WO2018216017A1 - Capteurs de dioxyde de carbone comprenant un polymère liquide ionique - Google Patents

Capteurs de dioxyde de carbone comprenant un polymère liquide ionique Download PDF

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Publication number
WO2018216017A1
WO2018216017A1 PCT/IL2018/050564 IL2018050564W WO2018216017A1 WO 2018216017 A1 WO2018216017 A1 WO 2018216017A1 IL 2018050564 W IL2018050564 W IL 2018050564W WO 2018216017 A1 WO2018216017 A1 WO 2018216017A1
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sensor
sensing layer
swcnts
pil
carbon black
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PCT/IL2018/050564
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English (en)
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Hossam Haick
Gilad PORAT
Gidi Shani
Manal ABUD HAWA
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Technion Research And Development Foundation Ltd.
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Publication of WO2018216017A1 publication Critical patent/WO2018216017A1/fr

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    • C—CHEMISTRY; METALLURGY
    • C09—DYES; PAINTS; POLISHES; NATURAL RESINS; ADHESIVES; COMPOSITIONS NOT OTHERWISE PROVIDED FOR; APPLICATIONS OF MATERIALS NOT OTHERWISE PROVIDED FOR
    • C09C—TREATMENT OF INORGANIC MATERIALS, OTHER THAN FIBROUS FILLERS, TO ENHANCE THEIR PIGMENTING OR FILLING PROPERTIES ; PREPARATION OF CARBON BLACK  ; PREPARATION OF INORGANIC MATERIALS WHICH ARE NO SINGLE CHEMICAL COMPOUNDS AND WHICH ARE MAINLY USED AS PIGMENTS OR FILLERS
    • C09C1/00—Treatment of specific inorganic materials other than fibrous fillers; Preparation of carbon black
    • C09C1/44—Carbon
    • C09C1/48—Carbon black
    • C09C1/56—Treatment of carbon black ; Purification
    • C—CHEMISTRY; METALLURGY
    • C01—INORGANIC CHEMISTRY
    • C01B—NON-METALLIC ELEMENTS; COMPOUNDS THEREOF; METALLOIDS OR COMPOUNDS THEREOF NOT COVERED BY SUBCLASS C01C
    • C01B32/00—Carbon; Compounds thereof
    • C01B32/05—Preparation or purification of carbon not covered by groups C01B32/15, C01B32/20, C01B32/25, C01B32/30
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
    • G01N27/02—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance
    • G01N27/04—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance by investigating resistance
    • G01N27/12—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance by investigating resistance of a solid body in dependence upon absorption of a fluid; of a solid body in dependence upon reaction with a fluid, for detecting components in the fluid
    • G01N27/125—Composition of the body, e.g. the composition of its sensitive layer
    • G01N27/127—Composition of the body, e.g. the composition of its sensitive layer comprising nanoparticles
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/0004—Gaseous mixtures, e.g. polluted air
    • G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
    • G01N33/0027—General constructional details of gas analysers, e.g. portable test equipment concerning the detector
    • G01N33/0036—General constructional details of gas analysers, e.g. portable test equipment concerning the detector specially adapted to detect a particular component
    • G01N33/004—CO or CO2
    • C—CHEMISTRY; METALLURGY
    • C01—INORGANIC CHEMISTRY
    • C01P—INDEXING SCHEME RELATING TO STRUCTURAL AND PHYSICAL ASPECTS OF SOLID INORGANIC COMPOUNDS
    • C01P2004/00—Particle morphology
    • C01P2004/60—Particles characterised by their size
    • C01P2004/62—Submicrometer sized, i.e. from 0.1-1 micrometer
    • C—CHEMISTRY; METALLURGY
    • C01—INORGANIC CHEMISTRY
    • C01P—INDEXING SCHEME RELATING TO STRUCTURAL AND PHYSICAL ASPECTS OF SOLID INORGANIC COMPOUNDS
    • C01P2004/00—Particle morphology
    • C01P2004/60—Particles characterised by their size
    • C01P2004/64—Nanometer sized, i.e. from 1-100 nanometer
    • C—CHEMISTRY; METALLURGY
    • C01—INORGANIC CHEMISTRY
    • C01P—INDEXING SCHEME RELATING TO STRUCTURAL AND PHYSICAL ASPECTS OF SOLID INORGANIC COMPOUNDS
    • C01P2006/00—Physical properties of inorganic compounds
    • C01P2006/12—Surface area

Definitions

  • the present invention relates to carbon dioxide (C0 2 ) sensors and sensing systems comprising a CO2 absorbing poly(ionic liquid) (PIL) and an electrically conductive material selected from single walled carbon nanotubes and carbon black.
  • C0 2 carbon dioxide
  • PIL poly(ionic liquid)
  • Carbon dioxide (CO2) is a byproduct of various industrial processes, combustion of hydrocarbons, metabolism of nearly all biologically activity species, and volcanic activity. Since industrialization, the effect of the increased anthropogenic emissions of CO2 on the atmosphere has been felt in the ecological system and drove political and social attention to controlling said emissions. Moreover, high CO2 concentrations have adverse effects on human health, ranging from difficulty in breathing and headache to serious respiratory distress and death.
  • Chemiresistors are chemical sensors which exhibit a change in electrical resistance in response to changes in the nearby chemical environment, for example, upon detection of an analyte.
  • a chemiresistor consists of a conductive sensitive film deposited over a pair of electrodes. Sorption of the analyte in the sensitive layer changes conductivity of the film.
  • Chemical CO2 gas sensors with sensitive layers benefit from very low energy consumption, compact size, which can be easily fitted into microelectronic systems and fast response times. However, chemical sensors suffer from short lifetime and low durability due to short- and long-term drift effects.
  • PILs Poly(ionic liquid)s
  • PILs are promising materials for CO2 sensing, capture and separation processes, showing high CO2 uptake and fast and reversible absorption and desorption rates.
  • Most PILs are non-volatile solids and unlike polyelectrolytes, which are usually glassy, PILs are similar to polymers, making them stable and easily manipulated for incorporation into various devices. Nevertheless, while sorption of CO2 in PILs has been studied, little work has been devoted for sensing applications using PILs (Huang, J. et al. "3D-ordered macroporous poly(ionic liquid) films as multifunctional materials" Chem. Commun. 46, 967-969 (2010); Mineo, P. G. et al.
  • the PIL-wrapped SWCNTs chemiresistor reported by Jin et al. has a detection limit of 500 ppt to 50 ppm (0.00005% - 0.005%). With fresh air containing 350-400 ppm CO2, this sensor is unsuitable for practical applications.
