Patent Application
20230266279
The present disclosure relates generally to the field of gas analysis. More particularly, the present disclosure relates to the analysis of compounds contained in a gas. In some embodiments, the analysis includes the use of sniffing sequences, which can provide active, dynamic odor/gas identification with adaptive or self-optimizing capabilities.
In one aspect, a method for analyzing a gas is described. The method includes:
detecting, over time and by the sensor, a characteristic indicative of a compound or compounds present in the gas.
In any one or more of the embodiments described herein, the sniffing recipe includes a pattern of actions.
In any one or more of the embodiments described herein, the sniffing recipe includes a specified length of time for each action in the sequence.
In any one or more of the embodiments described herein, sniffing recipe further includes one or more of the following actions: (4) hold, wherein hold comprises maintaining a relatively constant atmosphere in the chamber; (5) pressurize, wherein pressurize comprises increasing the pressure in the chamber; (6) convect, wherein convect comprises circulating the contents of the chamber; (7) de-pressurize or vacuum, wherein de-pressurize or vacuum comprises reducing the pressure in the chamber; (8) priming, wherein priming comprises introducing a known compound to the chamber before introducing the gas; (9) co-injection, wherein co-injection comprises introducing a known chaperone compound simultaneously with the gas; and (10) after-injection, wherein after-injection comprises introducing a compound that affects the desorption of the gas after introducing the gas to the chamber.
In any one or more of the embodiments described herein, the sniffing recipe includes a plurality of inhale actions alternating with a plurality of hold actions.
In any one or more of the embodiments described herein, the sniffing recipe includes a pattern of actions and a specified length of time for each action in the pattern, wherein the sequence of actions and specified length of time are pre-defined.
In any one or more of the embodiments described herein, the pre-defined pattern of actions is based on the gas being analyzed.
In any one or more of the embodiments described herein, the sniffing recipe comprises a first recipe followed by a second recipe, wherein the first recipe is pre-defined and the second recipe is determined based on machine learning from measurements resulting from the first recipe.
In any one or more of the embodiments described herein, the chamber is primed with a known compound prior to injecting the gas being analyzed.
In any one or more of the embodiments described herein, a known compound is co-injected simultaneously with the gas being analyzed.
In any one or more of the embodiments described herein, a known compound is injected after injecting the gas being analyzed.
In any one or more of the embodiments described herein, the sensor includes a photonic crystal.
In any one or more of the embodiments described herein, the sensor includes a field-effect transistor (FET).
In any one or more of the embodiments described herein, exhale includes flushing the chamber with another fluid to remove the gas being analyzed. In accordance with certain embodiments, flushing the chamber with another fluid includes injecting the fluid through the inlet.
In another aspect, a device is described, including:
a chamber configured to receive a gas to be analyzed, the chamber including an inlet and an outlet; a sensor disposed in the chamber, the sensor configured to detect a characteristic indicative of a compound or compounds present in the gas; and a pump configured to operate in accordance with a sniffing recipe, wherein the sniffing recipe comprises a sequence of actions and the sniffing recipe is either pre-defined, optimized, determined through machine learning, or a combination thereof, wherein the sniffing recipe comprises:
(1) inhale, wherein inhale includes activating the pump to introduce the gas into the chamber; and at least one of the following actions:
(2) exhale, wherein exhale includes flushing the chamber to remove the gas
(3) wait, wherein wait comprises allowing a concentration of the gas to decrease slowly.
In any one or more of the embodiments described herein, the sniffing recipe further includes one or more of the following actions: (4) hold, wherein hold comprises holding the gas in the chamber; (5) pressurize, wherein pressurize comprises increasing the pressure in the chamber to sample a larger section of the adsorption isotherm; (6) convect, wherein convect comprises circulating the contents of the chamber; (7) de-pressurize or vacuum, wherein de-pressurize or vacuum comprises reducing the pressure in the chamber; (8) priming, wherein priming comprises introducing a known compound to the chamber before introducing the gas; (9) co-injection, wherein co-injection comprises introducing a known chaperone compound simultaneously with the gas; and (10) after-injection, wherein after-injection comprises introducing a compound that affects the desorption of the gas after introducing the gas to the chamber.
In any one or more of the embodiments described herein, the sensor is selected from the group consisting of a photonic crystal, a field effect transistor, a nanogenerator, and photomechatronic nanostructures.
In any one or more of the embodiments described herein, the sensor is a photonic crystal.
In any one or more of the embodiments described herein, the sensor provides a spectral response.
In any one or more of the embodiments described herein, the spectral response comprises a bandgap shift.
In any one or more of the embodiments described herein, the device further includes a spectrophotometer configured to detect the evolution of the spectral response in time.
In any one or more of the embodiments described herein, the device further includes at least one processor configured to run one or more machine learning algorithms on data provided by the sensor, the machine learning algorithm capable of determining a pattern of actions based on features of the data from the sensor, wherein at least one of the one or more machine learning algorithms comprises at least one of pattern recognition, classification, regression, and segmented regression.
In any one or more of the embodiments described herein, the one or more machine learning algorithms are selected from the group consisting of LASSO, kernel ridge regression, decision trees, bagging classifiers, multiclass logistic regression, principle component analysis, linear discriminant analysis, supervised machine learning, semi-supervised machine learning, non-supervised machine learning, support vector machines, transfer learning neural networks, segmented regression, and a combination thereof.
In any one or more of the embodiments described herein, the pump or a second pump is configured to inject a known compound(s) into the chamber in accordance with one or more of the following:
1) prior to introducing the gas being analyzed;
2) simultaneously with the gas being analyzed;
3) after introducing the gas being analyzed.
In any one or more of the embodiments described herein, the pump or a second pump is configured to introduce a known compound(s) into the chamber in accordance with one or more of the following:
1) prior to introducing the gas being analyzed;
2) simultaneously with the gas being analyzed;
3) after introducing the gas being analyzed.
In any one or more of the embodiments described herein, the device may also include a filter disposed in the chamber between the inlet and the sensor. In accordance with certain aspects, the filter includes a size exclusive mesh.
In any one or more of the embodiments described herein, the device is selected from the group consisting of an indoor sensor, a medical diagnostic device, a food quality sensor, an air quality sensor and combinations thereof.
In any one or more of the embodiments described herein, the gas being analyzed may be from a biological sample, such as a person, animal, food item, etc.
