WO2026019644A1 - High throughput systems for detection of analytes - Google Patents

Abstract

A system far detecting an analyte in a sample liquid includes a printed circuity board including a plurality of electrochemical sensors positioned at unique positions. The plurality of electrochemical sensors include a substrate and a sensor medium on the substrate between spaced electrodes. The sensor medium includes at least one nanostructure. The sensor further includes an automated system including liquid handling system, an electronic test system, and a control system comprising a processor system and a memory system in connection with the processor system. The printed circuit board is further configured to be placed in electrical connection, with the electronic test system and the control system. The control system is configured to save in. the memory system a unique position in a coordinate system of each of the plurality of electrochemical sensors when the printed circuit board in placed in connection with the electronic test system and the control system.

Description

HIGH THROUGHPUT SYSTEMS FOR DETECTION OF ANALYTES

GOVERNMENTAL INTEREST

[0001 ] This invention was made with government support under grant number HDTRA1-21-1- 0009 awarded by the .Department of .Defense-Defense Threat Reduction Agency. The government has certain rights in the invention.

CROSS-REFERENCE TO RELATED APPLIC ATIONS

[0002] This application claims benefit of U.S. Provisional Patent Application Serial No, 63/671,300, filed July 15, 2024, the disclosure of which is incorporated herein by reference.

BACKGROUND

[0003] The following information is provided to assist the reader in understanding technologies disclosed below and the environment in which such technologies may typically be used. The terms used herein are not intended to be limited to any particular narrow interpretation unless clearly stated otherwise in this document. References set forth herein may facilitate understanding of the technologies or the background thereof. The disclosure of all references cited herein are incorporated by reference.

[0004] Laboratory automation techniques have greatly changed chemical analysis in the Iasi few decades. Various automated instrumental systems have been introduced to improve the efficiency of work and analytical performance by replacing manual operations with machines, to standardize ail operations and avoid manual errors. Laboratory automation techniques were first developed for conventional instruments. Modern chromatography, spectroscopy, and mass spectrometry instruments, for example, include automated sample handling and injection capabilities. However, the development of miniaturization and high-throughput analysis in analytical chemistry with emerging analytical methods introduces significant challenges to laboratory automation techniques.

[0005] Rapid response, good portability, and higher integrity make electrochemical instruments favorable for high-throughput analysis with laboratory automation techniques, which have been reported in different research works with different electrochemical methods such as cyclic voltammetry, chroiioamperometry, and differential pulse voltammetry. Electrochemical testing can be easily run in batch automatically with custom software or self-developed programs by coding, and the good portability of electrochemical instruments also made them expandable to be incorporated with other chemical instruments for different research purposes with laboratory automation techniques. Specifically, several open-source programs have been developed by researchers for automation of electrochemical tests.

[0006] Field-effect transistor-based (FET) sensors are electrochemical sensors used widely for different chemical and biological sensing applications, such as environmental monitoring, food analysis, and health screening. With many emerging applications of FET sensors, it is desirable to automate FET tests to achieve high-throughput screening. While FET sensors appear to provide many opportunities for high-throughpnt screening, many challenges must be overcome in real applications. For example, FET sensors may have different architectures (for example, top-gate FET, back-gate FET, electrolyte-gate FET), and different experimental setups maybe required for the coupling between FET sensors and electrical and electronic measuring equipment Moreover, different sample treatments must be considered.

[0007] Automated FET sensing systems have been studied for gas and vapor sensing, which were mainly based on monitoring of conductance changes from different sensor units. Compared with gas sensing based on back-gate FET mode, electrolyte-gate FET is usually more widely used in biological sensing since the sensing of biomolecules is commonly processed in aqueous biological matrices. Schemes used in automated gas sensing systems cannot be directly applied to an electrolyte-gate FET or other electrochemical sensors.

[0008] It is very desirable to develop technologies for the high-throughput analysis of liquid samples using electrochemical sensors. Il is, for example, desirable to develop technologies for high-throughput analysis of aqueous biological matrices. Such high-throughput analysis may, for example, be used to fulfill the testing requirements created by the rapid growth in the need for analysis of biological samples (for example, as a result of opioid use associated with the ongoing opioid abuse epidemic).

SUMMARY

[0009] A system for detecting an analyte in a sample liquid includes a printed circuity board including a plurality of electrochemical sensors positioned at unique positions on the printed circuitry board and in electrical connection therewith. Each of the plurality of electrochemical sensors include a substrate and a sensor medium on the substrate (deposited or positioned) between spaced electrodes. The sensor medium includes at least one nanostructure. At least one properly (for example, an electrical property) of the sensor medium is sensitive to the composition of a volume of the sample liquid on a surface of the sensor medium. The sensor further includes an automated system including a liquid handling system, an electronic test system, and a control system comprising a processor system and a memory system in connection with the processor system. The control system is in communicative connection with the liquid handling system and with the electronic test system. The printed circuit board is further configured to be placed in electrical connection with the electronic test system and the control system. The control system is configured to save in the memory system a unique position in a coordinate system of each of the plurality of electrochemical sensors when the printed circuit board in placed in connection with the electronic test system and the control system. The plurality of electrochemical sensors may include electrochemical sensors having different sensor media.

0 [0010] The liquid handling system may include a robotic system. In a number of embodiments, the robotic system includes a deck comprising a slot to position the printed circuit board thereon and one or more robotic arms. At least one of the one or more robotic arms may be configured to deliver liquids including the volume of the sample liquid independently to each of the plurality of electrochemical sensors of the printed circuit board under control of the control system. The electronic test system may be configured to be independently connected with each of the plurality of electrochemical sensors of the printed circuit board under control of the control system. The electronic test system may be further configured to independently apply electrical energy to each of the plurality of electrical sensors to create a voltage (for example, a bias voltage) between the spaced electrodes thereof, and to independently measure a response of each of the plurality of electrochemical sensors to the applied electrical energy.

[001 1] The electronic test system may include a source meter in connection with the control system and with the printed circuit board. The source meter may be configured to independently apply the electrical energy to each of the plurality of electrical sensors to create the bias voltage between the spaced electrodes thereof, and to independently measure the response of each of the plurality of electrochemical sensors to the applied electrical energy. The electronic test system may further include a system switch configured to switch independently between connections with each of the plurality of electrochemical sensors of the printed circuit board under control of the control system.

[Q012] In a number of embodiments, the liquid handling system is controlled via a first software algorithm stored in the memory system and executable by the processor system, and the electronic test system is controlled via a second software algorithm stored in rhe memory system and executable by the processor system. Each of the first software algorithm and the second software algorithm may be configured to send requests to the control system. The control system may be configured to pause or actuate one of the first software algorithm and the second software algorithm based upon the requests. In a number of embodiments, the memory' system of the control system include a web server software algorithm stored therein and executable by the processor system to exchange information with the first software algorithm and the second software algorithm.

[0013] In a number of embodiments, at least one of the one or more robotic arms is configured to position a pipette using a coordinate system (setting for three-dimensional positions) to at least one of (i) deliver one of the liquids to a determined electrochemical sensor or (ii) aspirate liquid from the determined electrochemical sensor, hi a number of embodiments, the electrochemical sensor in divided into multiple sections for separate aspiration.

[0014] The deck may include one or more slots at unique determined positions thereof. Each of the slots may be configured to connect a component thereto from the group consisting of a component including a source of the sample liquid, a component including a source of an electrolyte, a component including a source of pipettes, a component including a holder for a gate electrode, a component including a solid waste container, and a component including a liquid waste container. The location of all components io be used by the one or more robotic arms and the position at which such components are to be used in connection with each electrochemical sensors may be defined in three dimensions using the coordinate system (for exampie, a cartesian coordinate system.)

[0015] In a number of embodiments, each of the plurality of electrochemical sensors includes a liquidgated field effect transistor electrochemical sensor At least one of the one or more robotic arms may be configured to position a gate electrode to be in connection with the volume of the sample liquid on the surface of the sensor medium of a determined one of the plurality of electrochemical sensors. The electronic tests system may be configured, under control of the control system, to apply a gate voltage to the gate electrode. A plurality of features may be determined from the response of each of the plurality of electrochemical sensors via an analysis algorithm to determine the analyte, Tire analysis algorithm may include one or more artificial intelligence algorithms (for example, a machine learning a.lgoritlim).The one or more machine learning artificial intelligence algorithms may be configured to Speciate between a plurality of analytes. In a number of embodiments, the analyte is at least one of an op ioid or a metabol ite of an opioid.

[0016] The nanostructures may include carbon nanostructures. The carbon nanostructures may be single- walled carbon nanotubes. In a number of embodiments, the single-walled carbon nanotubes are semiconductor enriched single-walled carbon nanotubes.

[0017] The nanostructures of the sensor medium of the plurality of electrochemical sensors may include at least one of bare nanostructures and decorated nanostructures. The decorated nanostructures may include nanostructures decorated with at least one of metal nanoparticles, metal oxide nanoparticles, and receptors for at least one analyte of the one or more analytes (for example, opioids or metabolites of opioids). In a number of embodiments, the metal nanoparticles are selected from the group of gold Au, platinum Pt, palladium Pd, silver Ag, titanium Ti, copper Cu, iron Fe, nickel Ni, iridium Ir, and zinc Zn.

[0018] The plurality of electrochemical sensors include a field-effect transistor electrochemical sensor. In a number of embodiments, each of the plurality of electrochemical sensors includes a liquid-gated field-effect transistor electrochemical sensor, A gate electrode of the liquid-gated field-effect transistor electrochemical sensor of each of the plurality of electrochemical sensors may be a reference electrode. The reference electrode may be an AgZAgCl reference electrode, a Pt reference electrode, or a pseudo reference electrode.

[0019] A method for detecting an analyte in a sample liquid includes providing an automated system including a liquid handling system, an electronic test system, and a control system including a processor system and a memory system in connection with the processor system, wherein the control system being configured to be in communicative connection with the liquid handling system and with the electronic test system. The method further includes providing a printed circuity board including a plurality of electrochemical sensors positioned at unique positions on the printed circuity board and in electrical connection therewith. Each of the plurality of electrochemical sensors includes a substrate and a sensor medium on the substrate positioned between spaced electrodes. The sensor medium includes at least one nanostructure, wherein at least one properly of the sensor medium is sensitive to the composition of the volume of the sample liquid on a s tirface of the sensor medium, The method also inc ludes plac ing the printed circuity board in connection with the control system and with the electronic test system, applying a volume of the liquid sample via the automated liquid handling system separately to a set of one or more of the plurality of electrochemical sensors, and measuring a response of each one of the set of electrochemical sensors via the electronic test system. The plurality of electrochemical sensors may include electrochemical sensors having different sensor mediums.

[0020] As described above, in a number of embodiments, the liquid handling system includes a. robotic system. The robotic system includes a deck comprising a slot to position the printed circuit, board thereon and one or more robotic arms At least, one of the one or more robotic arms is configured to deliver liquids including the volume of the sample liquid independently to each of the plurality of electrochemical sensors of the printed circuit board under control of the control system.

[0021] The electronic test system may be configured to be independently connected with each of the plurality of electrochemical sensors of the printed circuit board under control of the control system. The method may further include independently applying electrical energy to each of the plurality of electrical sensors to create a bias voltage between the spaced electrodes thereof via the electronic test system, and independently measuring a response of each of the plurality of electrochemical sensors to the applied electrical energy via the electronic test system.