  • chemiresistor CO2 sensor utilizing PILs employed a composite of Poly[(p- vinylbenzyl)trimethylammonium hexafluorophosphate] and inorganic La202C0 3 nanoparticles (Willa, Christof, Yuan, Jiayin, Niederberger, Matkus & Koziej, Dorota. "When nanoparticles meet poly(ionic liquid)s: Chemoresi stive CO2 sensing at room temperature" Adv. Funct. Mater. 25 (2015): 2537-2542.). The chemiresistor reported by Willa et al. has a detection limit of 150-2400 ppm (0.015%) - 0.24%)), which is still insufficient for many practical applications. In addition, the use of the La202C0 3 nanoparticles introduces an additional synthesis step leading to a higher production cost.
  • the present invention provides carbon dioxide (C0 2 ) sensor comprising a composite CO2 sensing layer for the direct detection of CO2.
  • the composite CO2 sensing layer comprises CO2 absorbing poly(ionic liquid) (PIL) and an electrically conductive material.
  • the poly(ionic liquid) which is incorporated in the sensing layer of the disclosed sensors comprises a diallyldimethylammonium (DADMA) cation.
  • DADMA diallyldimethylammonium
  • the present invention is based in part on a surprising finding that a sensor comprising DADMA-based PILs provides a significantly higher sensitivity towards CO2 at a wide concentration range as compared to sensors comprising other PILs, such as, for example, poly[(p-vinylbenzyl) trimethylammonium hexafluorophosphate] (P[VBTMA][PF6]) and poly[l-vinyl-3-(2-methoxy-2-oxyl ethyl) imidazolium chloride-co- ethyl ene glycol dimethacrylate].
  • P[VBTMA][PF6] poly[(p-vinylbenzyl) trimethylammonium hexafluorophosphate]
  • P[VBTMA][PF6] poly[l-vinyl-3-(2-methoxy-2-oxyl ethyl) imidazolium chloride-co- ethyl ene glycol dimethacrylate].
  • the PIL which is used in the sensors of the invention can include different types of anions. As such, sensitivity, selectivity and/or specificity of the sensor towards the detection of CO2 can be further regulated.
  • the sensing layer of the CO2 sensors of the invention further includes an electrically conductive material, selected from single walled carbon nanotubes (SWCNTs) and carbon black particles.
  • SWCNTs single walled carbon nanotubes
  • carbon black particles can be combined with various DADMA-based PILs to form a CO2 sensing layer.
  • Sensors based on PILs combined with carbon black particles provided reliable and reproducible detection of CO2. Owing to the low cost and easy handling of carbon black, the use of carbon black in the CO2 sensors offers a promising alternative to SWCNTs.
  • the present invention further provides methods of fabrication of the sensors.
  • the present invention further provides a sensing system for the detection of CO2 comprising at least one PIL and conductive material-based sensor and a sample feeding system.
  • the sample feeding system is a dual feeding system, which enables decoupling of a sensor response to CO2 from the response to other gases or volatile organic compounds. Said dual sample feeding system can provide enhanced accuracy and selectivity, when used in combination with the sensors of the present invention.
  • the sensor and sensing system are configured for detecting CO2 concentrations from about 0.05 % up to 20% CO2 in air, or in other complex environments.
  • the present invention provides an accurate and portable sensor for use in the detection of CO2 at ambient conditions including STEL (3%), IDLH concentration and physiological exhaled breath concentration (4-5%) of CO2.
  • a sensor for the detection of carbon dioxide comprising: a composite CO2 sensing layer comprising: a CO2 absorbing poly(ionic liquid) (PIL) comprising a diallyldimethylammonium (DADMA) cation and an anion; and a conductive material comprising at least one of single walled carbon nanotubes (SWCNTs), and carbon black particles; at least two electrodes; and a substrate, wherein the at least two electrodes are disposed on the substrate or on the sensing layer and are in electric contact with the sensing layer.
  • PIL poly(ionic liquid)
  • DADMA diallyldimethylammonium
  • the conductive material comprises SWCNTs or carbon black particles.
  • the weight percent of the conductive material in the sensing layer ranges from about 3% to about 70% of the total weight of the sensing layer.
  • the conductive material comprises SWCNTs.
  • the SWCNTs are wrapped by the PIL.
  • the single walled carbon nanotubes have diameters ranging from about 1 nm to about 5 nm, and lengths ranging from about 1 micrometer to about 50 micrometers.
  • the weight percent of the SWCNTs ranges from about 30% to about 70% of the total weight of the sensing layer.
  • the conductive material comprises carbon black particles.
  • the carbon black particles are dispersed in the PIL.
  • the carbon black particles have a particle size which is smaller than about 500 nm and/or a surface area ranging from about 50 to about 100 m 2 /g.
  • the weight percent of the carbon black particles ranges from about 3% to about 50% of the total weight of the sensing layer.
  • the anion is selected from the group consisting of acetate (Ac), trifluoroacetate (TFAc), methanesulfonate (MS), trifluoromethanesulfonate (TFMS), benzoate (Bz), nitrate (NO3), and chloride (CI) ions.
  • Ac acetate
  • TFAc trifluoroacetate
  • MS methanesulfonate
  • TFMS trifluoromethanesulfonate
  • benzoate Bz
  • NO3 nitrate
  • chloride (CI) ions chloride
  • the sensing layer comprises from about 0.001 to about 10 ⁇ g of conductive material. In some embodiments, the sensing layer comprises from about 0.001 to about 100 ⁇ g PIL. In further embodiments, the area of the sensing layer ranges from about 0.1 mm 2 to about 50 mm 2 .
  • the senor of the present invention is configured in a form selected from the group consisting of a resistive sensor, a chemiresistive sensor, a capacitive sensor, an impedance sensor, and a field effect transistor sensor. Each possibility represents a separate embodiment of the invention. According to certain embodiments, the sensor is configured in a form of a chemiresi stive sensor.
  • a sensing system for the detection of C0 2 comprising at least one sensor, as presented hereinabove; and a sample feeding system.
  • the sample feeding system comprises at least two sample feeding tubes, which are in contact with the at least one sensor.
  • at least one tube comprises a CO2 adsorbent material.
  • said adsorbent material comprises a solid amine sorbent.
  • said CO2 adsorbent material is removable and/or reusable.
  • at least one tube in the sample feeding system does not include a CO2 adsorbent.
  • the sample feeding system is configured to alternately prevent the exposure of the at least one sensor to a gas sample through each one of the sample feeding tubes.
  • the conductive material comprises SWCNTs or carbon black particles.
  • the sensing system further comprises a computing system configured for executing various algorithms stored on a non-transitory memory, wherein said computing system receives output signals of the at least one sensor.