In another aspect, a device is described, including a chamber configured to receive a gas to be analyzed, wherein the chamber includes an inlet and, in some cases, an outlet. A sensor is disposed in the chamber, wherein the sensor is configured to detect a characteristic indicative of a compound or compounds present in the gas. The device is configured to operate in accordance with a sniffing recipe, wherein the sniffing recipe includes a sequence of actions and the sniffing recipe is either pre-defined, optimized, determined through machine learning, or a combination thereof, wherein the sniffing recipe includes:
(1) inhale, wherein inhale includes introducing the gas into the chamber; and at least one of the following actions:
(2) exhale, wherein exhale includes flushing the chamber to remove the gas;
(3) wait, wherein wait comprises allowing a concentration of the gas to decrease slowly.
In any one or more of the embodiments described herein, the device is a handheld device. In accordance with some embodiments, the device is a breathalyzer, smart phone or smart watch.
In any one or more of the embodiments described herein, the gas being analyzed may be a user's breath. In accordance with certain aspects, the device may provide instructions to the user to breathe in accordance with the sniffing recipe.
Any aspect or embodiment disclosed herein may be combined with another aspect or embodiment disclosed herein. The combination of one or more embodiments described herein with other one or more embodiments described herein is expressly contemplated.
Unless otherwise defined, used, or characterized herein, terms that are used herein (including technical and scientific terms) are to be interpreted as having a meaning that is consistent with their accepted meaning in the context of the relevant art and are not to be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Although the terms, first, second, third, etc., may be used herein to describe various elements, these elements are not to be limited by these terms. These terms are simply used to distinguish one element from another. Thus, a first element, discussed below, may be termed a second element without departing from the teachings of the exemplary embodiments. Spatially relative terms, such as “above,” “below,” “left,” “right,” “in front,” “behind,” and the like, may be used herein for ease of description to describe the relationship of one element to another element, as illustrated in the figures. It will be understood that the spatially relative terms, as well as the illustrated configurations, are intended to encompass different orientations of the apparatus in use or operation in addition to the orientations described herein and depicted in the figures. For example, if the apparatus in the figures is turned over, elements described as “below” or “beneath” other elements or features would then be oriented “above” the other elements or features. Thus, the exemplary term, “above,” may encompass both an orientation of above and below. The apparatus may be otherwise oriented (e.g., rotated 90 degrees or at other orientations) and the spatially relative descriptors used herein interpreted accordingly. Further still, in this disclosure, when an element is referred to as being “linked to,” “on,” “connected to,” “coupled to,” “in contact with,” etc., another element, it may be directly linked to, on, connected to, coupled to, or in contact with the other element or intervening elements may be present unless otherwise specified.
The terminology used herein is for the purpose of describing particular embodiments and is not intended to be limiting of exemplary embodiments. As used herein, singular forms, such as “a” and “an,” are intended to include the plural forms as well, unless the context indicates otherwise.
The invention is described with reference to the following figures, which are presented for the purpose of illustration only and are not intended to be limiting. In the Drawings:
There are impressive commercial sensors that mimic and outperform sight, hearing, and touch, but none that rival the sense of smell. In fact, the state-of-the-art in smell sensing is the use of dogs, which have been commonly trained for years to find drugs, explosives, and even diseases like cancer and potentially Parkinson's and Alzheimer's. Therefore, to improve man-made sensors and better mimic a nose, there is also a need to actively and quickly sample for vapors to search for identifying odors.
The present disclosure, in one or more embodiments, provides devices and methods for the detection and characterization of a gas and gas mixtures, and components thereof, using a pattern of actions that in some cases simulate sniffing, and utilizing stimuli-responsive sensors, such as photonic crystals or field-effect-transistors (FETs). Vapors of a sample or liquid can also be analyzed in accordance with the disclosed method. The methods and devices disclosed herein can also be used to analyze odor, fumes, liquid sprays, and aerosols, from biological and non-biological sources. As used herein, the term “gas” also encompasses these other types of samples that can be analyzed. The present disclosure in certain embodiments, simulates or incorporates aspects of the active sampling seen in the sniffing behaviors of dogs and other mammals to predict the properties of both liquids and gases in a diversity of applications, such as environmental (e.g., pesticide control) and medical monitoring (e.g., blood or urine). The use of sniffing sequences as disclosed herein, as opposed to existing static methods, provides active, dynamic odor/gas identification with adaptive or self-optimizing capabilities.
The use of sniffing sequences as disclosed herein, can also be used to evaluate dynamic samples that may change over time or during different test conditions. For example, the disclosed methods can be used to monitor and analyze biological samples. In some embodiments, the methods can be used to measure kinetics of biological systems such as bacterial systems wherein the changing systems can be sensed, classified, and regressed. The methods disclosed herein may be particularly useful for applications such as sensing biological growth signatures (providing a quantitative and/or qualitative analysis of bacteria), applications of food quality sensing, determining the kinetics and dynamics of bacterial growth and making inferences of environmental variables based on biological changes. In accordance with some embodiments, the sniffing sequences as disclosed herein can be used to analyze biological samples in which the gas analytes being sensed themselves are evolving from the sample due to various biological processes. This method can be used to understand the biological signature of such samples using machine learning and can undergo classification of bacterial make-up of samples, kinetics of bacterial growth—in cases where the substrate is a sample of food, the classification can be used to determine the quality of the substrate (such as healthiness/spoilage of food).
In terms of assessing the properties of the gas or gas mixtures being analyzed, approaches set forth in one or more embodiments herein provide several advantages when compared to other methods. For example, in some embodiments, the design of the architecture of, for example, photonic sensors or field effect transistor (FET) sensors allows for their unique gas sorption behavior to be exploited, and, thus, enable their application for a discriminative analysis of the compounds in the gas.