[0022] In a number of embodiments, the electronic test system .includes a source meter in connection with the control system and with the printed circuit board. The source meter is configured to independently apply the electrical energy to each of the plurality of electrical sensors to create the voltage (or bias voltage) between the spaced electrodes thereof, and to independently measure the response of each of the plurality of electrochemical sensors to the applied electrical energy,

[0023] The electronic test system may further include a system switch configured to switch independently between connections with each of the plurality of electrochemical sensors of the printed circuit board under control of the control system.

[0024] In a number of embodiments., the liquid handling system is controlled via a first software algorithm stored in the memory system: and executable by the processor system, and the electronic test system is controlled via a second software algorithm stored tn the memory system and executable by the processor system. Each of the first software algorithm and the second software algorithm may be configured to send requests to the control system, and the control system may be configured to pause or actuate one of the first software algorithm and the second software algorithm based upon the requests. In a number of embodiments, the memory system of the control system includes a web server software algorithm stored therein and executable by the processor system to exchange information with the first software algorithm and the second software algorithm.

[0025] At least one of the one or more robotic arms may be controlled via the control system to position a pipette using a coordinate system to at least one of (i) deliver one of the liquids to a determined electrochemical sensor or (ii) aspirate liquid from the determined electrochemical sensor.

[0026] In a number of embodiments, the deck includes one or more slots at unique determined positions thereof, each of the slots being configured to connect a component thereto from the group consisting Of a component including a source of the sample liquid, a component including a source of an electrolyte, a component including a source of pipettes, a component including a holder for a gate electrode, a component including a solid waste container, and a component including a liquid waste container.

[0027] Each of the plurality of electrochemical sensors may, for example, include a liquid-gated field effect transistor electrochemical sensor. At least one of the one or more robotic arms may be controlled via the control system to position a gate electrode to be in connection with the volume of the sample liquid on the surface of the sensor medium of a determined one of the plurality of electrochemical sensors. The electronic tests system may be controlled via the control system io apply a gate voltage to the gate electrode.

[0028] The method may further include determining a plurality of features from the response of each of the plurality of electrochemical sensors via an analysis algorithm to determine the analyte. The analysis algorithm optionally include one or more artificial intelligence algorithms (for example, one or more machine learning algorithms) .The one or more artificial intelligence algorithms, which may include one or more machine learning algorithms, may be configured to specials between a plurality of analytes. In a number of embodiments, the analyte is at least one of an opioid or a metabolite of an opioid.

[0029] The nanostructures may include carbon nanostructures. The carbon nanostructures may be single- walled carbon nanotubes. In a number of embodiments, the single- walled carbon nanotubes are semiconductor enriched single-walled carbon nanotubes.

[0030] The nanostructures of the sensor medium of the plurality of electrochemical sensors may include one of bare nanostructures and decorated nanostructures. The decorated nanostructures may include nanostructures decorated with at least one of metal nanoparticles, metal oxide nanoparticles, and receptors for at least one analyte of the one or more analytes, The metal nanoparticles may be selected from the group of gold Au, platinum Pt, palladium Pd, silver Ag, titanium Ti, copper Cu, iron Fe, nickel N i, iridium lr, and zinc Zn,

[0031 ] In a number of embodiments, each of the plurality of electrochemical sensors includes a fieldeffect transistor. Each of the plurality of electrochemical sensors may include a liquid-gated field-effect transistor electrochemical sensor. The gate electrode (which is controlled via one of the one or more robotic arms) of the liquid-gated field-effect, transistor electrochemical sensor of each of the plurality of electrochemical sensors may be a reference electrode. The reference electrode may be an Ag/AgCl reference electrode, a Pt reference electrode, or a pseudo reference electrode.

[0032] A method for detection of one or more analytes in a liquid sample includes providing a plurality of electrochemical sensors. Each of the electrochemical sensors includes a substrate and a sensor medium on the substrate between spaced electrodes. The sensor medium includes at least one nanostructure, wherein at least one property of the sensor medium is sensitive to the composition of a volume of the liquid sample on a surface of the sensor medium. The plurality of electrochemical sensors include electrochemical sensors having different sensor mediums. The method further includes placing a volume of the liquid sample over the sensor medium of each of a set of the plurality of electrochemical sensors, wherein the set of the plurality of electrochemical sensors including electrochemical sensors having different sensor mediums, independently applying a voltage across each of the electrochemical sensors of the set of the plurality of electrochemical sensors after placing the volume of the liquid sample on the sensor medium (hereof, measuring a response of each of the set of the plurality of electrochemical sensors to the applied voltage, and detecting at least one analyte of the one or more analytes in the liquid sample based upon an algorithm configured to analyze the responses of each of the set of the plurality of electrochemical sensors. The one or more analytes may be selected from the group consisting of opioids and metabolites of opioids.

[0033] The nanostructures may include carbon nanostructures. The carbon nanostructures may be single-walled carbon nanofabes. In a number of embodiments, the single-walled carbon nanotubes are semiconductor enriched single-walled carbon nanotubes,

[0034] The nanostructures of the sensor medium of the plurality of electrochemical sensors may include one of bare nanostructures and decorated nanostructures. The decorated nanostructures may include nanostructures decorated with at least one of metal nanoparticles, metal oxide nanoparticles, and receptors for at least one analyte of the one or more analytes. The metal nanopartieles may be selected from the group of gold Au, platinum Pt, palladium Pd, silver Ag, titanium Ti, copper Cu, iron Fe, nickel Ni, iridium Ir, and zinc Zn.

[0035] In a number of embodiments, each of the plurality of electrochemical sensors includes a fieldeffect transistor. Each of the plurality of electrochemical sensors may include a liquid-gated field-effect transistor electrochemical sensor. The gate electrode (which is controlled via one of the one or more robotic arms) of the liquid-gated field-effect transistor electrochemical sensor of each of the plurality of electrochemical sensors may be a reference electrode. The reference electrode may be an Ag/AgCl reference electrode., a Pt reference electrode, or a pseudo reference electrode.

[0036] In a number of embodiments, each of the plurality of electrochemical sensors is positioned at unique positions on a printed circuity board and is in electrical connection therewith. The method may further includes providing an automated system including liquid handling system, an electronic test system, and a control system Including a processor system and a memory system in connection with the processor system. The control system may be configured to be in communicative connection with the liquid handling system and with the electronic test system. The method further includes placing the printed circuity board in connection with the control system and with the electronic test system, applying the volume of the liquid sample via the liquid handling system separately to the set of one or more of the plurality of electrochemical sensors, and measuring a response of each one of the set of electrochemical sensors via the electronic test system.

[0037] The present devices, systems, and methods, along with the attributes and attendant advantages thereof, will best be appreciated and understood in view of the following detailed description taken in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

[0038] FIG.. 1A illustrates a schematic representation of an embodiment of an FET sensor device hereof.

[00391 FIG.. IB illustrates a schematic representation of an embodiment of a chemiresistor sensor device.

[0040] FIG. 2A illustrates a schematic representation of an embodiment of a printed circuit board including a plurality of nanostructure-based electrochemical sensor hereof.

[0041] FIG. 2B illustrates eight interdigitated electrodes on a representative embodiment of chip hereof, and sectioning of the chip into 25 sections (Al --- E5 ) for control of a waste removal process.

[0042] FIG. 3A illustrates schematically an embodiment of an automated electrolyte-gate FET test system hereof including a printed circuit board (PCB) including a sensor array thereon, a robot, a computer, a source meter unit, and a system switch, which may, for example, be used for high- throughput screening of samples for the detection of liquid samples including analytes such as opioids,

[0043] FIG. 3B illustrates a front view (yz plane) of a schematic representation of a portion of the automated system of FIG. 3 A including the PC B and the pipetting robot.

[0044] FIG. 3C illustrates a top view (xy plane) of a Cartesian coordinate system of the automated system of FIG. 3 A including the PCB. [0045] FIG. 3D illustrates a front view (yz plane) of a portion of the automated system of FIG. 3A illustrating placement of a gate electrode in operative connection with a liquid sample on a sensor device positioned on the PCB,

[0046] FIG. 3E illustrates an enlarged photograph of the gate electrode of FIG. 3C, which may be recognized as a pipette tip of the pipetting robot system.

[0047] FIG. 3F illustrates an embodiment of a manual test protocol and an automated test protocol used in studies hereof.

[0048] FIG. 4A illustrates an embodiment of a workflow of a field effect transistor (FET) test with the automated electrolyte-gate FET test system of FIG. 3A, wherein the workflow include of five actions or steps: (a) robot adds solution to a sensor; (b) robot makes gate electrode dipped in the liquid drop on the sensor; (c) sensor is connected to the source meter unit by closing the channel with system switch; (d) FET measurement; and (e) robot adds solution to another sensor and starts cycle.

[0049] FIG. 4B illustrates Table I, which sets forth commands in the workflow of FIG. 4A.

[0050] FIG. 4C illustrates Table 2, which sets forth software command in two scripts.

[0051 ] FIG. 5A illustrates drift of the source-drain current at -0.2 V after multiple tests in pH sensing test with a representative automated electrolyte-gate FET test system hereof, wherein there were 1 1 trials, and the FET tests were repeated 6 times in each trial, the same buffer (pH ^:::12) was applied for all 1 1 trials in the control group (black squares), and different buffers with pH values from 12 to 2 are applied for the 1 1 trials in the pH test group (gray circles).

[0052] FIG. 5B illustrates transfer characteristics of the pH sensor, (that is, the source-drain current (Ea) vs. the applied liquid gate voltage (V₈) in buffers) with different pH values from 12 to 2, wherein the drain current was measured by sweeping gate voltage (V_g) from -0.6 V to 0,6 V with a source-drain bias voltage ( V_Sij) of 0.05 V.

[0053] FIG. 5C illustrates conductance value at -0.2 V versus pH for the pH sensor, wherein black dots correspond to a common test and the grey circles correspond to a rapid test, wherein the error bars are calculated with the conductance values of 3 devices.

[0054] FIG. 6A illustrates a calibration plotted by the relative conductance changes at -0.2 V after the addition of fentanyl.

[0055] FIG. 6B illustrates a specificity test of fentanyl with an opioid drug FET sensor array with the automated system hereof, wherein the FET sensors were functionalized with target-specific antibodies. [0056] FIG. 6C illustrates a specificity test of hydrocodone with an opioid drug FET sensor array with the automated system hereof wherein the FET sensors were functionalized with target-specific antibodies.

[0057] FIG. 6D illustrates a specificity test of morphine with an opioid drug FET sensor array with the automated system hereof wherein the FET sensors were functionalized with target-specific antibodies.

[0058] FIG. 7 A illustrates chemical structures for codeine, fentanyl, hydrocodone, and morphine,

[0059] FIG. 7B illustrates an embodiment of a workflow of data analysis, as well as an architecture of a multi-path convolutional neural network model (CNN) used in deep learning.

[0060] FIG. 7C illustrates a representative embodiment of a flowchart of data analysis, including the collection and transformation of raw data and of the deep learning task with the CNN, with both U and Igs values,

DETAILED DESCRIPTION

[0061] It will be readily understood that the components of the embodiments, as generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations in addition to the described representative embodiments. Thus, the following more detailed description of the representative embodiments, as illustrated in the figures, is not intended to limit the scope of the embodiments, as claimed, but is merely illustrative of representative embodiments.

[0062] Reference throughout this specification to “one embodiment” or “an embodiment” for the like) means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, the appearance of the phrases “in one embodiment” or “in an embodiment” or the like in various places throughout this specification are not necessarily all referring to the same embodiment.