  • said computing system compares the signal obtained from exposure of the at least one sensor to the gas sample through the at least one tube, which does not include a CO2 adsorbent to the signal obtained from exposure of the at least one sensor to the gas sample through the at least one tube, which comprises a CO2 adsorbent.
  • the algorithm is selected from the group consisting of: artificial neural network (ANN) algorithm, support vector machine (SVM), discriminant function analysis (DFA), principal component analysis (PC A), multi -layer perceptron (MLP), generalized regression neural network (GRNN), fuzzy inference system (FIS), self- organizing map (SOM), radial basis function (RBF), genetic algorithm (GA), neuro-fuzzy system (NFS), adaptive resonance theory (ART), partial least squares (PLS), multiple linear regression (MLR), principal component regression (PCR), linear discriminant analysis (LDA), cluster analysis, Fisher linear discriminant analysis (FLDA), soft independent modeling of class analogies (SEVICA), K-nearest neighbors (KNN), fuzzy logic algorithms, canonical discriminant analysis (CD A) and combinations thereof.
  • ANN artificial neural network
  • SVM support vector machine
  • DFA discriminant function analysis
  • PC A principal component analysis
  • MLP multi -layer perceptron
  • GRNN generalized regression neural network
  • FIS fuzzy inference system
  • SOM self- organizing
  • the sensing system further comprises a detection device for measuring a change in at least one property of the at least one sensor, the at least one property being selected from the group consisting of resistance, conductance, direct current (DC), alternating current (AC), frequency, capacitance, impedance, inductance, electrical potential, and voltage threshold.
  • a detection device for measuring a change in at least one property of the at least one sensor, the at least one property being selected from the group consisting of resistance, conductance, direct current (DC), alternating current (AC), frequency, capacitance, impedance, inductance, electrical potential, and voltage threshold.
  • the sensing system has a response and recovery time between measurements of less than about 10 minutes. According to some additional embodiments, the sensing system is configured for the detection of C0 2 concentrations ranging from about 0.05 % to about 20% CO2 in a gas sample.
  • the sensing system comprises a sensor array comprising diallyldimethylammonium trifluoroacetate and SWCNTs-based sensor, diallyldimethylammonium methanesulfonate and SWCNTs-based sensor, diallyldimethylammonium benzoate and SWCNTs-based sensor, and diallyldimethylammonium nitrate and SWCNTs-based sensor.
  • the sensing system comprises a sensor array comprising diallyldimethylammonium trifluoroacetate and carbon black particles-based sensor and diallyldimethylammonium benzoate and carbon black particles-based sensor, and diallyldimethylammonium nitrate and carbon black particles-based sensor.
  • a method for fabricating the sensor for the detection of carbon dioxide (CO2) comprising: providing a substrate; forming at least two electrodes; preparing a composite CO2 sensing layer composition comprising a CO2 absorbing poly(ionic liquid) (PIL) comprising diallyldimethylammonium (DADMA) cation and an anion; and a conductive material comprising at least one of single walled carbon nanotubes (SWCNTs), and carbon black particles; and applying said composite CO2 sensing layer composition onto the substrate or the at least two electrodes, thereby forming a composite CO2 sensing layer which is in electric contact with the at least two electrodes.
  • PIL poly(ionic liquid)
  • DADMA diallyldimethylammonium
  • the conductive material comprises SWCNTs or carbon black particles.
  • the step of forming the composite CO2 sensing layer composition comprises: dispersing SWCNTs in an organic solvent to form a mixture; adding the PIL to the mixture; grinding the mixture for at least about 20 minutes, to form a paste; and washing the paste with the appropriate solvent to form a substantially stable dispersion comprising from about 5 to about 150 ppm SWCNTs and PIL.
  • the step of forming the composite C0 2 sensing layer composition comprises: dispersing carbon black particles in a solvent to form a mixture; adding the PIL to the mixture to form a dispersion; and sonicating the dispersion for at least about 30 minutes.
  • the step of applying the composite CO2 sensing layer composition onto the substrate or the at least two electrodes comprises drop casting from about 0.1 to about 5 ⁇ . of the composite CO2 sensing layer composition onto the substrate or the at least two electrodes.
  • the area to which the composite CO2 sensing layer composition is applied ranges from about 0.1 mm 2 to about 50 mm 2 .
  • a method of determining the concentration of CO2 in a gas sample comprising the steps of: providing a sensing system for the detection of CO2, comprising (a) at least one sensor comprising a composite CO2 sensing layer comprising: CO2 absorbing poly(ionic liquid) (PIL) comprising diallyldimethylammonium (DADMA) cation and an anion; and a conductive material comprising at least one of single walled carbon nanotubes (SWCNTs), and conductive carbon black particles; at least two electrodes; and a substrate and (b) a sample feeding system comprising a first sample feeding tube comprising a CO2 adsorbent material and a second sample feeding tube, which does not include a CO2 adsorbent material, wherein said sample feeding tubes are in contact with the at least one sensor; exposing the at least one sensor to the gas sample through the first sample feeding tube, thereby obtaining a confounding signal; exposing the at least one sensor to the gas sample
  • the sensing system further comprises a computing system configured for executing at least one algorithm stored on a non-transitory memory, wherein said computing system receives the confounding signal and the test signal of the at least one sensor.
  • the step of comparing the test signal with the confounding signal is performed by the computing system using an algorithm selected from the group consisting of: artificial neural network (ANN) algorithm, support vector machine (SVM), discriminant function analysis (DFA), principal component analysis (PCA), multi-layer perceptron (MLP), generalized regression neural network (GRNN), fuzzy inference system (FIS), self-organizing map (SOM), radial basis function (RBF), genetic algorithm (GA), neuro-fuzzy system (NFS), adaptive resonance theory (ART), partial least squares (PLS), multiple linear regression (MLR), principal component regression (PCR), linear discriminant analysis (LDA), cluster analysis, Fisher linear discriminant analysis (FLDA), soft independent modeling of class analogies (SEVICA), K-nearest neighbors (KNN), fuzzy
  • ANN artificial neural network
  • the gas sample is selected from the group consisting of air, breath sample, modified atmosphere packaging (MAP), fresh produce head space, and automobile emission.
  • MAP modified atmosphere packaging
  • fresh produce head space fresh produce head space
  • automobile emission fresh produce head space
  • Figure 1A schematically represents sensor 10 including substrate 12, two electrodes 14, and sensing layer 16 disposed on the substrate between the two electrodes, in accordance with some embodiments of the invention.
  • Figure IB shows an exploded view of sensor 20 including substrate 22, plurality of pairs of electrodes 24, and sensing layer 26 disposed on top of the electrodes, in accordance with some embodiments of the invention.