As shown in
In accordance with some embodiments, a gas or mixture of gasses for analysis is injected into, introduced into, removed from, or modified in a chamber containing a sensor using a sequence of sniffing steps, also referred to herein as actions. Using these sniffing steps, the gasses in the chamber can be changed through inhale, exhale, wait, hold, pressurize, convect or de-pressurize steps that control one or more of the various actions and properties, such as gas flow into and out of the chamber, concentration of gas in the chamber, the conditions in the chamber, opening and closing of the inlet and outlet valves. Inhale comprises introducing a sample gas to the chamber. In accordance with one aspect, the sample gas is introduced into the chamber through the inlet. In accordance with certain aspects, inhale comprises injecting the sample gas through the inlet and into the chamber. In accordance with some embodiments, the inlet and outlet to the chamber are both open. As a result, the concentration of the analyte in the chamber increases without intentionally also increasing the pressure inside the chamber. Exhale comprises cleansing the sensor. In accordance with one aspect, the sensor is cleaned by flushing the chamber to remove the sample gas. In accordance with one aspect, exhale comprises injecting a baseline gas or mixture of gasses (such as dry air) to flush the chamber to remove the sample gas. In accordance with some aspects, the inlet and outlet are both open such that the sample gas concentration quickly decreases. This procedure can also be used to flush the chamber before each analysis to ensure that the sensor, such as the photonic crystal—e.g., the Bragg stack—is clean. Wait comprises allowing the sample gas concentration to decrease slowly. In accordance with one aspect, wait comprises closing the inlet without injecting any gas into the chamber but leaving the outlet open such that the sample gas concentration decreases slowly over time. Hold comprises maintaining a relatively constant atmosphere in the chamber. In accordance with one aspect, hold comprises closing the inlet and outlet to keep a relatively constant atmosphere in the chamber. Pressurize comprises increasing the pressure in the chamber to sample a larger section of the adsorption isotherm. In accordance with one aspect, pressurize comprises opening the inlet to a sample gas while keeping the outlet closed to increase the pressure in the chamber to sample a larger section of the adsorption isotherm. This could allow one to probe specific odors and interactions at elevated pressures and concentrations.
Furthermore, sniffing recipes could include combinations of sniffing features or actions to provide additional functionality to the system. For example, inhales for two compounds could be combined (either sequentially or at the same time), varying the mixing ratio to probe the competitive or combined adsorption of one known and one unknown species. Additional sniffing steps or actions can also be used to modify the behavior inside the chamber from the outside by heating, cooling, magnetic fields, ionization, or by introducing a known or unknown second gas or vapor before, during, or after injection of the sample to modify the pattern of gasses reaching the sensor and the sensor response. In some embodiments, Priming the sensor with a known compound before the introduction or injection of the tested mixture is described. Such step may promote the adsorption of the tested species or prevent the adsorption of the unwanted species in the gas mixture. In some embodiments, Co-injection of the tested gas or gas mixture with a known chaperone compound can take place simultaneously. The chaperone molecule can bind to a tested gas species, thus facilitating its adsorption and affecting its adsorption/desorption kinetics, or bind to certain components of the gas mixture, and creating species that are prevented from adsorption. In some embodiments, an After-injection step is used to inject a compound that affects the desorption step of an unknown gas or gas mixture. The steps of co-injection and after-injection also encompass introducing the compound or gas simultaneously or subsequently, respectively.
Sniffing recipes may include various sniffing steps in a sequence, which may have different durations, and take place at different pressures, or temperatures. Sniffing offers a potentially endless combination to dynamically modify the sensing without the need to replace parts of the sensor. Sniffing steps can be combined into any sequence to control the flow of gasses or vapors into and through the chamber and produce unique signatures to characterize the character (e.g., dangerous or not, healthy or sick, etc.), composition, or physical properties of the samples. In certain embodiments, as shown in
The dimensions and materials of the chamber can be modified to limit and/or promote the flow of gasses and or the adsorption, desorption, diffusion, and condensation of gasses onto the surface or specific segments of the surface, including introducing other materials such as drying agents, porous materials, liquids, or others.
In some embodiments, the chamber can include glass, Teflon, or other solvent-resistant materials.
In some embodiments, one or more inside surfaces of the chamber 102 can have the same or different homogeneous or heterogeneous chemical patterns on the nanoscale and microscale.
In some embodiments, one or more inside surfaces of the chamber 102 can have the same or different homogeneous or heterogeneous topography patterns on the nanoscale and microscale.
In some embodiments, the surface of the inside of the chamber can be a hierarchical surface containing surface features on multiple length scales. In accordance with one aspect, the surface at the bottom of the inside of the chamber can be a hierarchical surface containing surface features on multiple length scales. For example, in some embodiments, the surface can have a first topological feature having dimensions on the microscale and a second topological feature on the nanoscale. In these embodiments, the first topological feature supports the second smaller topological feature. In some embodiments, the second topological features are referred to as “primary structures” as they are meant to denote the smallest feature sizes of the hierarchical structure. In these embodiments, the primary structures can include structures, such as nanofibers, or nanodots. In these embodiments, such nanoscale “primary structures” can have at least one kind of feature sizes that are a few to tens or hundreds of nanometers in size, such as less than 5 nm to 200 nm. For example, in these embodiments, nanofibers can have diameters of approximate 5, 10, 25, 50, or 100 nm. In some embodiments, in such cases, when “primary structures” having feature sizes of about 100 nm diameter are utilized, “secondary structures” having feature sizes that are larger than 100 nm, such as 150 nm, 300 nm, 500 nm, or 1000 nm, and larger can be utilized. Additional higher order structures, such as “tertiary structures,” each of which can have larger feature sizes than the lower order structures, are used in some embodiments.
In some embodiments, the chamber base has flat, round rectangular, square, triangular, or a geometrically complex shape with an area ranging from 1 mm2 to 10000 mm2. In some embodiments, the chamber has a height between about 0.1 cm to about 100 cm, more particularly between about 1 cm to about 30 cm.
In these embodiments, the homogeneous or heterogeneous chemical or topography patterns of one or more inside surfaces of the chamber 102 can tune the selectivity and sensitivity of the device for analyzing a gas or gas mixtures. For example, inclusion of pores or channels of various sizes on one or more surfaces of the chamber 102 can alter properties (e.g., kinetics) of wetting, evaporation, diffusion, or convection based on some analytes being able to enter the pores/channels (e.g., due to molecular size) and some not. Similarly, in some embodiments, inclusion of chemical coatings on one or more surfaces of the chamber 102 can alter properties (e.g., kinetics) of wetting, evaporation, diffusion, or convection based on intermolecular interactions between some analytes and said chemical coatings, but not other analytes. Thus, in these embodiments, for a given gas or gas mixture, the time it takes for certain analytes to reach the sensor can be altered via these chemical and topological modifications, thereby selecting detection of one or more analytes over one or more other analytes.