[0063] Furthermore, described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments, hi the following description, numerous specific details are provided to give a thorough understanding of embodiments. One skilled hi the relevant art will recognize, however, that the various embodiments can be practiced without one or more of the specific details, or with other methods, components, materials, ci cetera. In other instances, well known structures, materials, or operations are not shown or described in detail to avoid obfuscation.

[0064] As used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise. Thus, for example, reference to “a robotic arm” includes a plurality of such robotic arms and equivalents thereof known to those skilled in the art, and so forth, and reference to “the robotic arm” is a reference to one or more such robotic arms and eq uivalents thereof known to those skilled in the art and so forth . Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range. Unless otherwise indicated herein, each separate value, as well as intermediate ranges, are incorporated into the specification as if individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contraindicated by the text

[0065] The terms "‘electronic circuitry,” “circuitry” or “circuit,” as used herein include, but are not limited to, hardware, firmware, software, or combinations of each to perform a funetion(s) or an action(s). For example, based on a desired feature or need, a circuit may include a software controlled microprocessor, discrete logic such as an application specific integrated circuit (ASIC), or other programmed logic device. A circuit may also be fully embodied as software. As used herein, “circuit” is considered synonymous with “logic.” The term “logic,” as used herein includes, but is not limited to, hardware, firmware, software, or combinations of each to perform a function! s) or an action(s), or to cause a function or action from another component. For example, based on a desired application or need, logic may include a software controlled microprocessor, discrete logic such as an application specific integrated circuit (ASIC), or other programmed logic device. Logic may also be fully embodied as software.

[0066] The term "processor," as used herein includes, but is not limited to, one or more of virtually any number of processor systems or stand-alone processors, such as microprocessors, microcontrollers, central processing units (CPUs), and digital signal processors (DSPs), in any combination. The processor may be associated with various other circuits that support operation of the processor, such as random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read only memory (EPROM), clocks, decoders, memory controllers, or interrupt controllers, etc. These support circuits may be internal or external to the processor or its associated electronic packaging. The support circuits are in operative communication with theprocessor. The support circuits are not necessarily shown separate from theprocessor in block diagrams or other drawings.

[0067] The term “controller/’ as used herein includes, but is not limited to, any circuit or device that coordinates and controls the operation of one or more input and/or output devices. A controller may, for example, include a device having one or more processors, microprocessors, or central processing units capable of being programmed to perform functions.

[0068] The term “memory system” refers to one or more electronic components that store data and instructions. In computerized systems, a processor system can quickly access information stored in a memory system. Memory allows storage and retrieval of information and may, for example, include primary memory and secondary memory. Primary memory includes, for example, RAM, cache memory, etc. Secondary memory includes, for example, hard drives, hard disk drives etc. [006$)] The term “software,” as used herein includes, but is not limited to, one or more computer readable or executable instructions that cause a computer or other electronic device to perform functions, actions, or behave in a desired manner. The instructions may be embodied iti various forms such as routines, algorithms, modules, or programs including separate applications or code from dynamically linked libraries. Software may also be implemented in various forms such as a stand-alone program, a function call, a servlet, an applet, instructions stored in a memory, part of aa operating system or other type of executable instructions. It will be appreciated by one of ordinary skill in the art that the form of software is dependent on, for example, requirements of a desired application, the environment it rims on, or the desires of a designer, ■programmer or the like.

[0070] As used herein, and unless otherwise stated or otherwise clear from the context, terms such as generally or approximately when used in connection with, for example, a value, refer to a range of values with 10%, within 5%, or desirably with i% of the associated value. As used herein in the term "and/or” means one of or both of an entity. Thus, A and/or means A or B, or both A and B,

[0071] In a number of representative embodiments, integration of laboratory automation techniques into chemical analysis is discussed through use of electrochemical sensors including nanostructures (which, for example, may be readily miniaturized) to achieve high throughput testing. In such representative embodiments, an automated electrolyte-gate PET test system was used for rapid screening of multiple sensors. The automated system achieved precision control through individual programming of each instrument, followed by the synergistic integration of the instruments via software control (for example, using Python scripts). Studied embodiments of automated systems hereof performed FET measurements of 96 sensors in a single run, and different operations such as liquid transfer and waste removal were achieved. In representative examples, the automated system was evaluated by running a pH sensing test successfully and applied for opioid drug testing with high working efficiency and good accuracy, demonstrating that systems hereof provide a tool for diverse sensing applications based on electrochemical sensors such as electrolyte-gate FET sensors.

[0072] FET and other iianoslruc tare- based sensors may provide a number of advantages for automation. For example, FET sensor tests may readily be performed with a miniaturized electrochemical analyzer. The miniaturization and high integration of FET sensors may enable the construction of an entire FET sensing system (FET sensor, detector, and other circuits) on a single chip. Although the devices, systems, and method hereof are discussed primarily in connection with clectrolyte-gatc FET sensors, one skilled tn the art will appreciate that the devices, systems, and methods hereof may be used in connection with other sensors, and particularly electrochemical sensors.

[0073] Automation of laboratory processes is important in analytical chemistry. Such automation enhances experimental reproducibility by eliminating repetitive tasks and reducing human errors. The automated systems hereof provide high-resolution control, as well as rapid response and large capacity for higli-throughput analysis. For high-throughput analysis, the design of interfaces between sensors and electrical instruments are also important for multi -channel testing. Moreover, the sensing protocols are easily modified to realize the tests of different targets with robotics, which requires the synergy between the sensors and measuring equipment. Liquid handling, gas mixing, and waste collection may also be provided in the high-throughput analytical devices systems and methods hereof Further, sensing system hereof also readily in terface with other external equipment.

[0074] In the case of a gas sensing using back-gate FET sensors or detectors, the detector for gas sensing is commonly kept on throughout the sensing process, and the gate voltage is usually fixed in the measurement. In contrast, the electrical test in an electrolyte-gate FET should only be temporarily triggered after liquid handling, and the gate voltage is swept only during the measurement. Moreover, unlike the ease of gas sensing, precise control of liquid drops is important for electrolyte-gate FET and other l iquid phase tests. Besides the accurate location of liquid drops on the sensor, electrodes should also be located at a determined position in the liquid drop during a test. In addition, the size of the liquid drop may limit the integration of the sensors since the liquid drop may cover additional sensors if they are highly integrated. Further, since the whole liquid handling process should also be automated, additional liquid handling instruments are needed to incorporate with the sensors and detectors. Different instruments should be well integrated to accomplish an FET test, which requires a good synergy between the electrical instruments and the liquid handling instruments. In a number of representative embodiments, electrolyte-gate FET test/sensor (and other test/sensor) systems hereof fulfill the following goals: (I) the automated test system can be paused or stopped at any time during a test: (2) the test protocol can be easily modified for different research purposes; (3) the automated system has good reliability, and can perform tests of multiple sensors with high precision; (4) the construction of an automated system is not limited to specific laboratory equipment. By satisfying such goals or requirements, the automated system hereof can be rebuilt in different labs with commercially available instruments.

[0075] In a number of embodiments hereof, electrochemical sensors (for example, chemically sensitive solid-state resistors (chemiresistors), FETs, etc.) including nanostructures in the sensing medium thereof are used. In general, nanostructures are structures of intermediate size between microscopic and molecular structures. Nanostructures may, for example, have at least one dimension in the range of 0.1 to hundreds of nanometers. Many nanostructures have at least one dimension in the range of 1 to 100 nm. Nanotubes are, for example, considered two-dimensional nanostructures and may have a diameter in the range of, for example, 0. 1 nm to hundreds of am and a length that may be significantly greater than the diameter.

[0076] Chemiresistors and FETs hereof may, for example, exhibit room temperature liquid phase sensitivity to analytes, including (biologically active molecules and biochemicals such as opioids). In a nanostructure-based FET de vice, one, for example, measures electrical current through nanostructures such as sc-S WCNT under an applied gate voltage. In chemiresistor devices, a gate voltage is not applied. In both types of devices, an electrical property (for example, conductance or resistance.) of nanostructures such as nanotubes changes upon exposure to an analyte, thereby providing a sensor s ignal. Depending on the semiconducting nature of the nanostructures, application of a gate voltage can provide amplification of the sensor signal. Nanotubes such as single-walled carbon nanotubes or SWCNTs and, particularly, semiconductor-enriched or (schSWCNTs provide an ideal candidate for incorporation into extremely small and low power devices hereof because they demonstrate extreme environmental sensitivity, high electrical conductivity, and inherent compatibility with existing microelectronic fabrication techniques.

[0077] Electrochemical devices, systems, and methods hereof have the potential to achieve rapid screening of opioids with high sensitivity. Desirably, on-site detection of opioids is achievable with a portable electrochemical analyzer. In recent years, a series of studies have been reported for the detection of opioids based on electrochemical techniques such as cyclic voltammetry, square wave voltammetry, differential pulse voltammetry, and field-effect transistor based sensing. However, selectivity in electrochemical detection is still a significant challenge for opioid sensing as a result of for example, false-positive, response from interferences. The discrimination of different opioids is difficult, in part, as a result of their similar structures.

[0078] In a number of representative embodiments of devices, systems, and methods hereof arrays sc- SWCNT FET devices were studied for the detection of an opioid (and/or a metabolite thereof) and for discrimination of different opioids (and/or metabolites thereof). Nanostructure-based electrochemical sensors, inchiding FET and chemiresistor scnsors/dc vices, are, for example, discussed in PCT International Publication Number WO 2025/042890, and U, S. Patent Application Publication Nos 2024/0345074, 2022/0365078, 2023/0309920, and 2020/0093429, the disclosures of which are incorporate herein by reference. The use of nanostructures such as SWCNT in eomieetiou with various sensors and other applications is also described, for example, in International Patent Application Publication Number WO 2008/088789, U.S. Patent Application Publication Nos. 2011/0127446, and 2024/0345074, and U.S. Patent Nos. 8,920,764, 9,482,638, 10,436,745, 10,801,982, 10,244,964, 11,685,657, 11,712,200, 12,203,830, 12,213,800, the disclosures of which are incorporated herein by reference.

[0079] Schematic representation of embodiment of an FET sensor device 10 hereof is set forth in FIG. 1A, while an embodiment of a chemiresistor sensor device 10a hereof is illustrated in FIG. I B. The illustrated sensor devices 10, 10a include a sensing medium material including one or more representative nanostructures. Such nanostructures include, for example, sc-SWCNTs 20, 20a. In a number of embodiments, the mmostrnctmes are a network of sc-SWCNTs). Single-walled carbon nanotubes are classified based on their electrical properties. Nanotubes may, for example, be considered to be either semiconducting or metallic. The nanotube synthesis process typically yields a mix of both metallic and semiconducting nanotubes. Purification steps are required to enrich the samples to be either mostly metallic or mostly semiconducting. Either mixed or purified nanostructures may be used hi the sensor systems hereof. However, purified semiconducting nanostructures may provide improved, lower levels of detection and a wider dynamic range in devices, systems, and methods hereof. As used herein, the term “semiconductor enriched'' (in reference to nanostructures such as sc-SWCNTs) indicates that there is a semiconducting content of at least 66%. In a number of embodiments, the semiconducting content is at least 90%, at least 95%, at least 99%, or at least 99.9%. In general, a greater semiconducting content will result in a better output signal.

[0080] In single-walled carbon nanotubes, all carbon atoms are located on the surface where current flows, making a stable conduction channel that is extremely sensitive to a surrounding chemical environment. Nanotubes and other nanostructures, including single-walled nanotubes (SWNTs) such as SWCNT’s, have the ability to change conductance in response to interaction with analytes. This characteristic is, for example, implemented hi a number of embodiments of systems 10 and 10a (see FIGS. 1 A and IB).