  • Figure 1C schematically represents sensor 30 including substrate 32, plurality of pairs of electrodes 34, and sensing layer 36 disposed on the substrate, wherein the electrodes are disposed on top of the sensing layer, in accordance with some embodiments of the invention.
  • Figure ID schematically represents sample feeding system 100, including first tube 130, which includes a C0 2 adsorbent material and second tube 131 which does not include a CO2 adsorbent material, in accordance with some embodiments of the invention.
  • Figures 2A-2C show response features of a SWCNTs/ [poly[(p- vinylbenzyl)trimethylammonium hexafluorophosphate] (also termed herein "PIL 2")]-based sensor to increasing concentrations of CO2:
  • Figure 2A represents the area under the curve;
  • Figure 2B represents the relative resistance change in the end of the exposure step;
  • Figure 2C represents the relative resistance change in the beginning of the exposure step.
  • Figures 3A-3C show response features of a SWCNTs/ [poly(diallyldimethylammonium Benzoate) (also termed herein "PIL 3 [Bz]”)]-based sensor to increasing concentrations of C0 2 :
  • Figure 3 A represents the area under the curve;
  • Figure 3B represents the relative resistance change in the end of the exposure step; and
  • Figure 3C represents the relative resistance change in the beginning of the exposure step.
  • Figures 4A-4C show response features of a SWCNTs/ [poly(diallyldimethylammonium Trifluoroacetate) (also termed herein "PIL 3 [TFAc]”)]-based sensor to increasing concentrations of C0 2 :
  • Figure 4 A represents the area under the curve
  • Figure 4B represents the relative resistance change in the end of the exposure step
  • Figure 4C represents the relative resistance change in the beginning of the exposure step.
  • Figures 5A-5C show response features of a SWCNTs/ [poly(diallyldimethylammonium Trifluoromethanesulfonate) (also termed herein "PIL 3 [TFMS]”)]-based sensor to increasing concentrations of C0 2 :
  • Figure 5A represents the area under the curve;
  • Figure 5B represents the relative resistance change in the end of the exposure step;
  • Figure 5C represents the relative resistance change in the beginning of the exposure step.
  • Figures 6A-6C show response features of a SWCNTs/ [poly(diallyldimethylammonium Methanesulfonate) (also termed herein "PIL 3 [MS]”)] -based sensor to increasing concentrations of C0 2 :
  • Figure 6A represents the area under the curve;
  • Figure 6B represents the relative resistance change in the end of the exposure step;
  • Figure 6C represents the relative resistance change in the beginning of the exposure step.
  • Figures 7A-7C show response features of a SWCNTs/ [poly (diallyldimethylammonium Chloride) (also termed herein "PIL 3 [Cl]”)]-based sensor to increasing concentrations of C0 2 :
  • Figure 7 A represents the area under the curve;
  • Figure 7B represents the relative resistance change in the end of the exposure step; and
  • Figure 7C represents the relative resistance change in the beginning of the exposure step.
  • Figures 8A-8C show response features of a SWCNTs/ [poly(diallyldimethylammonium Nitrate) (also termed herein "PIL 3 [N0 3 ]”)]-based sensor to increasing concentrations of C0 2 :
  • Figure 8 A represents the area under the curve;
  • Figure 8B represents the relative resistance change in the end of the exposure step; and
  • Figure 8C represents the relative resistance change in the beginning of the exposure step.
  • Figures 9A-9C show response features of a PIL3 [TFAc]/carbon black sensor to increasing concentrations of C0 2 :
  • Figure 9A represents the area under the curve;
  • Figure 9B represents the relative resistance change in the end of the exposure step;
  • Figure 9C represents the relative resistance change in the beginning of the exposure step.
  • the sensor' s polymeric mass fraction was 90%.
  • Figures lOA-lOC show response features of a f PIL3 [N0 3 ]/carbon black sensor to increasing concentrations of C0 2 :
  • Figure 10A represents the area under the curve;
  • Figure 10B represents the relative resistance change in the end of the exposure step;
  • Figure IOC represents the relative resistance change in the beginning of the exposure step.
  • the sensor's polymeric mass fraction was 90%.
  • Figures 11A-11C show response features of a PIL3 [MS]/carbon black sensor to increasing concentrations of C0 2 :
  • Figure 11A represents the area under the curve;
  • Figure 11B represents the relative resistance change in the end of the exposure step;
  • Figure 11C represents the relative resistance change in the beginning of the exposure step.
  • the sensor' s polymeric mass fraction was 90%.
  • Figures 12A-12C show response features of a PIL3 [Bz]/carbon black sensor to increasing concentrations of C0 2 :
  • Figure 12A represents the area under the curve;
  • Figure 12B represents the relative resistance change in the end of the exposure step;
  • Figure 12C represents the relative resistance change in the beginning of the exposure step.
  • the sensor' s polymeric mass fraction was 90%.
  • Figures 13A-13C show response features of a duplicate of PIL3 [Bz]/carbon black sensor to increasing concentrations of C0 2 :
  • Figure 13A represents the area under the curve;
  • Figure 13B represents the relative resistance change in the end of the exposure step;
  • Figure 13C represents the relative resistance change in the beginning of the exposure step.
  • the sensor's polymeric mass fraction is 70%.
  • Figures 14A-14C show response features of a SWCNTs/PIL3 [Bz] sensor to increasing concentrations of C0 2 :
  • Figure 14A represents the area under the curve;
  • Figure 14B represents the relative resistance change in the end of the exposure step;
  • Figure 14C represents the relative resistance change in the beginning of the exposure step.
  • C0 2 concentration gradually increases from 0.05% up to 20%, and is then reduced back to 0.05%.
  • concentration of 2.75% C0 2 is marked by hollow rectangles in both directions.
  • Figures 15A-15C show the response features of a SWCNTs/PIL3 [TFAc] sensor to increasing concentrations of C0 2 :
  • Figure 15A represents the area under the curve;
  • Figure 15B represents the relative resistance change in the end of the exposure step;
  • Figure 15C represents the relative resistance change in the beginning of the exposure step.
  • C0 2 concentration gradually increases from 0.05% up to 20%, and is then reduced back to 0.05%.
  • the concentration of 2.75% CO2 is marked by hollow rectangles in both directions.
  • Figures 16A-16C show the response features of a SWCNTs/PIL3 [MS] sensor to increasing concentrations of C0 2 :
  • Figure 16A represents the area under the curve;
  • Figure 16B represents the relative resistance change in the end of the exposure step;
  • Figure 16C represents the relative resistance change in the beginning of the exposure step.
  • CO2 concentration gradually increases from 0.05% up to 20%, and is then reduced back to 0.05%.
  • concentration of 2.75% CO2 is marked by hollow rectangles in both directions.