For example, in some embodiments, the one or more inside surfaces can be functionalized with silyl groups. Non-limiting examples of such silyl groups include perfluorooctyltrichlorosilane, triethoxsilylbutyraldehyde, bis(2-hydroxyethyl)-3-aminopropyltriethoxysilane, 3-chloropropyltriethoxysilane, 3-(trihydroxysilyl)-1-propanesulfonic acid, n-(triethoxysilylpropyl)-alpha-poly-ethylene oxide urethane, n-(trimethoxysilylpropyl)ethylene diamine triacetlc acid, n-octyltriethoxysilane, n-octadecyltriethoxysilane, (3-trimethoxysilylpropyl)diethylenetriamine, methyltriethoxysilane, hexyltrimethoxysilane, 3-aminopropyltriethoxysilane, hexadecyltriethoxysilane 3-mercaptopropyltrimethoxysilane, and dodecyltriethoxysilane, or chiral functionalities including N-(3-triethoxysilylpropyl)gluconamide or (R)—N-triethoxysilylpropyl-O-quinineurethane). In some embodiments, the one or more inside surfaces can be a roughened by including a porous material. In these embodiments, the roughened surface includes both the surface of a three-dimensionally porous material as well as solid surface having certain topographies, whether they have regular, quasi-regular, or random patterns. In some embodiments, the surface can be roughened by incorporation of micro textures. In other embodiments, the substrate can be roughened by incorporation of nano textures.
In some embodiments, microparticles or nanoparticles are applied to the surface to form a roughened, porous surface. In these embodiments, microparticles or nanoparticles can be applied to the surface using photolithography, projection lithography, electron-beam writing or lithography, depositing nanowire arrays, growing nanostructures on the surface of a substrate, soft lithography, replica molding, solution deposition, solution polymerization, electropolymerization, electrospinning, electroplating, vapor deposition, layered deposition, rotary jet spinning of polymer nanofibers, contact printing, etching, transfer patterning, microimprinting, self-assembly, boehmite formation, spray coating, and combinations thereof.
In some embodiments, the surface can include a fluoropolymer. Non-limiting examples of fluoropolymers can include polytetrafluoroethylene, polyvinylfluoride, polyvinylidene fluoride, and fluorinated ethylene propylene.
In some embodiments, the surface can include a plurality of holes, a three-dimensionally interconnected network of holes, or random array of fibrous materials.
In some embodiments, the roughened surface can be formed over a two-dimensionally flat surface by providing certain raised structures or protrusions. In other embodiments, the roughened surface can be formed by forming pores over a two-dimensionally flat surface to yield a porous material. In these embodiments, pores can have any geometry and can include pathways, columns, or random patterns. In yet other embodiments, a three-dimensionally interconnected network of regular or random pores is used, which can include open-cell bricks, post arrays, parallel grooves, open porosity PTFE (ePTFE), plasma-etched PTFE, and sand-blasted polypropylene (PP).
In certain embodiments, the roughened surface may have a periodic array of surface protrusions (e.g., posts or peaks) or any random patterns or roughness. In some embodiments, the size of the features producing the roughened surface can range from 10 nm to 100 μm, with geometries ranging from regular posts or open-grid structures to randomly oriented spiky structures. In some embodiments, the features can range from be any combination of low and high values of 10 nm, 25 nm, 50 nm, 100 nm, 250 nm, 500 nm, 1 m, 2.5 μm, 5 μm, 10 μm, 25 μm, 50 μm, or 100 am. In some embodiments, the widths of the raised structures can be constant along their heights. In some embodiments, the widths of the raised structures can increase as they approach the basal surface from the distal ends. In some embodiments, the raised structures can be raised posts of a variety of cross-sections, including, but not limited to, circles, ellipses, or polygons (e.g., triangles, squares, pentagons, hexagons, octagons, and the like), forming cylindrical, pyramidal, conical, or prismatic columns. Although the exemplary substrates described in these embodiments illustrate raised posts having uniform shape and size, the shape, orientation or size of raised posts on a given substrate can vary.
In some embodiments, a range of surface structures with different feature sizes and porosities can be used. In these embodiments, feature sizes can be in the range of hundreds of nanometers to microns (e.g., 50 to 1000 nm), and have aspect ratios from 1:1 to 10:1, from 1:1 to 2:1, from 1:1 to 3:1, from 1:1 to 4:1, from 1:1 to 5:1, from 1:1 to 6:1, from 1:1 to 7:1, from 1:1 to 8:1, and from 1:1 to 9:1. In some embodiments, porous nano-fibrous structures can be generated in situ on the inner surfaces of metallic microfluidic devices using electrochemical deposition techniques.
In some embodiments, the adsorption-desorption kinetics of vapors or gasses onto the sensor is the primary means to produce a sensor response. The evolution of the resulting time-dependent signal can be analyzed using machine learning. Examples of particularly useful sensors include, but are not limited to, a photonic crystal, a field effect transistor, a nanogenerator, photomechatronic nanostructures, light dependent resistor (LDR), photodiode, photo-transistor, solar cell, and chemiresistor sensors.
In some embodiments, the sensor can be on the side or at the end of a microfluidic channel on or in one or more surfaces of the chamber.
In some embodiments, microporous sensing materials for photonic, field effect transistor (FET), or nanogenerator-based sensors can include metal-organic framework (MOF) materials. In these embodiments, metal-organic framework (MOF) materials can be crystalline compounds consisting of rigid organic molecules held together and organized by metal ions or clusters (e.g., ZIF-8, CAU, and HKUST). In some embodiments, the metal organic framework (MOF) materials can include surface-mounted metal-organic frameworks (SURMOFs), iso-reticular metal-organic frameworks (IRMOFs), covalent organic framework (COF), zeolitic inorganic framework (ZIF), or a combination thereof. In some embodiments, the metal-organic framework (MOF) material is a porous material. In some embodiments, the metal-organic framework (MOF) materials can be functionalized to bind and interact with various volatile analytes including, but not limited to, ammonia, carbon dioxide, carbon monoxide, hydrogen, amines, methane, oxygen, argon, nitrogen, argon, organic dyes, polycyclic organic molecules, and combinations thereof. In some embodiments, the metal organic framework (MOF) materials can include a chemically-sensitive resistor, where the metal organic framework (MOF) material is disposed in-between conductive leads and undergoes a change in resistance when the material sorbs a volatile analyte. In these embodiments, the change in electrical resistance between the leads can be correlated to the sorption of a volatile analyte to the sensor material. Additional examples of metal organic framework (MOF) materials and their use in sensors can be found in U.S. Pat. Nos. 8,735,161, 8,480,955, and International Application No. PCT/US2015/049402, which are hereby incorporated by reference in their entirety.