[0081] Various nanostructures other than SWCNTs are suitable for use in the present invention. Such nanostructures include, but are not limited to, multi-walled carbon nanotubes, graphene nanosheets and their derivatives (for example, reduced graphene oxide and holey graphene), nanowires, nanofibers, nanorods, nanospheres, nanoribbons (for example, interconnected nanoribbons of holey reduced graphene oxide) or foe like, or mixtures of such nanostructures. Moreover, in addition to carbon, those skil led in the art will appreciate that the nanostructures of the present invention can be formed of boron, boron nitride, and carbon boron nitride, silicon, germanium, gallium nitride, zine oxide, indium phosphide, molybdenum disulfide, silver, and/or other suitable materials. The formation and/or function of reduced graphene oxide and holey graphene compositions are, for example, discussed in U.S. Patent Nos. 8,920,764, 9,482,638, and 10,801, 982, and U.S, Patent Application Publication No. 2021/0122638, the disclosures of which are incorporated herein by reference.

[0082] As illustrated in FIGS. 1A and IB, the sensing medium or material 20. 20a, including semiconducting sc-SWCNTs or a network of sc-SWCNTs 21a (see FIG. I B; or other nanostructures), may, for example, be disposed upon a substrate 30, 30a (for example, silicon dioxide or quartz) and contacted by two conductive (for example, metallic - such as Au and/or Ti) electrodes representing a source (S) (a conductive electrode or terminal) and a drain (D) (a. conductive electrode or terminal). In the operation of an FET circuit such as illustrated in FIG. 1 A, changes in electrical conductivity may, for example, be measured for an applied gate voltage V<, via gate electrode (G). Ote may, for example. measure current flow between source (S) and drain (D) as a function of a swept' varied gate voltage range. Liquid gating was used in the studied sensors hereof.

[0083] As described above, a chemiresistor sensor device such as device 10a need not include an applied gate voltage. In chemiresistor 10a the sensing medium or material, including nanostructures 2 la, bridges the gap between two conductive electrodes 40a and 40a’ (for example, gold electrodes), which may be referred to a source and a drain. The sensing medium or material may alternatively be immobilized upon a set of interdigitated electrodes. The resistance or conductance between electrodes 40a and 40a' can be readily measured. The sensing medium or material has an inherent resistance or conductance that is changed by the presence of the analyte. In a chemiresistor, a source-drain bias voltage may, for example, be swept through a range of voltages, and drain current may be measured. In a number of embodiments, decorations 24, 24a (for example, metal nanoparticles, antibodies, etc.) may be attached to nanostructures ( for example, nanotubes) of sensor medium 20, 20a.

[0084] FIGS. 2A and 2B illustrates a plurality (or an array) of sensor devices hereof such as FET devices 10 formed on chips 60 which are positioned on and in electrical connection with a printed circuit board or PCB 50. hi a number of embodiments, a method for detection of one or more opioids in a liquid sample hereof includes providing a plurality of electrochemical sensors (for example, FET devices 10 on PCB 50). Each of the elecirochemi val sensors includes a substrate and a sensor medi um on the substrate as described above. The sensor medium includes at least one nanostructure as described above, wherein at least one property of rhe sensor medium is sensitive to the composition of a liquid on a surface of the sensor medium. The plurality of electrochemical sensors include a number of electrochemical sensors which have different sensor mediums. For example, some of the electrochemical sensors may include bare nanostructures, while others of the electrochemical sensors may be decorated (for example, with different metal particles or nanoparticles such as gold Au, platinum Pt, palladium Pd, silver Ag. titanium Ti, copper Cu, iron Fe, nickel Nr, iridium In, and zinc Zn, as well as their metal oxides). In a number of studies hereof, sensor mediums of the sensor devices hereof include bare carbon nanotubes, carbon nanotubes decorated with gold nanoparticles, and carbon nanotubes decorated with platinum nanoparticles.

[0085] Further, the electrochemical sensors hereof may be decorated with various receptors for one or more analytes such as opioids. For example, such receptors may include antibodies, antibody fragments (which include a binding region), or aptamers. An “antibody” (sometimes abbreviated as Ab and sometimes referred to as an immunoglobulin or Ig), is a relatively large, Y-shaped protein that is used by the immune system to identify and neutralize foreign objects (including opioids etc,). See, US Patent Application Publication No. 2024/0345074. Aptamers are single-stranded oligonucleotides that fold into defined architectures arid bind to targets (including opioids etc.).. [0086] In a number of embodiments, the plurality or array of electrochemical sensors (sometime referred to as sensor devices, devices or sensors) hereof, include nanostructure-based electrochemical sensors having differing sensing media, may function as nanostructure-based electronic noses/iongucs. In a number of embodiments, volume of the liquid sample may be placed in contact witlvover each of the plurality of electrochemical sensors. After such placement, a response of each of the plurality of electrochemical sensors may be measured. At least one of one or more opioids may then be detected in the liquid sample based upon a predetermined algorithm or model. The response of the plurality of electrochemical sensor may be analyzed using a predetermined algorithm or model to determine if a pattern or footprint of responses associated with the liquid sample is present. In that regard, changes in electronic properties (response) of a group of different nanostructure-based electrochemical sensors of the plurality of sensor or sensor array may be analyzed, to determine if a footprint or pattern thereof may be associated with or classified as one or the one or more target analytes/opioids. The group of different nanostructure-based electrochemical sensors can include all of the plurality of electrochemical sensor or a subgroup thereof.

[0087] As, for example, described further below, a feature vector may be determined from measurement of the responses of the electrochemical sensors. Based on features of the feature vector, the predetermined model (which may include one or more algorithms) may be used to determine at least one of the one or more opioids.

[0088] One or more artificial intelligence (for example, machine learning) algorithms or methodologies may be used in creating the predetermined model hereof. In that regard, change in electronic properties (responses) may be statistically analyzed using machine learning algorithms. For example, classification or determination of different opioids may be effected using one or more of linear discriminant analysis (LDA), support vector machine (SVM, linear kernel), k-nearest neighbors (KNN)_t Gaussian naive Bayes (GNB), ridge regression (RR), logistic regression (LR), and random forest (RF) as known in the computer, artificial intelligence, and machine learning arts. Machine learning models may be trained as known in the computer, art i ficial intelligence, and machine learn ing arts. One or more machine learning models may, for example, be trained using features and labels of a training set. as known in the art. Methods to select features in optimizing models (for example, recursive feature elimination, may be applied. Deep learning algorithms and neural networks may, for example, be used herein,

[0089] A sensor device array or sensor array such as illustrated in FIGS. 2A and 28 may be used in either a manual or automated sensing methodology hereof. Both manually operated and automated systems, devices, and/or methods hereof (along with the attributes and attendant advantages thereof), are further described herein. In either a manual or automated mode of operation, one or more machine learning algorithms may be used in determining or detecting one or more analytes such as opioids in a sample (for example, via classification of determined footprints or patterns of responses).

[0090] FIGS. 3 A through 3E illustrates a printed-control-board- or PCB-based sensor array 50 used in connection with an automated system 100. In a number of embodiments, a robotic system 200 such as a pipetting robotic system is adapted for used in the transfer of liquid and the controVpositioning of gate electrode (in the case of FET sensors). For example, in a number of studied embodiments, an Opentons OT-2 robotic liquid handler (available from Openlrons Labworks, Inc. of Long Island City, NY) was used as robotic system 200, See, for example, Opentrons OT-2 Liquid Handler (Manual), Re vision OT- 2R (2022), available from Opentons Labworks, Inc, All the operations of robotic system 200 may, for example, be software-controlled. In that regard, one or more software algorithm may be stored in a memory system and be executable by a processor system in connection with the memory system. In a number of studied embodiments, software control was achieved using the Python programming language, and the programming was based on the open source Opentrons Python ProtocolAPI available via GitHub, which is a developer platform that enables developers to create, store, manage and share code.

[0091 ] In the illustrated embodiment of FIG. 3A, automated electrolyte-gate FET test system 100 may be described as including three primary subsystem including a liquid handling system, an electrical test system or platform, and a control system or center, all of which were controlled by one or mote software algorithms such as Python script, as described herein. In a number of embodiments, the control system is configured to control operations of automated system 109, control measurements of response of electrochemical sensors thereof, and to analyze such responses. All the liquid handling operations and electrical measurements in opioid sensing tests could be performed via automated system 100. Electronic c ircuitry hereof was distributed among the liquid handling system, the electrical test system, and the control system. Studied representative embodiment of the electrical test system of the platform hereof included a source meter 400, a system switch 500 and PCB SO. As used herein a “source meter” is an instrument that can (for example, precisely) source voltage or current and simultaneously measure voltage and/or current. In that regard, a Keithley Source Meter Unit 26O2B (40(1 in FIG. 3A) was used for FET electrical characteristics measurement. A Keithley 3706A-S system switch (500 in FIG. 3.4) was used for, for example, switching between different sensor channels on PCS 50. The liquid handling system and the electrical test system or platform (including robotic system 200, source meter 400 and system switch 500) were controlled by a computer system 600 (which includes a processor system and a memory system connected to the processor system). Robotic system 200 was controlled by a first Python script. Python script A. Source meter 400 and system switch 500 were controlled by a second Python Script, Python script B. Such scripts or algorithms were stored tn the memory system of' computer system 600 and were executable by the processor system thereof. [0092] A robotic arm 300 is used to engage with and position a pipetie 310 (see FIG. 3A) or a gate electrode 310a (see, FIG. 3D). More than one robotic arms 300 may be provided in robotic system 200 to, for example, conduct various actions in parallel, which may be used to increase throughput and data collection rate. Robotic arm 300 may be programmed (for example, using a coordinate system such as a Cartesian coordinate system) for precise positioning. Such position can be calibrated during a calibration step.

[0093] Referring to FIG. 3 A, there are a number of different slots (as known, for example, in the robotic and computer arts) to locate laboratory equipment on a deck 210 within a housing 205 of robotic system 200. in the studied embodiments, because devices 10 for the electrical test were located on PCB 50, the entire PCB 50 was located on deck 210 as a single unit or piece of equipment. Other laboratory equipmentfor liquid handling, testing, etc., such as a source of sample liquid 220 (for example, a sample plate), a source of elcctrolyte/gating liquid 222, a source of pipettes 224 (for examp le, a pipette tip rack), a solid waste container 226, a liquid waste container 228, and a holder 230 for a gate or reference electrode 210a, may also located in different slots (at unique positions) on deck 210. As described above, use of a robotic system such as robotic system 200 enables one to, for example, reduce or eliminate errors associated with manual operations and to improve working efficiency. Moreover, use of the plurality of sensors or sensor array hereof in connection with robotic system 200 enables the realization of high-throughput screening o f samples for the presence of analy tes such as opioids,

[0094] In that regard, automated electrolyte-gate FET test system 100 was applied in the high- throughput screening of opioids in which opioid sensing could be achieved with 96 different electrochemical sensors or sensor devices such as FET devices 10 in the same test procedure. In that regard, PCB 50 included 96 parallel devices slots (8x12), PCB 50 functions as an interface between each FET sensor and source meter unit 400. In studied embodiments, there were two terminals on each slot to connect the source/drahi electrode of each sensor with the PCB. All the slots were connected in parallel, and the connection between PCB 50 and source meter unit included a busbar, which also included two terminals for the separate connection of source and drain electrode. Further, each slot was connected to the system switch 500 in parallel, which was achieved using another busbar on PCB 50.