  • Figures 17A-17C show the response features of a SWCNTs/PIL3 [TFMS] sensor to increasing concentrations of CO2:
  • Figure 17A represents the area under the curve;
  • Figure 17B represents the relative resistance change in the end of the exposure step;
  • Figure 17C represents the relative resistance change in the beginning of the exposure step.
  • CO2 concentration gradually increases from 0.05% up to 20%, and then reduced back to 0.05%. To illustrate the reversibility in sensor response. 2.75% CO2 is marked in black in both directions.
  • Figure 18A-18C show response features of a PIL3 [Bz]/carbon black sensor comprising 70%) wt. polymer to increasing concentrations of CO2:
  • Figure 18A represents the area under the curve;
  • Figure 18B represents the relative resistance change in the end of the exposure step;
  • Figure 18C represents the relative resistance change in the beginning of the exposure step.
  • CO2 concentration gradually increases from 0.05% up to 20%, and is then reduced back to 0.05%.
  • concentration of 2.75% CO2 is marked by hollow rectangles in both directions.
  • Figures 19A-19C show response features of a PIL3 [Bz]/carbon black sensor comprising 90% wt. polymer to increasing concentrations of CO2:
  • Figure 19A represents the area under the curve;
  • Figure 19B represents the relative resistance change in the end of the exposure step;
  • Figure 19C represents the relative resistance change in the beginning of the exposure step.
  • CO2 concentration gradually increases from 0.05% up to 20%, and is then reduced back to 0.05%.
  • concentration of 2.75% CO2 is marked by hollow rectangles in both directions.
  • Figures 20A-20C show response features of a PIL3 [TFAc]/carbon black sensor comprising 90%) wt. polymer to increasing concentrations of CO2:
  • Figure 20A represents the area under the curve;
  • Figure 20B represents the relative resistance change in the end of the exposure step;
  • Figure 20C represents the relative resistance change in the beginning of the exposure step.
  • C0 2 concentration gradually increases from 0.05% up to 20%, and then reduced back to 0.05%.
  • 2.75% CO2 is marked in black in both directions.
  • CO2 concentration gradually increases from 0.05% up to 20%, and is then reduced back to 0.05%.
  • the concentration of 2.75% CO2 is marked by hollow rectangles in both directions.
  • Figure 21 shows a principal component analysis (PC A) of the responses of an array of sensors to bell peppers' volatile organic compounds (VOCs) and to increasing CO2 concentrations in a background containing bell peppers' VOCs. Every data point corresponds to the multidimensional features of one exposure to a specific concentration (.
  • the analysis of the sensor array responses consist of the following sensors: SWCNTs/PIL3 [Bz], SWCNTs/PIL3 [TFAc], SWCNTs/PIL3 [MS] SWCNTs/PIL3 [N03], PIL3 [Bz]/carbon black sensor comprising 70% wt. polymer and PIL3 [TFAc]/carbon black sensor comprising 90% wt. polymer.
  • Figures 22A-22C shows response features of a PIL2/carbon black sensor comprising 90% wt. polymer to increasing concentrations of CO2 added to air from the headspace of a bag of bell peppers, wherein “x” represents 0.05% C0 2 , “— “ represents 0.15% C0 2 , “ ⁇ ” represents 0.35% C0 2 , “ A” represents 1% C0 2 , “ ⁇ ” represents 20% C0 2 , represents 3% C0 2 , “ “ represents 7.5% C0 2 , " ⁇ ” represents 0% C0 2 .
  • Figure 22A represents the area under the curve
  • Figure 22B represents the relative resistance change in the end of the exposure step
  • Figure 22C represents the relative resistance change in the beginning of the exposure step.
  • the present invention provides a sensor for the detection of carbon dioxide (CO2) and a method of preparation of said sensor.
  • the sensor of the present invention comprises a composite CO2 sensing layer for the direct detection of CO2.
  • the composite CO2 sensing layer comprises CO2 absorbing poly(ionic liquid) (PIL) and an electrically conductive material.
  • the poly(ionic liquid) which is incorporated in the sensing layer of the disclosed sensor comprises a diallyldimethylammonium (DADMA) salt polymer comprising a DADMA cation and an anion.
  • DADMA diallyldimethylammonium
  • the sensing layer of the CO2 sensors of the invention further includes electrically conductive material, selected from single walled carbon nanotubes (SWCNTs) and carbon black particles.
  • the present invention is based in part on the surprising finding that sensor comprising DADMA-based PILs provides a significantly higher sensitivity towards C0 2 with a wide practical concentration range as compared to sensors comprising some other PILs, inter alia, those currently known in the field of CO2 sensing technology.
  • sensors based on poly[(p- vinylbenzyl)trimethylammonium hexafluorophosphate] PIL showed inferior performance, as compared to the DADMA-based sensors, when tested at essentially similar conditions.
  • carbon black particles can be combined with various DADMA-based PILs to form a CO2 sensing layer.
  • Sensors based on PILs combined with carbon black particles were found to provide a reliable and reproducible detection of CO2.
  • its use in the CO2 sensors offers a promising alternative to SWCNTs.
  • preparation of the sensing layer comprising carbon black does not involve wrapping of the conductive material by the PIL as in the case of SWCNTs.
  • the use of carbon black therefore, affords for a convenient and versatile preparation process, which allows adjusting the content of the conductive material in the sensing layer to control sensitivity and/or detection limit of the sensor towards the detection of CO2.
  • the PIL which is used in the sensors of the invention can include different types of anions. This way, sensitivity, selectivity, specificity and/or detection limit of the sensor towards the detection of CO2 can be further tuned. Additionally, a combination of sensors incorporating DADMA-based PILs with different anions can provide sensitivity towards different concentrations of CO2 without significantly changing the type of chemistry of the sensors and their preparation procedure.
  • the present invention further provides a sensing system for the detection of CO2 comprising at least one sensor and a sample feeding system.
  • the sensing system according to the principles of the present invention is configured for detecting CO2 concentrations from about 0.05 % up to at least 20% CO2 in air, or in other complex environments. Additionally, said sensing system invention enables direct, non-destructive detection of the inert CO2 gas.
  • the sample feeding system is configured to allow sensor exposure to a CC -free sample in addition to the test sample, thereby enabling elimination of the effect of other gases or volatiles on the detection of CO2. Said sample feeding system can provide enhanced accuracy and selectivity of the CO2 detection, using the P[DADMA]-based sensors of the invention.
  • the present invention provides a sensor for the detection of CO2 comprising: a composite CO2 sensing layer comprising: CO2 absorbing poly(ionic liquid) (PIL) comprising a diallyldimethylammonium (DADMA) cation and an anion; and a conductive material comprising at least one of single walled carbon nanotubes (SWCNTs), and conductive carbon black particles; at least two electrodes; and a substrate, wherein the at least two electrodes are disposed on the substrate or on the sensing layer and are in electric contact with the sensing layer.