In some embodiments, the sensing materials for photonic, field effect transistor (FET), and nanogenerator-based sensors can include conducting and non-conducting polymeric networks. In some embodiments, the polymeric networks can be cross-linked (e.g., hydrogels and elastomers) and non-cross-linked. In some embodiments, these materials can undergo changes in optical properties (e.g., due to a refractive index change), electrical properties (e.g., conductance), phase transitions and physical dimensions (e.g., upon swelling/contraction and a consequent change in refractive index or resistance) in response to sorption of one or more volatile analytes, and which can be analyzed to characterize the analyte. Non-limiting examples of polymers that can form the polymeric network according to some embodiments include polyaniline, polypyrrole, polythiophene, poly(phenylene sulphide-phenyleneamine), perylene tetracarboxylic diimide, polyurethane, polystyrene, poly(methyl methacrylate), polyacrylate, polyalkylacrylate, substituted polyalkylacrylate, polystyrene, poly(divinylbenzene), polyvinylpyrrolidone, poly(vinylalcohol), polyacrylamide, poly(ethylene oxide), polyvinylchloride, polyvinylidene fluoride, polytetrafluoroethylene, and other halogenated polymers, hydrogels, organogels, and combinations thereof. In some embodiments, the polymers can include random and block copolymers, branched, star and dendritic polymers, and supramolecular polymers. In some embodiments, the polymers can include one or more natural materials, such as cellulose, natural rubber (e.g., latex), wool, cotton, silk, linen, hemp, flax, feather fiber, and combinations thereof.
In some embodiments, the sensitivity (i.e., detection limit) of the sensor can be less than 5 ppm, with detectable refractive index change of up to ˜10−7.
In some embodiments, the sensor is a photonic crystal. In some embodiments, the photonic crystal can be a porous photonic crystal (PPC). In some embodiments, the porous photonic crystal can be a 1-dimensional porous photonic crystal, 2-dimensional porous photonic crystal, or 3-dimensional porous photonic crystal.
In some embodiments, the sensor is a field effect transistor. For the electronic sensing according to these embodiments, the gate material for the field-effect transistor (FET) or the material of the nanogenerator electrodes can include one or more micro- and mesoporous layers that permit adsorption of the analyte of interest. In some embodiments, the porous layer can be chemically functionalized, and this functionalization, together with the pore geometry, can collectively affect the diffusion rates of gas or vapor into or within the pores. In some embodiments, the pore geometry, layer thickness, porosity, and surface functionalization can be varied, individually or collectively, to obtain a desired sensitivity to an analyte of interest. In some embodiments, the pore geometry, layer thickness, porosity, and surface functionalization can be varied, individually or collectively, to affect biological kinetics of a sample. Non-limiting examples of field-effect transistors and methods of tuning their sensitivity to an analyte of interest can be found, for example, in International Patent Application No. PCT/IB2007/051764, which is hereby incorporated by reference in its entirety.
In some embodiments, the sensor is a nanogenerator. Non-limiting examples of nanogenerators include surface-acoustic-wave-actuated piezo-electric nanogenerators or triboelectric photonic nanogenerators. Additional non-limiting examples of nanogenerator-based sensors can be found in U.S. Pat. No. 9,595,894, the contents of which are hereby incorporated by reference in their entirety.
In some embodiments, the field-effect transistor (FET) or nanogenerator sensing material can comprise non-porous materials, such as conducting polymers, which exhibit physical changes, e.g., a change of conductance, when exposed to different chemicals. In some embodiments, the gate electrode layer can comprise metals such as Ta, Fe, W, Ti, Co, Au, Ag, Cu, Al, or Ni, or organic materials such as PSS/PEDOT or polyaniline. In some embodiments, the gate electrode material is chosen such that it is a good conductor. In some embodiments, the first dielectric layer can comprise amorphous metal oxides such as Al2O3 and Ta2O5, transition metal oxides such as HfO2, ZrO2, TiO2, BaTiO3, SrTiO3, BaZrO3, PbTiO3, and LiTaO3, rare earth oxides such as Pr2O3, Gd2O3, and Y2O3, or silicon compounds such as Si3N4, SiO2 and microporous layers of SiO and SiOC. In some embodiments, the first dielectric layer can comprise polymers such as SU-8, BCB, PTFE, or even air. In some embodiments, the source electrode and the drain electrode can be fabricated using metals such as aluminium, gold, silver or copper or, alternatively, conducting organic or inorganic materials. In some embodiments, the organic semiconductor can comprise materials selected from poly(acetylene)s, poly(pyrrole)s, poly(aniline)s, poly(arylamine)s, poly(fluorene)s, poly(naphthalene)s, poly(p-phenylene sulfide)s or poly(phenylene vinylene)s. In these embodiments, the semiconductor also may be n-doped or p-doped to enhance conductivity. In some embodiments, the second dielectric layer can include the same materials listed for the first dielectric layer. In some embodiments, the second dielectric layer also shields the layers below from outside conditions, therefore waterproof coatings such as PTFE or silicones may be used in these embodiments.
In some embodiments, the sensor can include an organic semiconductor. Non-limiting examples of organic semiconductors according to one or more embodiments include pentacene, anthracene, rubrene, phthalocyanine, CC, CO-hexathiophene, α-dihexylquaterthiophene, α-dihexylquinquethiophene, α-dihexylhexathiophene, bis(dithienothiophene), dihexyl-anthradithiophene, n-decapentafluorophenylmethylnaphthalene-1-tetracarboxylic diimide, Ceo CeO infused organic polymers, poly(9,9-dioctylfluorene-alt-benzothiadiazole) (F8BT), poly(p-phenylene vinylene), poly(acetylene), poly(thiophene), poly(3-alkylthiophene), poly(3-hexylthiophene), poly(triarylamines), oligoarylamines, poly(thienylenevinylene), and combinations thereof.