[0095] In studies of opioids, an optimized protocol based upon a manual, test protocol used in connection with a sensor array was applied to the automated test protocol with a number of changes. See, for example, FIG. 3F. Since only 100 pl, of opioid solution was used for sample incubation in certain studied, the coordinates of pipette were calibrated relative to each slot before the experiment. The calibration step helped ensure that the pipette tip was exactly pointed to the sensor chip, which could be folly covered by the liquid drop after liquid dispensing (see, for example, FIG. 3D). Meanwhile, to avoid the liquid contamination on the printed circuit board, 240 p.L of PBS was used as the blank sample for incubation in the automated test group, instead of 400 gL in the manual test group. Since a system for gas drying, which was used in the manual test group protocol, was not included in automated system 200, an extra liquid cleaning protocol was executed to in the automated test group protocol to remove the residual liquid after sample incubation. Automated system 200 may, for example, be readily modified to include a system for gas drying. The liquid cleaning protocol included the liquid aspiration on 25 different sections of (he same sensor chip. A comparison of workflow between manual test group and automated test group is shown in FIG. 3F. In a number of embodiments, for the quality control, the transfer characteristics of sensors used for data analysis fulfilled the three requirements: 10.

[0096] In the manual test group protocol, FET sensors 10 were immersed in 400 pL of 0.01 M phosphate buffer saline (PBS) for 10 minutes. That step was repeated three times After rinsing with nanopure water and drying, sensors 10 were incubated in another 400 pL of PBS for 1 b and the FET characteristics were measured as the control group. Source meter 400 (Keithley Source Meter Unit 2400) was used for electric measurements. For each measurement, the sensor was rinsed with nanopure water first and dried with nitrogen gas flow, then immersed in 400 pL of PBS for 2 minutes before the measurement started. Then, the same sensor was incubated with I pg of opioid reference standard (1.0 pg/mL, 100 pL in PBS) for I h. After rinsing and drying, the nanotube FET (NTFET) characteristics were measured as the experimental group. After tests, the sensors were recovered by rinsing with isopropanol and stored for the test of another compound. The da ta collected manually was assigned as the manual test group. For each compound, all the test samples are tested with separated sensors (e.g., 43 codeine samples are tested with 43 different sensors).

[0097] Opioid sensing using die automated electrolyte-gate FET test system 100 shared a similar protocol with the manual test group but with several changes. As described above, the sensors were incubated with 240 pL of PBS. which was transferred by robotic system 200. for 10 mitts and repeated 3 times. Then those sensors were incubated with 240 pL of PBS for 1 h. Since gas drying had not been coupled with robotic system 200, a solution removal protocol was developed to remove the residual on the sensor thoroughly before the electrical measurement In that regards, pipette tip of pipete 210 aspirated solution on 25 different sections on the package as described above to remove all the residual. A gas line for drying may (additionally or alternatively) be included on robotic arm 300 or on a separate robotic arm in embodiments hereof. The NTFET characteristics as the control group were then measured in 240 pL of PBS after incubation for 2 mins. Source meter 200 was used as the detector in the automated system. Subsequently, robotic system 200 transferred 1 pg of opioid reference standard (10 pg/mL, 100 pL in PBS) to the sensor. After 1 h incubation, the sensors were rinsed with 240 uL of PBS twice and residual was removed thoroughly. Finally, the NTFET characteristics of the experimental group were measured. The sensors were also recovered by rinsing with isopropanol. The data collected with the automated system was assigned as the automated test group. [0098] In a number of studied embodiments, two busbars were linked to the source meter 400 and system switch 500 separately. PyVISA^1M, a Python package using the Virtual Instrument Software Architecture specification was imported for the communication between the source meter 400/system switch 500 and computer system 600. Single-step operations were realized with the built-in commands in source meter 400. Those built-in commands were then combined to build different test functions for multi-step operations. The FET measurement in a number of studies hereof was primarily a test of transfer characteristics. Channel A of source meter 400 was employed was used to apply a bias voltage and to measure source-drain current. Channel B thereof was used to sweep gate voltage. Various operations of system switch 50ft are illustrated in FIG. 4A. System switch 500 was, for example, used for the switching of device slots between different tests. In a test, for example, only the slots with the assigned sensor devices were closed by system switch 500, and other slots remained open. The 8^;< 12 circuit board design is representative for the coupling between source meter unit 400 and sensors fabricated for representative studied hereof. Based on different designs of circuit boards (nxm slots, wherein n and m could be any integer), one can be readily designed for use in systems hereof to provide PCBs to test more devices (> 96) or fewer devices (< 96) in rhe same experiment, wherein each sensor may be operated individually. The electrical test platform hereof can also work independently, without the liquid handling system/robotic system 200, or be integrated with other sampling systems, such as coupling with mass flow controllers for gas sensing.

[0099] In an electrolyte-gate FET test hereof; pipetting robotic arm 300 should accurately dispense the gating liquid on the assigned device by robotic system 200, As described above and illustrated in FIG. 3A, a Cartesian or other coordinate system may, for example, be used to locate different laboratory equipment on the robot deck. The positions of laboratory equipment on the deck may; for example, be determined by (a; y) coordinates (the coordinates are illustrated in millimeters in FIGS. 3C and 3D). By inputing the (x, y) coordinates of destination, the robotic arm 300 and attached pipete 310 can transfer the liquid to any position on deck 210 accurately. Since a single equipment may need multiple coordinates to locate different wells, (for example, a 96-well microplate), each laboratory equipment may be defined as a single module separately in memory, which stores all the coordinates of the equipment. Such modules can, for example, be called by the protocol API for the control of each article of equipment in die test.

[00100] As described above, the movement of gate electrode 310 a is also achieved by the robotic arm 300 of robotic system 200. Gate electrode 310a may; for example, be recognized as a pipette tip by robotic system 200 in all operations and may be kept in a certain or determined position on a pipette tip rack when it is on standby. A commercial Ag/AgCl reference electrode was used as gate electrode 310a in studied systems hereof, which was connected to the source meter unit by an external wire. By inputting the U, y) coordinates of the assigned sensor device, robotic arm 300 may be controlled to carry' gate electrode 310a to the sensor device, and the vertical distance between gate electrode 310a and the sensor device may be determined by z coordinates. Desirably, gate electrode 310a should be dipped m the liquid drop, but not contact the surfaces of the sensor devices. The same strategy can also be applied for the cleaning process of gate electrode 310a after testing. The gate electrode can be rinsed with pure water in the assigned liquid wells. By setting the location coordinates, gate electrode 310a can be moved to the liquid wells for washing, and time length for washing may be controlled by setting a time delay. Since the lengths of different commercial reference electrodes may also be different, the vertical distance may be calibrated for each gate electrode 310a.

[00101 ] Both the liquid handling system and the electrical test system or platform hereof were connected with computer system 600 via a USB port. In a number of embodiments of automated system 100 hereof, each instrument can be controlled by computer system 600 independently with a separate Python script. In a number of experiments, all the operations from different instruments are run in sequence. An important aspect in the design of automated system 100 hereof is the synergy among the different instruments thereof, which is realized by tire exchange of information among different instruments through the local network formed by the control center, ■•computer system 600. In the studied embodiments, AIOHTTP, a Python package providing an asynchronous HTTP client/server, was imported to build a Web server on computer system 600 for the exchange of information between the liquid handling system and the electrical test platform. For example, if one wants to pause liquid handling system and start electrical test platform, the Python script for liquid handling system (Python script A) may send a request to the Web server and then hang afterwards. On the other hand, the script for electrical test platform (Python script B) receives the same request from the Web server and starts running electrical testing. After it is finished. Python script B will be hanging after sending another request to the Web server, and Python script A will resume after receiving the request. To achieve the exchange of information, the two scripts share the same Web server with a self-assigned IP address of the USB port. Using this methodology, operations can be easily switched between the instrument in operation and other standby instruments. The methodology also guarantees that only one instrument is working at one moment, and that all the operations are run successively based on the sequence in the Python scripts.

[00102] The workflow of an electrolyte-gate FET test with automated system 100 hereof is summarized hi FIG 4A. The commands used in FIG, 4A are summarized in Table 1 of FIG. 4B, and all the commands executed in an FET test by the automated system are gi ven in Table 2 of FIG. 4C. An electrolyte-gate FET test can, for example, be divided into five actions or steps, which corresponds to the five panels in FIG. 4A: Step (a): Add a gating electrolyte to the assigned device, Step (b): Connect with the gate electrode. Step (c): Connect to the source and drain electrode after a time delay, Step (d): Measure transfer characteristics, and Step (e): Remove the gating electrolyte and move it to another of the 96 sensor devices of PCB 50. [00103 ] In the studied embodiments, script B should be run first, and it will be hanging when there is no request sent by script A. Then script A is run to start the pipetting operations. At the beginning, the electrical test platform is on standby in Step (a), and the robotic atm 300 will add the gating electrolyte onto the assigned sensor device 60 (device l-l in FIG, 4A) via pipette 310, The position of the sensor device can be adjusted by changing the coordinates (x,y). Subsequently, gate electrode 310a is connected and moved to sensor de vice 1*1 , which is represented in Step (b). As shown in FIG. 3D, gate electrode 310a is dipped in the liquid drop, and a time delay is set before Step (c) to balance the mass transfer in the drop. Script A then hangs after sending a request to the Web server, which also pauses robotic arm 300. As a result, the gate electrode will be suspended in the liquid drop until the electrical test is complete.

[00104] The control system (computer 600) will receive the request in Step (c) and transfer the request to script. B through the Web server. Script B then resumes to run the measurement of transfer characteristics. The slot with the assigned device will be closed to connect to the source meter unit 400 by the system switch 500, while the electrical test will not start without running the test functions. Only by running the transfer characteristics test function of in Step (d), the whole circuit for the electrical test will be closed and the test will start. For each test, all data may be automatically saved in a separate file in memory in Step (d). After electrical tests arc completed, script B will hang to wait for the next test, and script A resumes in Step (e) after receiving the request sent by script b, 'lire circuit is open, and robotic arm 300 will move gate electrode 310a to the next device (device 1 -2 in the numbering scheme of FIG. 4A) for another cycle.

[00105] An important step in an automated FET measurement as described in connection with representative embodiments hereof is the accurate control of the .liquid drop. Although the movement of robotic arm 300 can be precisely controlled ■with the coordinates, the drift of a l iquid drop during the test may still affect the accuracy. The drift may be partially the result of the impulse from the collision between the liquid drop and a sensor chip 60 in the liquid dispersion. Herein, when dispensing samples on a sensor chip 60, the distance between the tip of pipette 310 and sensor chip 60 was maintained short or minimized but nonzero (commonly set between 0.1 and 0.3 mm), which minimizes the impulse but also avoids the direct contact between the pipette tip and sensor chip 60. Another reason for the liquid drop drift is because a sensor chip 60 may not be horizontally placed, which could be completely avoided in the studied embodimen t s as a result of the design of the 40-p in ceramic duahinline package, used. This problem can be solved by applying extra liquid wells to the sensor chips or by designing new slots to couple with planar sensor chips.