  • PIL poly(ionic liquid)
  • DADMA diallyldimethylammonium
  • the sensing layer comprises a poly(ionic liquid), which is based on a diallyldimethylammonium ionic liquid.
  • PIL poly(ionic liquid)
  • IL ionic liquid
  • Ionic liquids are organic salts in which the ions are poorly coordinated, resulting in reduced intermolecular interactions and breakage of the symmetry of the chemical structure. Therefore, ILs have a wide range of liquid-state temperature and low glass transition temperature and they are often in a liquid state at room temperature.
  • a bulky organic cation e.g., alkyl-substituted ammonium, imidazolium, or pyrrolidinium
  • an inorganic or organic anion is typically paired with an inorganic or organic anion.
  • the PIL comprises a DADMA cation and an anion, which counters the positive charge of the cation.
  • the DADMA-containing PIL is also termed herein P[DADMA].
  • the DADMA cation and a counter anion constitute a monomer of the PIL
  • Poly(ionic liquid)s are polyelectrolytes based on ILs monomers. Most PILs are non-volatile solids and unlike polyelectrolytes, which are usually glassy, they are similar to polymers, making them stable and easily manipulated for incorporation into various materials. PILs combine the properties of ILs (negligible vapor pressure, nonflammability, high ionic conductivity, wide electrochemical window and chemical and thermal stability) with the properties of polymers. Moreover, PILs benefit from extreme tunability, which allows customizing their properties via selection of cation and anion combinations. There are a few factors which need to be considered when fabricating a gas sensor.
  • the sensing material needs to have high affinity towards the analyte molecule, and in the case of gas molecules - high gas solubility coefficient. Further, the sensing material has to be selective (i.e., having high solubility selectivity). Additionally, in order to fabricate reliable sensors, such sensors should be reversible and reproducible. Accordingly, absorption of the analyte molecule in the sensing material should be reversible, with fast desorption rates and minimal retention. Furthermore, in order to enable wide sensing range, the sensing material should be responsive to different concentrations of the analyte molecule. Last but not least, the sensing material should have mechanical and chemical stability.
  • PILs are promising materials for C0 2 sensing, capture and separation processes, showing high CO2 uptake and fast and reversible absorption and desorption rates (Yuan, J., Mecerreyes, D. & Antonietti, M “Poly(ionic liquid)s: An update” Prog. Polym. Sci. 38, 1009-1036 (2013)).
  • the cation on the PIL backbone affects CO2 sorption in the polymer, as evident by higher solubility in tetralkyl ammonium type cations compared to imidazolium type cation, it is generally accepted that the cation plays a secondary role while the nature of the anion dictates the overall solubility of CO2 in the polymer (Bhavsar, R.
  • the sensing layer comprises a polymer comprising poly(diallyldimethylammonium)-based monomers.
  • DADMA poly(ionic liquid) comprising anion [X] (P [DADMA] [X]) is presented hereinbelow:
  • the cation monomer DADMA is widely used in various applications and is therefore abundant and cheap.
  • the water-soluble polymer P[DADMA][X] provides an effective anion exchange reaction.
  • Bhavsar et al. describe a study performed to evaluate CO2 sorption capacity and sensitivity of PILs based on diallyldimethylammonium (DADMA) cation with varying anions, the study being performed at elevated pressure (i.e., 20 Atm of CO2).
  • DADMA diallyldimethylammonium
  • tetra-alkyl ammonium based PILs are widely used in flocculation, dewatering, coagulation, retention, flotation and similar separation processes.
  • Bhavsar et al. do not mention the use of tetra-alkyl ammonium based PILs for the fabrication of CO2 sensors. While comparing solubility coefficient and solubility selectivity values provided by Bhavsar et al.
  • DADMA-based PILs and poly(vinylbenzyl)trimethylammonium-based PILs having the same counter anion, it can be seen that only one of the DADMA-based PILs had better solubility coefficient and solubility selectivity than the respective poly(vinylbenzyl)trimethylammonium-based PILs (i.e., acetate-containing P[DADMA]), while other DADMA-based PILs were inferior to poly(vinylbenzyl)trimethylammonium-based PILs.
  • DADMA-based PILs i.e., acetate-containing P[DADMA]
  • the sensors comprising DADMA-based PILs showed better performance than the poly[(p-vinylbenzyl)trimethylammonium hexafluorophosphate]-based sensor, the P[DADMA]-based sensors being highly sensitive towards a wide range of CO2 concentrations.
  • the anion of the PIL can be varied.
  • the anion is an inorganic anion.
  • the inorganic anion is selected from nitrate (NO3) and chloride (CI) ions.
  • the anion is an organic anion.
  • the organic anion can be a sulfonate anion, such as, but not limited to methanesulfonate (MS) or trifluoromethanesulfonate (TFMS).
  • the organic anion can be a carboxylate anion, such as, but not limited to, acetate (Ac), trifluoroacetate (TFAc), and benzoate (Bz).
  • the organic anion is selected from acetate (Ac), trifluoroacetate (TFAc), methanesulfonate (MS), trifluoromethanesulfonate (TFMS), and benzoate (Bz).
  • acetate Ac
  • TFAc trifluoroacetate
  • MS methanesulfonate
  • TFMS trifluoromethanesulfonate
  • Bz benzoate
  • the anion of the PIL is benzoate.
  • the molecular weight of the monomer diallyldimethylammonium chloride from the P[DADMA][C1] poly(ionic liquid) can be in the range of about 150 - 250 g/mol, and the polymer molecular weight can be selected from a wide range of molecular weights, such as, for example, from about 100,000 to about 500,000 g/mol.
  • the weight percent of the PIL in the sensing layer ranges between about 30% and about 99% of the total weight of the sensing layer. According to further embodiments, the weight percent of the PIL in the sensing layer ranges between about 30% and about 97% of the total weight of the sensing layer. In still further embodiments, the weight percent of the PIL in the sensing layer ranges between about 40% and about 60% of the total weight of the sensing layer or between about 45% and about 55%. In some exemplary embodiments, the weight percent of the PIL in the sensing layer is about 50% of the total weight of the sensing layer.
  • the weight percent of the PIL in the sensing layer ranges between about 50% and about 99% of the total weight of the sensing layer. In still further embodiments, the weight percent of the PIL in the sensing layer ranges between about 50% and about 97%), between about 60% and about 97%, between about 65% and about 95%, between about 70%) and about 90%, between about 70% and about 99% or between about 85% and about 95% of the total weight of the sensing layer.