In some embodiments, mesoporous sensing materials for photonic, field effect transistor (FET), and nanogenerator-based sensors can be fabricated by alternating spin-, dip-, or spray-coating of nanoparticle suspensions of materials with a high refractive index contrast. Non-limiting examples of materials with high refractive index contrast, in accordance with some embodiments, include silica, alumina, iron oxide, zinc oxide, tin oxide, alumina silicates, aluminum titanate, beryllia, noble metal oxide, platinum group metal oxide, titania, zirconia, hafnia, molybdenum oxide, tungsten oxide, rhenium oxide, tantalum oxide, niobium oxide, vanadium oxide, chromium oxide, scandium oxide, yttria, lanthanum oxide, ceria, thorium oxide, uranium oxide, and other rare earth oxides, and combinations thereof. In some embodiments, such colloidal nanoparticle suspensions can be synthesized by wet-chemistry methods, e.g., sol-gel hydrolysis.
In some embodiments, the separation distance between the inlet 104 and the sensor 108 can be varied to tune the sensitivity of the device for analyzing gasses and gas mixtures.
In some embodiments, the photonic crystals can be a thin film on a transparent substrate, e.g., glass, the shape of which can be, for example, flat, round, spherical, and the like.
In some embodiments, multiple sensors or sensor arrays, each with its own response to the sniffing sequences can be placed within the chamber, and their combined effect can be analyzed.
In some embodiments, the detection time of the sensor 108 depends upon the configuration of the device 100, including, for example, the position of the sensor 108 on or in the chamber 102, the speed of the injection into the inlet 104, the volume of the gas injected, the possibility of gas leakage from the chamber 102, and the porosity and surface chemistry of the sensor 108.
Furthermore, in some embodiments, the kinetics discussed above can be tuned by the temperature of the device. In some embodiments, the temperature can increase slowly or in steps.
In some embodiments, the plurality of sensor responses can include a spectral response. In some embodiments, the spectral response can include a bandgap shift. In some embodiments, the plurality of sensor responses (e.g., spectral responses or bandgap shift) can be detected using a spectrometer or a spectrophotometer.
In some embodiments, the plurality of sensor responses can include a color change. In some embodiments, the color change can be detected using a camera. In some embodiments, the camera can be a smartphone camera. In these embodiments, the color change detected by the camera can be converted into a spectral response. In these embodiments, these images can be converted to an RGB color model, which can in turn be converted to a HSV color model. In these embodiments, the wavelength corresponding to each color present in the HSV color model can be estimated, which can provide the spectral shift.
In some embodiments, the plurality of spectral responses can include contour plots, wavelength derivative plots, Fourier amplitude and phases, and their derivatives, histogram of gradients, wavelet transforms, and a combination thereof.
The present disclosure utilizes active, dynamic sampling of a gas or mixture of gasses to improve analysis results. Sniffing sequences can be used to actively sample the gas being analyzed. In accordance with some embodiments, sniffing sequences are provided through a pattern of actions, wherein the pattern of actions may be either pre-defined, optimized or determined through machine learning. Sniffing sequences can include those pre-defined based on experiments, machine learning methods, or human intuition. Sequences may be generated using e.g., artificial intelligence or machine learning methods such as supervised, semi-supervised, self-supervised, and unsupervised methods (including methods based on federated learning) and used for e.g., classification, regression, clustering, etc. Examples of sniffing features or actions include the following:
(1) inhale, wherein inhale comprises introducing the gas into the chamber, typically injecting the gas through the inlet, wherein the inlet and outlet are both open;
(2) wait, wherein wait comprises allowing the sample gas concentration to decrease slowly, typically by opening the outlet without injecting any gas;
(3) exhale, wherein exhale comprises cleansing the sensor, such as by flushing the chamber to remove the sample gas, typically by injecting a carrier gas or mixture of gases into the chamber through the inlet, wherein the inlet and outlet are both open;
(4) hold, wherein hold comprises maintaining a relatively constant atmosphere in the chamber, typically by holding the gas in the chamber with both the inlet and outlet closed;
(5) convect, wherein convect comprises circulating the gas in the chamber, typically with both the inlet and outlet closed; and
(6) de-pressurize or vacuum, wherein the pressure in the chamber is reduced.
A sniffing recipe can be used to control the concentration of odor in the sensor chamber. As the sensor (e.g., photonic Bragg stack) is exposed to the vapors of a particular compound, the adsorption of the vapors into a thin film causes the reflectance of the Bragg stack to shift towards redder colors which is caused by the increase in the effective refractive index in the porous layers of the Bragg stack. This redshift can be recorded using a spectrophotometer. The evolution of the shift of the reflectance peak over time can be quantified using the phase of a Fourier transform. The shift of the spectrum can be shown using the phase (instead of the phase derivative) to show the increase in the adsorption in the Bragg crystal. Because the vapors are injected directly into the chamber, the color of the crystal changes rapidly and experiments rely predominantly on the adsorption and desorption kinetics of different vapors. Focusing on adsorption and directing the injection of the vapors straight at the photonic crystal accelerates the analysis substantially.
In some embodiments, some basic sniffing patterns include, but are not limited to, the following: (1) Fast and short sniffing 110, where the odor is inhaled in a number of short bursts before exhaling, and (2) deep sniffing 120, where the odor is inhaled in a single breath. During fast sniffing, short (e.g., 1 second) intervals of inhaling are interrupted by 0.5 second intervals of waiting, allowing the odor to distribute in the chamber. After a total of 5 inhales and waits, the odor is exhaled for 5 seconds as nitrogen flushes the chamber and sensor (e.g., Bragg stack). By interrupting the flow of odorant into the chamber, the concentration at the sensor periodically decreases and never fully equilibrates. Instead, the phase approaches and then oscillates around a dynamic mean between adsorption maximum and desorption minimum.
In deep sniffing, the odor may be inhaled for a long period of time before any of the other actions such as wait, pressurize, exhale. In accordance with a non-limiting example the inhaling period can be a 5 second inhale followed by a 2.5 second wait—before the odor is again exhaled for 5 seconds. These values are representative only and can be varied as warranted by the particular analysis being conducted. For example, the inhale period can be any multiple or single sequences of lengths such as 1 second, 5 seconds, 10 seconds, 50 seconds, 100 seconds, 5 minutes, or 10 minutes. As a result, the total amount of odorant adsorbed onto the sensor during deep sniffing is likely higher.