[00106] Because sensors 60 may be incubated with different solutions separately, the residual liquid on the sensor surface, which may result in the cross-contamination of different solutions and the dilution of target analytes, is desirably cleaned thoroughly after each incubation. Sensor cleaning and waste removal may thus be important for a test. Sensor cleaning is accomplished in a number of embodiments hereof by assigning a container with nanopure water for washing. For waste removal, all sensors 60 may be defined as accessories of robotic arm 300, which can be located in the Cartesian coordinate system. Each chip 60 was, for example, divided into different sections (25 In the studied embodiments) at illustrated in FIG. 2B, and a function based on the Opentons Python Protocol API was built for liquid aspiration on these sections. In the studied hereof, the liquid aspiration was executed 25 times to effectively remove the residual liquid ftom different sections of a chip 60. The distance between the pipette tip and sensor chip 60 was set at 0.3 mm to avoid collision and guarantee that the residual liquid could be thoroughly removed. The transfer characteristics remained constant after washing, indicating the effectiveness of our waste removal protocol.

[00107] A common error which may in test using systems hereof is an error in communication through the local network. To avoid such an error, the IP address of the Web server in different scripts should be exactly matched in all tests. Drift of pipettes may also occur after multiple tests, leading to errors in the location. Pipettes should thus be regularly calibrated, and calibration should always be performed if new sensors are applied for the test. Another possible error arises from the failure of liquid transfer. Such a failure is typically a result of the lack of solution in the liquid well or the inappropriate height of the pipette tip in the liquid aspiration. Because the amount, of sample for an analysis is limited in some eases, a suitable liquid container should be selected for different solutions. Randomly, if an unexpected error occurs in the liquid handling process, the error can typically be resolved by re-starting from the present step in script A directly.

[00108] As described above, the performance of the automated system was first evaluated by a pH-sensing test, C'NT-based FET sensors for pH sensing were based on previous work, which was employed for the pH-sensing test, Liu, Z. R., et al., Anal. Chem., 94, 3849-3857 (2022), the disclosure of which is incorporated herein by reference. The pH sensing test was conducted with gold nanoparticledecorated semiconductor-enriched carbon nanotube field-effect transistors (Au-NTFET), which were previously shown to provide good. pH sensing performance. The automated system was first tested by evaluating the precision in multiple tests. The pH sensor was immersed in buffer (pH = 12) for 5 minutes prior to FET measurements. The FET measurements were repeated six times for each sensor and then the buffer was removed. Subsequently, the above protocol was repeated 11 times, and the buffer (pH = 12) was always replaced between two tests. The precision assessment was based on the comparison of source-drain current values at -0.2 V among different tests. The reproducibility of the six repeated FET measurements was good in all the 1 1 trials (with RSD values varies from 0.507% to 0.630%), which demonstrated the high precision in repeated tests with the electrical test platform. The current changes were also compared in all 1 1 trials. It should be noted that the slight decrease in Current between two different trials may be due to the signal drift with time in the electrolyte-gate FET measurement. However, the RSD value of all 66 tests in 2.5 h is only 6.0%, showing high precision in liquid handling operations. Compared to the control group, the current value decreased by approximately 50% in the test group (FIG. 5A) when measuring the transfer characteristics in buffers with pH values from 12 to 2 successively in the 1 1 trials. It can be concluded that the automated system has good reproducibility in the operations, which is favorable for batch operations in FET tests.

[00109] The transfer characteristics were then measured in the buffer with pH values from 12 to .2 FIG. 5B) using the automated system, which shared the same protocol used in previously published studies. See Liu, Z. R., et al., Anal. Chem., 94, 3849-3857 (2022) In addition to a common test (black squares in FIG, 5C) in which FET measurements were repeated six times for each sensor, another ‘rapid’ test (gray circles in FIG. 5C) was also designed, which was carried out taking the FET measurement only once for each sensor. The ‘rapid’ test was completed in 1 h 36 min with 88 FET tests, which saved 50 minutes compared to the same manual operation test (2 h 26 min). For a manual FET test, it takes another 20 to 30 seconds for a series of manual operations, including channel s witch, running tests and data saving, which can be finished simultaneously with the automated system. It also improved accuracy by avoiding continuous signal drift in transfer characteristics as a result of liquid incubation (the same as the observation in FIG. 5A). In addition, all liquid handling operations are standardized with the automated systems hereof. Besides saving time in the liquid handling, it also avoids random errors in manual operations and accidental damage of sensors. Finally, the pH sensing performance was evaluated, and good linearity was obtained between the drain current at -0.2 V and the pH values (FIG 5C) for both the common test and the rapid test, which was consistent with the results previous work based on manual operations. Even a lower standard deviation was observed in the rapid test group, which might be because the rapid test was completed later. In previous work, it was found that the pH sensor performed best after running tests several times. Furthermore, the two test groups shared similar slope values, demonstrating the stability of the pH sensor and the reliability of the automated system. The results demonstrate that the automated system provides a good test platform for different electrolyte-gate FET sensors. The automated systems hereof provide a good replacement for manual operations, providing high work efficiency and good accuracy.

[001 10] Automated system 100 hereof was subsequently studied in various opioid drug testing studies. The automated system was first used to evaluate the sensing performance of a fentanyl antibody-ftmctionalized FET sensor. The protocol script was written to incorporate a previously published manual sensing methodology. See, Shao W. et al., ACS Appl. Mater. Interfaces, 15, 37784-- 37793 (2023), the disclosure of which is incorporated herein by reference. Specifically, the sensing experiment started with a test in the blank solution (PBS), which was used as a baseline for sensor response calculations, and then tbe opioid drug sol inions were tested from the lowest concentration to the highest concentration. For each test, 30 pL of each sample was first transferred from a well plate to the FET sensors 10 by robotic arm 300. The position of robot arm 300 was calibrated so that the sample droplet would co ver sensor chip 60 to allow interactions between the drug molecules and their specific antibodies. After a .10 min incubation, the sample solution was removed, and the FET sensor chip was washed with the gating liquid (i.e., 6.001 x PBS), which was achieved by repeated aspiration and dispensing with the gating liquid, to remove the unbound analyte. After repeating the washing step three times, 200 pL of the gating liq uid was transferred to the FET sensor, and robotic arm 300 was switched to pick up gating 310a electrode tor the FET measurements. During data collection, the switching matrix swi tched between channels, and the FET characteristics of each channel was recorded three times for each sample.

[001 1 I ] The FET characteristics of the fentanyl sensor were determined, and the calibration curve of the fentanyl sensor is shown m FIG, 6A. The fentanyl antibody-ftinciionalized gold- nanoparticle- or AuNP-decarated carbon nanotube (CNT) FET sensors demonstrated good sensing capabilities toward fentanyl detection. The automated FET sensor test system hereof offered several advantages including the consistent measurement time across tests help maintain accuracy and comparability between samples, keeping the distance between the gate electrode and the sensor chips the same ensures that each sensor is tested under identical conditions, and. the system minimizes the potential for manual mistakes in general,, improving the effectiveness of the sensing platform as an accurate and efficient tool for high-throughput sensing.

[001 12] In studies for detecting opioids and their metabolites in sweat, the automated system hereof was further tested in a sensor array incorporating FET sensors (Au-nanoparticle-fhnctionalized S WONT) functionalized with three different antibodies on, namely norfentanyl antibody (NOR-ab), morphine antibody (MOR-ab), and 6-monoaeetylmorphine antibody (6-MAM-ab). Apart from the specific target of the three antibodies (NOR, MOR. and 6-MAM), the sensor array could be expanded to detect the corresponding parent drugs, (that is, fentanyl, codeine, and heroin). Antibodies recognize their target molecules through molecular shapes, fimctional groups, and electronic properties. Since opioid drug metabolites share a large portion of their structures with their parent drugs, antibodies raised against the metabolites also bind to the drugs, albeit with varying affinities, By leveraging the crossreactivity of these antibodies, the sensor array allows for broader detection of opioid drug exposure, including both the parent drugs and their metabolites.

[001 13] Another important feature of sensor testing systems hereof is multi-panel sensing. To demonstrate this feature, an opioid drug sensor array was prepared. The opioid sensing array include FET sensors designed to detect fentanyl, hydrocodone, and morphine. Antibodies that recognize fentanyl, hydrocodone, and morphine were immobilized on the NTFET sensors to ensure specific targeted detection. The specificity of the sensors was evaluated by first testing them with two nonspecific drugs, followed by the specific target drug. As illustrated m FIGS. 68 through 6D, for all the antibody-fonctionalized FET sensors, the specific target analyte generated the most significant sensor responses. The results demonstrated not only the high selectivity of the antibody-furictionalized FET sensors for the detection of opioid drugs, but also the high reliability of the automated systems hereof, which provided good stability and consistency after long working hours, highlighting the potential of the automa ted sensing systems hereof as a rapid screening tool for sensor arrays, which are particularly valuable for analyzing muhi-componciit samples.

[001 14] As described above, machine teaming methods provide new solutions for the identification of analytes such as opioids with similar structures. In recent years, machine teaming methods have been increasingly utilized for the classification of opioids by analyzing the “fingerprints” of samples. Such “fingerprints” are composed of different characteristic features extracted from the original data, in contrast to traditional methods in which the determination is only based on several specific parameters. Although those studies are based on spec trome trie methods which are not portable, labor intensive and time-consuming, the great potential of machine learning methods in opioid screening has been demonstrated. However, the amount of training data is important for the model performance. For the identification of opioids, a large number of tests are required for each compound to collect enough data for model training.

[001 15] In further studies of automated systems hereof, carbon nanotube field-effect transis tor sensor arrays for the screening of four different opioids with machine learning methods were tested. The screening of opioids with different testing approaches and classification with different machine learning methods was studied. The FET sensors were fabricated with semiconducting single-walled carbon nanotubes (SWCNTs), include bare electrode (BE) and electrodes decorated with gold (A.u) and platinum (Pt) metal nanoparticles, which were applied for the sensing of codeine, fentanyl, hydrocodone, and morphine (see FIG. 7 A). Results were studied both under manual operations and via automated system 100 for the high-throughput testing of sensor arrays. The opioids were first classified with previously developed conventional supervised machine learning models with 15 features. The results showed that fentanyl could be well classified with the other three opioids with an accuracy value of up to 97.1%, and three different opioids could also be well classified by the optimized models with accuracy values higher than 90%, The classification of four opioids was also evaluated w i th an accuracy higher than 80%, Deep learning methods were then introduced for the classification of opioids by utilizing all the test data . An embodiment of a workflow of data analysis, including the collection and transformation of raw data, as well as an architecture of a multi-path convolutional neural network (CNN) used tn deep learning is illustrated in FIG. 73. FIG. 7C illustrates a representative embodiment of a workflow of data analysis, including the collection and transformation of raw data, as well as an architecture of the multi-path convolutional neural network (CNN) used in deep learning with both Ij_A and I_Ss input values. The studies indicated that accuracy could be further improved for the classification of four opioid compounds using an optimized multi-path convolutional neural network model. Further, gate-source current values were also applied to build the deep learning model, providing more information for each training sample in the classification task. The results revealed the importance of gate-source current to improve the classification, which is meaningful for the design of new training models.

[001 16] Experimental

[001 17] Components of the automated electrolyte-gate FET test system. The automated electrolyte-gate FET test system include five primary components. An Opentron OT-2 robot was applied for liquid handling. A Keithley Source Merer Unit 2.602B was applied for the measurement of electrical characteristics of the FET. A printed circuit board (PCB) with 96 channels, designed by the University of Pittsburgh Electronic Shop, provided the sensors slots. A Keithley 3706A-S system switch was applied to switch between different sensor channels on the PCB. Those four components were controlled by a computer (#3 in Figure I ). The OT-2 robot was controlled by Python script A. The source meter and the system switch were controlled by Python script B.

