically readable storage media. The electronic storages may include one or more virtual storage resources (e.g., cloud storage, a virtual private network, and/or other virtual storage resources). The electronic storage may store software algorithms, information determined by the processors, information obtained from servers, information obtained from client devices, or other information that enables the functionality as described herein.

[051] The processors may be programmed to provide information processing capabilities in the computing devices. As such, the processors may include one or more of a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and/or other mechanisms for electronically processing information. In some embodiments, the processors may include a plurality of processing units. These processing units may be physically located within the same device, or the processors may represent processing functionality of a plurality of devices operating in coordination. The processors may be programmed to execute computer program instructions to perform functions described herein of subsystems 120-126, subsystem 110, and/or other subsystems. The processors may be programmed to execute computer program instructions by software; hardware; firmware; some combination of software, hardware, or firmware; and/or other mechanisms for configuring processing capabilities on the processors.

[052] It should be appreciated that the description of the functionality provided by the different subsystems 120-126 and subsystem 110 described herein is for illustrative purposes, and is not intended to be limiting, as any of subsystems 120-126 or subsystem 110 may provide more or less functionality than is described. For example, one or more of subsystems 120-126 or subsystem 110 may be eliminated, and some or all of its functionality may be provided by other ones of subsystems 120-126 or subsystem 110. As another example, additional subsystems may be programmed to perform some or all of the functionality attributed herein to one of subsystems 120-126 or subsystem 110.

[053] Although the present invention has been described in detail for the purpose of illustration based on what is currently considered to be the most practical and preferred embodiments, it is to be understood that such detail is solely for that purpose and that the invention is not limited to the disclosed embodiments, but, on the contrary, is intended to cover modifications and equivalent arrangements that are within the scope of the appended claims. For example, it is to be understood that the present invention contemplates that, to the extent possible, one or more features of any embodiment can be combined with one or more features of any other embodiment.

[054] The present techniques will be beher understood with reference to the following enumerated embodiments:

1. A system for sensing chemicals, the system comprising: a computer system that comprises one or more processors programmed with computer program instructions that, when executed, cause the computer system to: receive, at a sensing device having one or more chemical sensitivities, a fluid sample; apply, to the sensing device, stress; detect, based on the one or more chemical sensitivities of the sensing device and the applied stress, a reaction of the sensing device to a chemical in the fluid sample; and identify the chemical in the fluid sample associated with the reaction of the sensing device.

2. The system of embodiment 1, wherein the control circuitry is further configured to apply, to the sensing device, motion.

3. The system of embodiment 2, wherein the motion is applied at one or more resonance frequencies.

4. The system of embodiment 3, wherein the reaction comprises a change in resistivity or piezoresistivity amplified by the applied stress and motion and the one or more resonance frequencies associated with a chemical sensitivity of the one or more chemical sensitivities.

5. The system of embodiment 4, wherein the reaction comprises a change in the one or more resonance frequencies.

6. The system of any of embodiments 1-5, wherein the computer system is further caused to: provide a reaction based on a chemical sensitivity and applied stress as input to a neural network to cause the neural network to generate a predicted associated chemical; obtain feedback indicating an associated chemical; and provide the feedback as reference feedback to the neural network to cause the neural network to assess the feedback against the predicted associated chemical, the neural network being updated based on the assessment of the feedback.

7. The system of embodiment 6, wherein the chemical in the fluid sample associated with the reaction of the sensing device is identified using the updated neural network.

8. A method being implemented by one or more processors executing computer program instructions that, when executed, perform the method comprising any of embodiments 1-7.

9. A tangible, non-transitory, machine-readable medium storing instructions that, when executed by a data processing apparatus, causes the data processing apparatus to perform operations comprising those of any of embodiments 1-7.

Claims

WHAT IS CLAIMED IS:

2. The system of claim 1, wherein the control circuitry is further configured to apply, to the sensing device, motion.

3. The system of claim 2, wherein the motion is applied at one or more resonance frequencies.

4. The system of claim 3, wherein the reaction comprises a change in resistivity or piezoresistivity amplified by the applied stress and motion and the one or more resonance frequencies associated with a chemical sensitivity of the one or more chemical sensitivities.

5. The system of claim 4, wherein the reaction comprises a change in the one or more resonance frequencies.

6. The system of claim 1, wherein the computer system is further caused to: provide a reaction based on a chemical sensitivity and applied stress as input to a neural network to cause the neural network to generate a predicted associated chemical; obtain feedback indicating an associated chemical; and provide the feedback as reference feedback to the neural network to cause the neural network to assess the feedback against the predicted associated chemical, the neural network being updated based on the assessment of the feedback.

7. The system of claim 6, wherein the chemical in the fluid sample associated with the reaction of the sensing device is identified using the updated neural network.

8. A method for sensing chemicals, the method comprising: receiving, at a sensing device having one or more chemical sensitivities, a fluid sample; applying, to the sensing device, stress; detecting, based on the one or more chemical sensitivities of the sensing device and the applied stress, a reaction of the sensing device to a chemical in the fluid sample; and identifying the chemical in the fluid sample associated with the reaction of the sensing device.

9. The method of claim 8, further comprising applying, to the sensing device, motion.

10. The method of claim 9, wherein the motion is applied at one or more resonance frequencies.

11. The method of claim 10, wherein the reaction comprises a change in resistivity or piezoresistivity amplified by the applied stress and motion and the one or more resonance frequencies associated with a chemical sensitivity of the one or more chemical sensitivities.

12. The method of claim 11, wherein the reaction comprises a change in the one or more resonance frequencies.

13. The method of claim 8, further comprising: providing a reaction based on a chemical sensitivity and applied stress as input to a neural network to cause the neural network to generate a predicted associated chemical; obtaining feedback indicating an associated chemical; and providing the feedback as reference feedback to the neural network to cause the neural network to assess the feedback against the predicted associated chemical, the neural network being updated based on the assessment of the feedback.

14. The method of claim 13, wherein the chemical in the fluid sample associated with the reaction of the sensing device is identified using the updated neural network.