In short sniffing, the odor may be inhaled for a relatively shorter period of time before any sequence of actions such as wait, pressure, exhale or even inhale are performed. A non-limiting example is a short sniff for 0.5 seconds can be followed by a 1 second wait. This sequence can be repeated for a specified number of times (e.g., 5) in succession, before finally a long exhale (e.g., 5 seconds). This inhale period can be any multiple or single sequence of lengths such as 0.1 seconds, 0.5 seconds, 1 seconds, 2 seconds, 5 seconds, or 15 seconds.
The sequences of sniffing steps in
As shown in
Specific parameters relating to the sniff pattern will depend on the configuration of the sampling device, the dynamics of the sensor itself, and the properties of the pump or other device (e.g., vacuum) used to move the sample through the chamber. Accordingly, these parameters can vary significantly in consideration of these other variables. In accordance with some embodiments, the following values may be considered as general guides:
short inhale or wait step: 0.01-3 seconds; 0.1-1 seconds; 0.5-1 seconds
deep inhale or long wait step: 1-30 seconds; 3-15 seconds; 5-10 seconds
exhale: 0.5-300 seconds; 1-20 seconds; 3-10 seconds.
Of course, as noted above these values should not be considered limiting as they will depend on the particular device and samples being tested.
In accordance with some embodiments, the disclosed sniffing sequences can be associated with other methods/variation other than temporal changes. Long and short sniffs aim to modulate the integration time of the signal. However, other sniff patterns such as low pressure sniffs vs. high pressure sniffs can give key information on the physical properties of the sample (evaporation rate, diffusivity, etc.) and its signature dependence on pressure; classification accuracy can be increased using this method.
In accordance with some embodiments, the disclosed sniffing sequences can include but are not limited to low-concentration vs high-concentration sniffs. In accordance with this aspect, the sniffing of the sample may be filtered. In accordance with certain embodiments, the filter may be a physical mesh disposed between the sample and sensor. The filter can either allow the gas to permeate through the mesh to provide for complete sensing or the filter can be used to resist the gas from reaching the sensor in which a low-concentration sniff is maintained. This can be useful for probing over-saturated signals without changing time integration of the sniff sequence. In accordance with other aspects, the filter can be selectively permeable in which the sensor can get information on particular analytes at a time. A non-limiting example includes where the sample produces gases A, B, C. In accordance with this example, a first mesh allows gas A to permeate easily, ˜50% of B to permeate (or a slow permeation), and completely resists the permeation of gas C. A second mesh enables gas C to permeate completely, but resists permeation of A, and B. This unique permeation signature through the filter between the sample and sensor allows the signal to be partially de-convoluted. This has capabilities to strengthen machine learning classification.
In accordance with one embodiment, the filter can be a size exclusive mesh that prevents gas analytes above certain size from traversing. In accordance with another embodiment, the filter that may operate based on van der Waals or polarity to prevent certain partially charged gases from transporting through the filter. Filters can be used to filter non-polar analytes from polar analytes during sensing or to filter smaller molecules. In accordance with one particular example, filters can be used to facilitate distinguishing nitrogen gas, ammonia, and putrescine. All three compounds described are nitrogen containing compounds and can be detected by a sensor sensitive to nitrogen containing compounds. However, only putrescine among these is a compound signatory of spoilage of food, whereas other nitrogen containing compounds are merely byproducts of other processes (for example, N2 is a carrier gas). Thus, being able to use selective filter to suppress the dominant compound (N2, a carrier gas) can be useful to discern more useful smells of putrescine that can be low in concentration but the compound of interest.
As exemplified in
As exemplified in
As shown in
Different sniffing recipes can be used to increase the ability of the sensor to differentiate between various chemicals, e.g., by using pressurize to increase the rate of adsorption and thereby increase the contrast between different chemicals (
Phase signal for low-concentration toluene in water mixtures using two sniffing sequences are shown in
Phase signal for low-concentration toluene in water mixtures using two sniffing sequences with a different baseline gas containing water vapor to isolate the signal for toluene are provided in
In certain embodiments, the sniffing element may be any of optical, acoustic, temperature, pressure, chemical, pH, type sensors or any combination thereof.
In certain embodiments, the sniffing element may be part of a larger device in which the device is a smart mobile device (either smart phones, or smart watches) in which the smart device is equipped with at least one of the sniffing elements to detect any of diseases, illnesses, air quality, illicit drugs consumption, or any other blood adsorbed compounds that can emit volatile compounds in breath.
In certain embodiments, the device may be used by persons to monitor progression of diseases, monitor self-health, or perform non-invasive evaluation of certain compounds.
In certain embodiments, the device can be used by law enforcement personnel to detect use of illicit drugs to prevent illegal activities. In other embodiments, the same device can be used by health professionals to diagnose consumption of illicit drugs of unconscious patients to improve health treatment.
In certain embodiments, the sensing device can be of the form of a smart device in which the data is seamlessly transmitted to a continuously monitored server.
In certain embodiments, the sensing is used to monitor closed spaces for unsafe environments such as development of asphyxiation hazards, or dangerous chemicals.
In certain embodiments, the sensor can be deployed to detect spread of biological compounds.
The sniffing sequences can be used on biological samples, which themselves are evolving in time. The biological samples can be food items such as meats, vegetables, fruits, or even dairies products. These samples can be detected via various device forms such as traditional indoor sensors, smart mobile sensors, packaging or container sensors, or smart refrigerator sensors. These sensors can detect for spoilage of contents, predict expected lifetime (best before date) of foods, or even profile the current bacterial content within the food.
In certain embodiments, the sniffing device can be used to diagnose development of harmful bacteria such as Salmonella on meats.
In certain embodiments, the sniffing device can be used to detect spoilage of any food item using odor signatures from bacteria development and their by-products.
In certain embodiments, the sniffing device can be a hand-held mobile smartphone in which the sensing can provide a quick method to probe a food item before consumption.
In certain embodiments, the sniffing device can be part of the food packaging in which the state of the food can be evaluated from the packaging (such as RFID scan), or a color indicator on the wrapping.
In certain embodiments, the sniffing device can be part of a container in which various food devices can be sensed and detected by a universal database on the cloud or server. In these embodiments, more usage of such a device strengthens the sensing capabilities and accuracy of the device as each use by consumer is tracked and added as training points for machine learning purposes.