[001 18] pH sensing. The details of the fabrication of carbon nanotube fie Id-effect transistors decorated with gold nanoparticles (Au-NTFET) are given in Liu, Z. R., el ah. Anal. Chem., 94, 3849- 3857 (2022). The 40-pin ceramic dual-inline package was used for sensor fabrication. Britton-Robinson buffers with pH ranging from 2 to 12 were prepared for p H detection, Britton, H. T. S. , and Robinson, R. A., J. Chem. See., 1456-1462 (1931). In the test, the 1 1 different buffers (pH ~ 2 to pH = 12) were kept in different wells in a 12-well microplate. At first, 300 p.L of buffer (pH ^::: 12) was added to the sensor by the pipetting robot and incubated for 5 minutes. The same process was repeated by the robot three times. The sensor was then incubated with 2.40 pL of buffer (pH - 12.) for 5 minutes before the FET measurement started. FET measurements were taken in buffers with pH values from 12 to 2 successively. The gate voltage was swept from 0.6 V to -0.6 V with a source-drain bias voltage of 0.05 V. Between two different buffers, the pipetting robot would run a waste removal protocol to thoroughly remove the residual liquid from the device surface.

[001 19] Materials for sensors for opioid detection with automated systems using machine learning. The FET sensors were fabricated with commercial semiconductor-enriched single-walled carbon nanotubes (SWCNTs, IsoSol-SlOO, Nanolntegris) with 0.1% metallic and 99.9% semiconducting composition. The metal nanoparticles were decorated on the bare CNT by bulk electrolysis. 1 niM chloroauric acid (HAuCLcSEkO, Alfa Ae&ar) and 1 mM chlorophtinie acid (HPtClfj'xHsO, Sigma-Aldrich) were prepared in 0. 1 M HC1 solution for bulk electrolysis. The certified reference standard (I mg/mL in methanol) of codeine, fentanyl, hydrocodone, and morphine were all purchased from Cerilliant.

[00120] Fabrication of FET. The standard 40-pin ceramic dual inline packages were used for wire bonding with 2,6 x 2.6 mm* chips, which were fabricated with Si/SiOs wafers patterned with interdigitated gold electrodes using photolithography. The wire-bonded packages were secured with polydimethylsiloxane (PDMS) and heated at 200 ”C for 1 h. The IsoSoi-S WCNTs (3 uL, 0.02 g ml; ^] , dispersed in toluene) were then deposited between the interdigitated electrodes through dielectrophoresis (DEP) using a Keithley 339(1 Arbitrary Waveform (10 Vw, 100 kHz for 2 min). The chips deposited with SWCNTs were then annealed at 200 ^,!C for 24 h on a hotplate to improve the contact between the SWCNTs and the gold electrodes. The bulk electrolysis of HAuCLrSFkO/ HPtCE-xH;.:O was realized by an electrochemical analyzer (CH instruments). A 1 M Ag/AgCl electrode was used as the reference electrode in the bulk electrolysis. Pt electrode worked as the counter electrode, and SWCNTs on the chip were applied as the working electrode.

[00121] Characterization. An XplorA Raman-AFMZTERS system was employed for the collection of Ramau spectra using a 638 nm (24 mW) laser for excitation, which was operated at 1% power. The Raman Spectra were collected from 25 different locations on a sensor ehip and averaged for plotting. All spectra were normalized to the Si peak at 499.8 nm for the comparison of spectra among different samples. The atomic force microscopy images were taken by a Broker Multimode 8 AFM system with a Veeco Nanoscope III a controller in the tapping mode. The AFM images were processed by Gwyddion,

[00122] Opioid Sensing with Manual Operations. The FET sensors were immersed in 400 gL of 0.01 M phosphate buffer saline (PBS) for 10 minutes, and the above step was repeated three times. After rinsing with nanopure water and drying, the sensors were incubated in another 400 pL of PBS for 1 h and the NTFET characteristics were measured as the control group. A Keithley Source Meter Unit 2400 was applied for electric measurements. The gate voltage was swept from 0.6 V to -0.6 V with an interval of 0.06 V, and the source-drain voltage is 0.05 V. Both source-drain current (fo) values and source-gate current (l_g5) values at 200 different voltage values were recorded. For each measurement, the sensor was rinsed with nanopurc water first and dried with nitrogen gas flow, their immersed in 400 pL of PBS for 2 minutes before the measurement started. Then, the same sensor was incubated with 1 pg of opioid reference standard (10 gg/mL, 100 pL in PBS) for 1 h. After rinsing and drying, the FET characteristics were measured as the experimental group. After tests, the sensors were recovered by rinsing with isopropanol and stored for the test of another compound. The data collected manually was assigned as the manual test group. For each compound, all the test samples arc tested with separate sensors (for example, 43 codeine samples arc tested with 43 different sensors).

[00123] Opioid Sensing with an. Automated Etectrolyfe-gate FET Test System, The opioid sensing using the automated electrolyte-gate FET test system shares a similar protocol with the manual test group but with several changes. The sensors were incubated with 240 pl, of PBS, which was transferred by the pipetting robot, for 10 mins and repeated 3 times. Then these sensors were incubated with 240 pL of PBS for 1 h. Since gas drying had not been coupled with the automated system, a solution removal protocol was developed to remove the residual on the sensor thoroughly before the electrical measurement. The pipette tip aspirated solution on 25 different sections on the package to remove all the residuals. The FET characteristics as the control group were then measured in 240 pL of PBS after incubation for 2 mins. A Keithley Source Meter Unit 2600 was applied as the detector in the automated system. Subsequently, the pipetting- robot transferred I p.g of opioid reference standard (10 pg/tnL, 100 pL in PBS) to the sensor. After 1 h incubation, the sensors were rinsed wi th 240 pL of PBS twice and residual was removed thoroughly. Finally, the NTFET characteristics as the experimental group were measured. The sensors were also recovered by rinsing with isopropanol. The data collected with the automated system was assigned as the automated test group.

[00124] Classification of Different Opioids with Conventional Supervised Learning Algorithms. For the quality control, the transfer -characteristics of sensors used for data analysis should fidfill the following three requirements: a) < 10‘’ A; b) l_ds ® „«.» v > 10" A; c) Iy_sy AS V / hs-aas v > 10. All the data analysis was done using Python. Initially, 15 features were extracted from the NTFET characteristics of control group and experimental group based on the protocol reported in Z.R., Liu, et al., A Carbon Nanotube Sensor Array for the Label-Free Discrimination of Live and Dead Cells with Machine Learning. Anal. Chem., 94 (8), 3565-3573 (2022). There were 43 samples for each compound in the manual test group and 41 samples in the automated test group. For the classification of different opioids, seven different algorithms including linear discriminant analysis (LDA), support vector machine (SVM, linear kernel), k-ncarcst neighbors (KNN), Gaussian naive Bayes (GNB), ridge regression (RR), logistic regression (LR), and random forest (RF) were applied for model training. The leave-onc-out method was used for the cross validations.

[00125] Classification of Different Opioids with Deep Learning Methods, A deep learning task was achieved by designing a multi-path convolutional neural network (CNN). The flowchart of the deep learning task with CNN model is shown in Figure 7C. The input layers were composed of two input streams. One input stream included ail the U values at different gate voltages and the other input stream included ail the fo values. For each training sample in an input stream, a feature was calculated by the changes in I* or Inbetween the control group (blank sample) and experimental group (after opioid incubation), resulting in 200 features from each sensor. Since each training sample contained all the current values from all three types of sensors, there are 600 features in total (200 features for each sensor). The feature extraction was processed by two consecutive ID convolutional layer. Finally, the flatten features passed through a fully connected layer with 64 neurons, and an output layer with 4 neurons provided the results. The cross validation was still based on the leave-one-out method. More details about the CNN model are provided the model architecture shown in FIG 7B.

[00126] The foregoing description and accompanying drawings set forth a number of representative embodiments at the present time. Various modifications, additions and alternative designs will, of course, become apparent to those skilled in the art in light of the foregoing teachings without departing from the scope hereof, which is indicated by the following claims rather than by the foregoing description. All changes and variations that fall within the meaning and range of equivalency of the claims are to be embraced within their scope.

Claims

WHAT IS CLAIMED IS:

1. A system far detecting an analyte in a sample liquid, comprising: a printed circuity board comprising a plurality of electrochemical sensors positioned at unique positions on the printed circuitry board and in electrical connection therewith, each of the plurality of electrochemical sensors comprising a substrate and a sensor medium on the substrate between spaced electrodes, the sensor medium comprising at least one nanostructure, wherein at least one property of the sensor medium is sensitive to the composition of a volume of the sample liquid on a surface of the sensor medium, and an automated system comprising a liquid handling system, an electronic test system, and a control system comprising a processor system and a memory system in connection with the processor system, the control system being in communicative connection with the liquid handling system and with the electronic test system, the printed circuit board being further configured to be placed in electrical connection with the electronic test system and the control system, the control system being configured to save in the memory system a unique position in a coordinate system of each of the plurality of electrochemical sensors when the printed circuit board in placed in connection with the electronic test system and the control system,

2. The system of claim I wherein the plurality of electrochemical sensors include electrochemical sensors having different sensor media,

3. The system of claim 2 wherein the liquid handling system comprises a robotic system, the robotic system comprising a deck comprising a slot to position the printed circuit board thereon and one or more robotic arms, at least one of the one or snore robotic arms being configured io deliver liquids including the volume of the sample liquid independently to each of the plurality of electrochemical sensors of the printed circui t board under control of the control system.

4. The system of claim 3 wherein the electronic test system is configured to be independently connected with each of the plurality of electrochemical sensors of the printed circuit board under control of the control system, the electronic test system being further configured to independently apply electrical energy to each of the plurality of electrical sensors to create a bias voltage between the spaced electrodes thereof and to independently measure a response of each of the plurality of electrochemical sensors to the applied electrical energy.

5. The system of claim 4 wherein the electronic test system comprises a source meter in connection with the control system and with the printed circuit board, the source meter being configured to independently apply the electrical energy to each of the plurality of electrical sensors to create the bias voltage between the spaced electrodes thereof, and to independently measure the response of each of the plurality of electrochemical sensors to the applied electrical energy.

6. The system of claim 4 wherein the electronic test system further comprises a system switch configured to switch independently between connections with each of the plurality of electrochemical sensors of the pri nted circuit board under control of the control system.

7, The system of claim 6 wherein the liquid handling system is controlled via a first software algorithm stored in the monoty system and executable by the processor system, and the electronic test system is controlled via a second software algorithm stored hi the memory system and executable by the processor system.

8. The system of claim 7 wherein each of the first software algorithm and the second software algorithm is configured to send requests to the control system and the control system is configmed to pause or actuate one of the first software algorithm and the second software algorithm based upon the requests.

9. The system of claim 8 wherein the memory system of the control system comprises a web server software algorithm stored therein and executable by the processor system to exchange infbnnation with the first software algorithm and the second software algorithm.

10. The system of claim 4 wherein at least one of the one or more robotic arms is configured to position a pipette using a coordinate system to at least one of (I) deliver liquid to a determined electrochemical sensor or (ii) aspirate liquid from the determined electrochemical sensor,

1 1 . The system of claim 10 whe rein the deck comprises one or more slots at unique determined positions thereof, each of the slots being configured to connect a component thereto from the group consisting of a component comprising a source of the sample liquid, a component comprising a source of an electrolyte, a component comprising a source of pipettes, a component comprising a holder for a gate electrode, a component comprising a solid waste container, and a component comprising a liquid waste container.