  • the weight percent of the PIL in the sensing layer is about 90%) of the total weight of the sensing layer. In some other exemplary embodiments, the weight percent of the PIL in the sensing layer is about 70% of the total weight of the sensing layer.
  • the sensing layer includes an electrically conductive material.
  • the conductive material comprises SWCNTs or carbon black particles.
  • the conductive material comprises single walled carbon nanotubes (SWCNTs).
  • the sensing layer comprises the PIL and SWCNTs.
  • the sensing layer consists essentially of the PIL and SWCNTs.
  • single walled carbon nanotube refers to a cylindrically shaped thin sheet of carbon atoms having a wall which is essentially composed of a single layer of carbon atoms which are organized in a hexagonal crystalline structure with a graphitic type of bonding.
  • a nanotube is characterized by the length-to-diameter ratio. It is to be understood that the term “nanotubes” as used herein refers to structures in the nanometer as well as micrometer range.
  • the single-walled carbon nanotubes of the present invention have diameters ranging from about 0.6 nanometers (nm) to about 100 nm and lengths ranging from about 50 nm to about 10 millimeters (mm). More preferably, the single-walled carbon nanotubes have diameters ranging from about 0.7 nm to about 50 nm and lengths ranging from about 250 nm to about 1 mm. Even more preferably, the single-walled carbon nanotubes have diameters ranging from about 0.8 nm to about 10 nm and lengths ranging from about 0.5 micrometer ( ⁇ ) to about 100 ⁇ .
  • the single-walled carbon nanotubes have diameters ranging from about 1 nm to about 5 nm and lengths ranging from about 1 micrometer ( ⁇ ) to about 50 ⁇ . In certain embodiments, the single-walled carbon nanotubes of the present invention have diameters ranging from about 1 nm to about 2 nm and lengths ranging from about 1 ⁇ to about 6 ⁇ .
  • the SWCNTs have purity of at least about 80% wt. In further embodiments, the SWCNTs have purity of at least about 85% wt, at least about 90% wt, or at least about 95% wt. Each possibility represents a separate embodiment of the invention.
  • the SWCNTs have a semiconducting content of at least about 50%. In further embodiments, the SWCNTs have a semiconducting content of at least about 60%. In still further embodiments, the SWCNTs have a semiconducting content of at least about 70%.
  • the SWCNTs are wrapped by the PIL.
  • the weight ratio between the PIL and SWCNTs is about 1 : 1.
  • the PIL wraps the SWCNT in a substantially uniform manner.
  • the sensing layer does not include agglomerates of SWCNTs.
  • agglomerate of SWCNTs refers in some embodiments to an aggregation of a plurality of SWCNTs having a mean size of more than about 100 nm.
  • the term “agglomerate of SWCNTs” refers to an aggregation of a plurality of SWCNTs having a mean size of more than about 500 nm.
  • mean size as used in connection with the SWCNTs agglomerate, refers to the agglomerate size in the shortest dimension thereof (e.g., to a sum of the SWCNTs diameters).
  • the sensing mechanism of sensors comprising SWCNTs which are wrapped by PIL is based on the charge transfer from the SWCNTs through the electronegative anion of the polymer backbone located on the SWCNTs surface to the carbon atom of the C0 2 via a unique Lewis acid-base interaction.
  • the hole concentration in the p-type SWCNTs increases, improving the film's conductivity.
  • the SWCNTs surface and the anion of PIL have to be in close proximity in order to enable efficient charge transfer. Said close contact between the SWCNTs and the PIL can be afforded by the uniform wrapping of the SWCNTs by PIL.
  • the conductive material comprises carbon black particles.
  • the sensing layer comprises the PIL and carbon black particles.
  • the sensing layer consists essentially of the PIL and carbon black particles.
  • the carbon black particles are dispersed in the PIL.
  • the sensing mechanism of sensors comprising carbon black particles and PIL is based in part on the absorption of C0 2 in a conductive composite layer comprising conductive carbon black particles dispersed in the PIL.
  • the carbon black particles provide conductivity to the film while the PIL provides absorption sites for the CO2 gas and offers chemical diversity to the sensors.
  • the applied current passes between a pair of electrodes through continuous pathways of the conductive carbon black, the sorption of the gas into the organic phase induces swelling of the insulating polymeric phase. Swelling of the film breaks some of continuous conductive pathways, thus increasing the resistance of the composite film and reduces the overall conductivity.
  • Non-limiting examples of the carbon black material suitable for use in the sensors of the invention include acetylene black, channel black, furnace black, lamp black and thermal black. Each possibility represents a separate embodiment of the invention.
  • the mean particle size of the carbon black particles is less than about 1000 nm. According to some embodiments, the mean particle size of the carbon black particles is smaller than about 500 nm. According to some embodiments, the carbon black particles have a mean surface area ranging from about 50 to about 500 m 2 /g. According to some embodiments, the carbon black particles have a mean surface area ranging from about 50 to about 200 m 2 /g. In further embodiments, the carbon black particles have a mean surface area ranging from about 50 to about 100 m 2 /g.
  • particle size refers to the length of the particle in the longest dimension thereof.
  • the dispersion of the carbon black particles inside the PIL is substantially uniform.
  • the term "uniform distribution”, as used herein denotes that the volume percentage of the carbon black particles varies from one portion of the PIL to another by less than about 40%, less than about 20% or less than about 10%. Each possibility represents a separate embodiment of the invention.
  • the sensing layer does not include carbon black agglomerates.
  • carbon black agglomerate refers in some embodiments to an aggregation of carbon black particles having a mean size of at least about 500 nm. In certain embodiments, the term “carbon black agglomerate” refers to the aggregates of carbon black particles having a mean size ranging between about 500 to about 1000 nm.
  • the weight percent of the conductive material in the sensing layer ranges between about 1% and about 70% of the total weight of the sensing layer. According to further embodiments, the weight percent of the conductive material in the sensing layer ranges between about 3% and about 70% of the total weight of the sensing layer. In still further embodiments, the weight percent of the conductive material in the sensing layer ranges between about 40% and about 60%> of the total weight of the sensing layer or between about 45% and about 55%. In some exemplary embodiments, the weight percent of the conductive material in the sensing layer is about 50% of the total weight of the sensing layer.
  • the weight percent of the conductive material in the sensing layer ranges between about 1% and about 50% of the total weight of the sensing layer. In still further embodiments, the weight percent of the conductive material in the sensing layer ranges between about 3%) and about 50%, between about 3% and about 40%, between about 5% and about 35%, between about 10% and about 30%, between about 1% and about 30% or between about 5% and about 15%) of the total weight of the sensing layer.