In certain embodiments, the sniffing device can be setup in a particular 3-dimensional arrangement within a smart refrigerator, or a storage unit, that allows of sensing multiple foods together. In such an embodiment, the 3-dimensional configuration of various sensors allows the detection of spoilage of a particular food within the fridge.
In certain embodiments, the smart applications of such sniffing devices can be used to predict estimated lifetime of food items; the time before the consumption of such a food item poses no/or minimal health concerns.
In certain embodiments, the sniffing device can be used at the industrial scale to ensure healthy food is maintained in stock. In other industrial applications, the device may serve to profile bacterial species and concentrations. In such embodiments, the use of this device can be used for food engineering purposes such as probiotic foods. In a society where nutritional and dietary choices are becoming more accessible to consumers, the use of such device can be used to probe bacterial content and engineer foods with heathy bacteria to improve the overall health of consumers and specifically gut microbiome.
In some embodiments, the disclosed methods and devices for analyzing gasses or gas mixtures via detecting the time evolution of the plurality of dynamic responses uses data acquisition and analysis routines. In these embodiments, the data acquisition and analysis routines can lead to a high dimensionality, i.e., the number of possible independent variables, of the sensing platform, which was not possible with other single-output and combinatorial steady-state sensors, and which can be implemented to perform the compositional analysis of analytes that are not included in a data library (i.e., “unknowns”) via supervised and unsupervised machine learning frameworks (MHLFs).
In some embodiments, the machine learning frameworks facilitate the characterization and classification of single-component and gas mixtures, as well as the recognition of specific components, for example through the formation of a library of sensor responses. In some embodiments, the use of an array of photonic structures or field effect transistors (FETs) with the same or different porosities and surface functions can enhance the accuracy and precision of the machine learning methods. In some embodiments, various machine learning (or “self-learning”) algorithms can be implemented to perform classification, regression, and clustering tasks. In some embodiments, various machine learning (or “self-learning”) algorithms can, in part, enable analysis of the composition of the gas mixture. Non-limiting examples of the machine learning algorithms include supervised machine learning algorithms, unsupervised machine learning algorithms, semi-supervised machine learning algorithms, support vector machines, transfer learning neural networks, and segmented regression algorithms. Additional machine learning algorithms include, but are not limited to, artificial intelligence algorithms including e.g., recurrent neural networks, convolutional neural networks, transformers, generative adversarial networks, auto encoders, and natural language processing algorithms more broadly. Still other machine learning algorithms that may be used include, but are not limited to, reinforcement learning (e.g., policy iteration, value iteration, SARSA method, Q-Learning, Deep Q learning and any off-policy or on-policy variations with or without neural networks), support vector regression, probabilistic models, mixture models, topic models, inference bayes networks, hidden Markov models, clustering models, K-Means and Hierarchical Agglomerative Clustering (HAC).
In some embodiments, the experimentally obtained data can first be pre-processed to extract nuanced independent features from the plurality of spectral responses (e.g., via contour plots, Fourier transform amplitudes and phases and their derivatives, wavelength derivative plots, histogram of gradients, or wavelet transforms), and then imported into a classifier, e.g., a support vector machine or principal components analyzer, or a regressor (e.g., linear, radial basis function, LASSO, or ridge support vector regressors) with optimized performance, to perform pattern recognition and discrimination of the composition of the volatile liquid mixture. In some embodiments, monitoring the response of a photonic sensor can be performed using a spectrometer or a camera and converting the recorded data into color models. In some embodiments, monitoring the response of a field effect transistor (FET) sensor or nanogenerator can be performed through measuring the current-voltage signal or the time-dependent current change. In some embodiments, the obtained profiles can be further processed and combined into data vector for further classification.
In some embodiments, the choice of machine learning framework can vary as a function of the application. In some embodiments, where signal processing is performed, to obtain a list of features from the measured data, support vector machines are a useful first choice. In these embodiments, support vector machines can be used for classification of analytes into hazard classification, compound classes, or based on other features using support vector classifiers. In addition, in these embodiments, support vector regressors are suitable for analyses of concentration ranges and physical parameters. In some embodiments, more specialized classification and regression algorithms, such as bagging classifiers, can be useful to, for example, divide the dataset for further analysis or segment a range of mixtures into smaller regression ranges. In some embodiments the sensor data is used without post-processing. In these embodiments, advanced machine learning frameworks, such as neural networks, transfer learning, and deep neural networks, are useful. In these embodiments, transfer learning, in particular, can be applied to improve sensor accuracy with limited datasets. Examples of transfer learning that can be used include, but are not limited to, zero-shot, one-shot, and few-shot learning.
In some embodiments, machine learning frameworks constitute a useful analysis tool for the non-equilibrium sensing method according to one or more embodiments described herein. In some embodiments, the machine learning framework includes a series of sensor signal-preprocessing methods (e.g., transform and normalization), followed by extraction and selection of the sensor features from the initial multidimensional fingerprints, and followed by classification, regression, clustering, and cross-validation. In some embodiments, if the analyte is not from the training data set and the supervised classification/regression is not possible, the machine learning framework can establish an unsupervised model for mapping the unknown fingerprint with the target physico-chemical properties. Examples of the machine learning frameworks according to some embodiments include LASSO, kernel ridge regression, support vector machine, neural networks (including transfer learning neural networks), GANs, decision trees, bagging classifiers, multiclass logistic regression, principal component analysis, and linear discriminant analysis.
It will be appreciated that while one or more particular materials or steps have been shown and described for purposes of explanation, the materials or steps may be varied in certain respects, or materials or steps may be combined, while still obtaining the desired outcome. Additionally, modifications to the disclosed embodiment and the invention as claimed are possible and within the scope of this disclosed invention. For example, different sensor types and configurations, surface treatments, measurement parameters, data analysis techniques, and other aspects discussed above can be combined in various combinations to tune the apparatus to particular analytes or characteristics to measure, as will be apparent to those of skill in the art.
This application claims the benefit of priority under 35 U.S.C. § 119(e) to co-pending U.S. Application Ser. No. 63/047,086, filed Jul. 1, 2020, the contents of which are incorporated in their entirety by reference. All patents, patent applications and publications cited herein are hereby incorporated by reference in their entirety in order to more fully describe the state of the art as known to those skilled therein as of the date of the invention described herein.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/US2021/040126 | 7/1/2021 | WO |
| Number | Date | Country | |
|---|---|---|---|
| 63047086 | Jul 2020 | US |