12. The system of any one of claims 1 through 1 1 wherein each of the plurality of electrochemical sensors comprises a liquid-gated field effect transistor electrochemical sensor.

13. The system of claim 12 wherein the liquid handling system comprises a robotic system comprising one or more robotic arms, and at least one of the one or more robotic arms is configured to position a gate electrode to be in connection with the volume of the sample liquid on the surface of the sensor medium of a determined one of the plurality of electrochemical sensors, and the electronic tests system is configured, under control of the control system, to apply a gate voltage to the gate electrode.

14. The system of claim 13 wherein a plurality of features is determined from the response of each of the plurality of electrochemical sensors via an analysis algorithm to determine the analyte, the analysis algorithm optionally comprising one or more artificial intelligence algorithms, which optionally comprise one or more machine learning algorithms.

15. The system of claim 14 wherein the one or more artificial intelligence algorithms are configured to speciate between a plurality of analytes.

16. The system of claim 14 wherein the analyte is at least one of an opioid or a metabolite of an opioid.

17. The system of claim 2 wherein the nanostructures comprise carbon nanostructures.

18. The system of claim 17 wherein the carbon nanostructures are single-walled carbon nano tubes.

19. The system of claim 18 wherein the single-walled carbon nanotubes are semiconductor enriched single-walled carbon nanotubes.

20. The system of c laim 19 wherein the nanostructures of the sensor medium of the plurality of electrochemical sensors comprise one of bare nanostructures and decorated nanostructures.

21. The system of claim 20 wherein the decorated nanostructures comprise nanostructures decorated with at least one of metal nanoparticies, metal oxide nanoparticles, and. receptors for at least one analyte of the one or more analytes.

22. The system of claim 21 wherein the metal nanoparticles are selected from the group of gold Au, platinum Pt, palladium Pd, silver Ag, titanium Ti, copper Cu, iron Fe, nickel Ni, iridium lr, and zinc Zn.

23. The system of claim 2.1 wherein each of the plurality of electrochemical sensors comprises a field-effect transistor.

24. The system of claim 23 wherein each of the plurality of electrochemical sensors comprises a liquid-gated field-effect transistor electrochemical sensor.

25. The system of claim 24 wherein a gate electrode of the liquid-gated field-effect transistor electrochemical sensor of each of the plurality of electrochemical sensors is a reference electrode.

26. The system of claim 25 wherein the reference electrode is an Ag/AgCl reference electrode, a Pt reference electrode, or a pseudo reference electrode.

27. A method for detecting an analyte in a sample liquid, comprising: providing an automated system comprising a liquid handling system, an electronic test system, and a control system comprising a processor system and a memory system in connection with the processor system, the control system being configured to be in communicative connection with the liquid handling system and with the electronic test system, providing a printed circuity board comprising a plurality of electrochemical sensors positioned at unique positions on the printed circuity board and in electrical connection therewith, each of the plurality of electrochemical sensors comprising a substrate and a sensor medium on the substrate positioned between spaced electrodes, the sensor medium comprising at least one nanostructure, wherein at least one property of the sensor medium is sensitive to the composition of a volume of the sample liquid on a surface of the sensor medium; placing the printed circuity board in connection with the control system and with the electronic test system, applying a volume of the liquid sample via an automated liquid handling system separately to a set of one or more of the plurality of electrochemical sensors, and measuring a response of each one of the set of electrochemical sensors via the electronic test system,

28. The method of claim 27 wherein the plurality of electrochemical sensors include electrochemical sensors having different sensor mediums.

29. The method of claim 28 wherein the liquid handling system comprises a robotic system, the robotic system comprising a deck comprising a slot to position the printed circuit board thereon and one or more robotic arms, at least one of the one or more robotic arms being configured to deliver liquids including the volume of the sample liquid independently to each of the plurality of electrochemical sensors of the printed circuit board under control of the control system.

30. The method of claim 29 wherein the electronic test system is configured to be independently connected with each of the plurality of electrochemical sensors of the printed circuit board under control of the control system, the method further comprising independently applying electrical energy to each of the plurality of electrical sensors to create a bias voltage between the spaced electrodes thereof via the electronic test system, and independently measuring a response of each of the plurality of electrochemical sensors to the applied electrical energy via the electronic test system.

31. The method of claim 30 wherein the electronic test system comprises a source meter in connection with the control system and with the printed circuit board, the source meter being configured to independently apply the electrical energy to each of the plurality of electrical sensors io create the bias voltage between the spaced electrodes thereof and to independently measure the response of each of the plurality of electrochemical sensors to the applied electrical energy.

32. The method of claim 30 wherein the electronic test system further comprises a system switch configured to switch independently between connections with each of the plurality of electrochemical sensors of the primed circuit board under control of the control system,

33. The method of claim 32 wherein the liquid handling system is controlled via a first software algorithm stored in the memory’ system and executable by the processor system, and the electronic test system is controlled via a second software algorithm stored in the memory system and executable by the processor system.

34. The .method of claim 33 wherein each of the first software algorithm and the second software algorithm is configured to send requests to the control system and the control system is configured to pause or actuate one of the first software algorithm and the second software algorithm based upon the requests.

35. The method of claim 34 wherein the memory system of the control system comprises a web server software algorithm stored therein and executable by the processor system to exchange information with the first software algorithm and the second software algorithm.

36. The method of claim 30 wherein at least one of the one or more robotic arms is controlled via the control system to position a pipette using a coordinate system to at least one of (i) deliver one of the liquids to a determined electrochemical sensor or (ii) aspirate liquid from the determined electrochemical sensor.

37. The method of claim 36 wherein the deck comprises one or more slots at unique determined positions thereof, each of the slots being configured to connect a component thereto from the group consisti ng of a component compris ing a source of the sample l iquid, a component comprising a source of an electrolyte, a component comprising a source of pipettes, a component comprising a holder for a gate electrode, a component comprising a solid waste container, and a component comprising a liquid waste container.

38. The method of any one of claims 27 through 37 wherein each of the plurality of electrochemical sensors comprises a liquid-gated field effect transistor electrochemical sensor.

39. The method of claim 38 wherein the liquid handling system comprises a robotic system comprising one or more robotic arms, and wherein at least one of the one or more robotic arms is controlled via the control system to position a gate electrode to be in connection with the volume of the sample liquid on the surface of the sensor medium of a determined one of the plurality of electrochemical sensors and the electronic tests system is controlled via the control system to apply a gate voltage to the gate electrode.

40. The method of claim 38 further comprising determining a plurality of features from the response of each of the plurality of electrochemical sensors via ati analysis algorithm to determine the analyte, wherein the analysis algorithm optionally comprises one or more artificial intelligence algorithms, which optionally comprise one or more machine learning algorithms.

41. The method of claim 40 wherein the one or more artificial intelligence algorithms are configured to speciate between a plurality of analytes.

42. The method of claim 40 wherein the analyte is at least one of an opioid or a metabolite of an opioid.

43. The method of claim 28 wherein the nanostructures comprise carbon nanostructures.

44. The method of claim 43 wherein the carbon nanostructures are single-wailed carbon nanotubes.

45. The method of claim 44 wherein the single-wailed carbon nanotubes are semiconductor enriched single- walled carbon nanotubes.

46. The met hod of claim 45 wherein the nanostructures of the sensor medium of the plurality of electrochemical sensors comprise one of bare nanostructures and decorated nanostructures.

47. The method of claim 46 wherein the decorated nanostructures comprise nanostructures decorated with at least one of metal nanoparticles, metal oxide nanoparticles, and receptors for at least one analyte of the one or more analytes.

48. The method of claim 47 wherein the metal nanoparticles are selected from the group of gold Au, platinum Pt, palladium Pd, silver Ag, titanium Ti, copper Cu. iron he, nickel Ni, iridium Ir, and zinc Zn,

49. The method of claim 47 wherein each of the plurality of electrochemical sensors comprises a field-effect transistor.

50. The method of claim 49 wherein each of the plurality of elec trochemical sensors comprises a liquid-gated field-effect transistor electrochemical sensor.

51. The method of claim 50 wherein a gate electrode of the liquid-gated field-effect transistor electrochemical sensor of each of the plurality of electrochemical sensors is a reference electrode.

52. The method of claim 51 wherein the reference electrode is an Ag/AgCi reference electrode, a Pt reference, electrode, or a pseudo reference electrode.

53. A method for detection of one or more analytes in a liquid sample, comprising; providing a plurality of electrochemical sensors, each of the electrochemical sensors comprising a substrate and a sensor medium on the substrate between spaced electrodes, the sensor medium comprising at least one nanostructure, wherein at least one property of the sensor medium is sensitive to the composition of a volume of the l iquid sample on a surface of the sensor medium, and wherein the plurality of electrochemical sensors include electrochemical sensors having different sensor mediums, placing a volume of the liquid sample over the sensor medium of each of a set of the plurality of electrochemical sensors, the set of the plurality of electrochemical sensors including electrochemical sensors having different sensor mediums, independently applying a vol tage across each of the electrochemical sensors of the set of the plurality of electrochemical sensors after placing the volume of the liquid sample on the sensor medium thereof. measuring a response of each of the set of the plurality of electrochemical sensors to the applied voltage, and detecting at least one analyte of the one or more analytes in tire liquid sample based upon an algorithm configured to analyze the responses of each of the set of the plurality of electrochemical sensors.

54. The method of claim 53 wherein the one or more analytes are selected from the group consisting of opioids and metabolites of opioids,

55. The method of claim 54 wherein the nanostructures comprise carbon nanostructures.

56. The method of claim 55 wherein the carbon nanostructures are single-walled carbon nanotubes,

57. The method of claim 56 wherein the single-walled carbon nanotubes are semiconductor enriched single-walled carbon nanotubes.

58. The method of claim 57 wherein the nanostructures of the sensor medium of the plurality of e lectrochemical sensors comprise one of bare nanostructures and decorated nanostructures.

59. The method of claim 58 wherein the decorated nanostructures comprise nanostructures decorated with at least one of metal nanoparticles, metal oxide nanoparticles, and receptors for at least one analyte of the one or more analytes.

60. rhe method of claim 59 wherei n the metal nanoparticles are selected from the group of gold Au, platinum Pt, palladium Pd, silver Ag, titanium Ti, copper Cu, iron Fe, nickel Ni, iridium Ir, and zinc Zn.

61. The method of any one of claims 53 through 60 wherein each of tire plurality of electrochemical sensors comprises a field effect transistor.

62. The method of claim 61 wherein each of the plurality of electrochemical sensors comprises a liquid-gated field effect transistor electrochemical sensor.

63. The method of claim 62 wherein a gate electrode of the liquid-gated field-effect transistor electrochemical sensor of each of the plurality of electrochemical sensors is a reference electrode.

64. The method of claim 63 wherein the reference electrode is an Ag/AgCl reference elec trode, a Pt reference electrode, or a pseudo reference electrode.

65. The method of claim 53 wherein each of the plurality of electrochemical sensors is positioned at unique positions on a printed circuity board and is in electrical connection therewith.

66. The method of claim 65 further comprising providing an automated system comprising liquid handling system, an electronic test system, and a control system comprising a processor system and a memory system in connection with the processor system, the control system being configured to be in communicative connection with the liquid handling system and with the electronic test system, placing the printed circuity board in connection with the control system and with the electonic test system, applying the volume of the liquid sample via the liquid handling system separately to the set of one or more of the plurality of electrochemical sensors, and measuring a response of each one of the set of elec trochemical sensors via the electronic test system.