  • the weight percent of the conductive material in the sensing layer is about 10% of the total weight of the sensing layer. In some other exemplary embodiments, the weight percent of the conductive material in the sensing layer is about 30% of the total weight of the sensing layer.
  • the weight percent of the SWCNTs as the conductive material ranges between about 30% and about 70% of the total weight of the sensing layer. According to further embodiments, the weight percent of the SWCNTs as the conductive material ranges between about 40% and about 60% of the total weight of the sensing layer, or between about 45%) and about 55%. In some exemplary embodiments, the weight percent of the SWCNTs in the sensing layer is about 50% of the total weight of the sensing layer.
  • the weight percent of the carbon black particles as the conductive material ranges between about 1% and about 55% of the total weight of the sensing layer. In further embodiments, the weight percent of the carbon black particles in the sensing layer ranges between about 1% and about 50% of the total weight of the sensing layer or between about 5%) and 55%. In still further embodiments, the weight percent of the carbon black particles in the sensing layer ranges between about 3% and about 50%, between about 3% and about 40%, between about 5% and about 35%, between about 10% and about 30%, between about 1% and about 30%) or between about 5% and about 15% of the total weight of the sensing layer.
  • Each possibility represents a separate embodiment of the invention.
  • the weight percent of the carbon black particles in the sensing layer is about 10% of the total weight of the sensing layer. In some other exemplary embodiments, the weight percent of the carbon black particles in the sensing layer is about 30% of the total weight of the sensing layer.
  • the sensing layer comprises from about 0.0005 to about 100 ⁇ g of conductive material. According to further embodiments, the sensing layer comprises from about 0.001 to about 100 ⁇ g of conductive material, from about 0.001 to about 10 ⁇ g, or 0.001 to about 1 ⁇ g of conductive material. According to some embodiments, the sensing layer comprises from about 0.0005 to about 0.1 ⁇ g of conductive material, from about 0.001 to about 0.1 ⁇ g of conductive material, or from about 0.001 to about 0.05 ⁇ g of conductive material. According to certain embodiments, the sensing layer comprises from about 0.005 to about 0.01 ⁇ g of conductive material. In some embodiments, the area of the sensing layer ranges from about 0.1 mm 2 to about 50 mm 2 .
  • the sensing layer comprises from about 0.01 to about 50 ⁇ g of conductive material, from about 0.1 to about 20 ⁇ g, or from about 1 to about 10 ⁇ g of conductive material. According to further embodiments, the sensing layer comprises from about 0.01 to about 10 ⁇ g of conductive material or from about 0.1 to about 10 ⁇ g of conductive material. In some embodiments, the area of the sensing layer ranges from about 0.1 mm 2 to about 50 mm 2 .
  • the sensing layer comprises from about 0.001 to about 55 ⁇ g of SWCNTs as the conductive material. According to some additional embodiments, the sensing layer comprises from about 20 to about 30 ⁇ g of SWCNTs as the conductive material. According to some embodiments, the sensing layer comprises from about 0.0005 to about 0.1 ⁇ g of SWCNTs as the conductive material. According to some embodiments, the sensing layer comprises from about 0.001 to about 1 ⁇ g of SWCNTs as the conductive material, from about 0.001 to about 0.1 ⁇ g, or from about 0.001 to about 0.05 ⁇ g of SWCNTs. According to certain embodiments, the sensing layer comprises from about 0.005 to about 0.01 ⁇ g of SWCNTs as the conductive material. In some embodiments, the area of the sensing layer ranges from about 0.1 mm 2 to about 50 mm 2 .
  • the sensing layer comprises from about 0.001 to about 10 ⁇ of carbon black particles as the conductive material. According to further embodiments, the sensing layer comprises from about 0.01 to about 10 ⁇ g of carbon black particles as the conductive material. According to still further embodiments, the sensing layer comprises from about 0.1 to about 10 ⁇ g of carbon black particles as the conductive material. According to yet further embodiments, the sensing layer comprises from about 1 to about 10 ⁇ g of carbon black particles as the conductive material. According to some embodiments, the sensing layer comprises from about 1 to about 5 ⁇ g of carbon black particles as the conductive material. In some embodiments, the area of the sensing layer ranges from about 0.1 mm 2 to about 50 mm 2 .
  • the sensing layer comprises from about 0.00001 to about 1000 ⁇ g/mm 2 of conductive material. According to further embodiments, the sensing layer comprises from about 0.0001 to about 100 ⁇ g/mm 2 of conductive material. According to still further embodiments, the sensing layer comprises from about 0.0001 to about 10 ⁇ g/mm 2 of conductive material. According to yet further embodiments, the sensing layer comprises from about 0.0001 to about 1 ⁇ g/mm 2 of conductive material. According to still further embodiments, the sensing layer comprises from about 0.0001 to about 0.1 ⁇ g/mm 2 of conductive material. According to some embodiments, the sensing layer comprises from about 0.0001 to about 100 ⁇ g/mm 2 of conductive material. According to further embodiments, the sensing layer comprises from about 0.001 to about 100 ⁇ g/mm 2 of conductive material. According to still further embodiments, the sensing layer comprises from about 0.002 to about 100 ⁇ g/mm 2 of conductive material.
  • the sensing layer comprises from about 0.00001 to about 1000 ⁇ g/mm 2 of SWCNTs as the conductive material. According to further embodiments, the sensing layer comprises from about 0.0001 to about 100 ⁇ g/mm 2 of SWCNTs, from about 0.0001 to about 10 ⁇ g/mm 2 , from about 0.0001 to about 1 ⁇ g/mm 2 , or from about 0.0001 to about 0.1 ⁇ g/mm 2 of SWCNTs.
  • the sensing layer comprises from about 0.00001 to about 1000 ⁇ g/mm 2 of carbon black particles as the conductive material. According to further embodiments, the sensing layer comprises from about 0.0001 to about 100 ⁇ g/mm 2 of carbon black particles, from about 0.001 to about 100 ⁇ g/mm 2 , or from about 0.002 to about 100 ⁇ g/mm 2 of carbon black particles.
  • the composite C0 2 sensing layer comprises from about 0.0005 to about 100 ⁇ g PIL. According to further embodiments, the composite CO2 sensing layer comprises from about 0.001 to about 100 ⁇ g PIL. According to some embodiments, the composite CO2 sensing layer comprises from about 0.001 to about 0.1 ⁇ g PIL, from about 0.1 ⁇ g PIL to about 1 ⁇ g PIL, from about 1 ⁇ g PIL to about 10 ⁇ g PIL, or from about 10 ⁇ g PIL to about 100 ⁇ g PIL. Each possibility represents a separate embodiment of the invention. In some embodiments, the area of the sensing layer ranges from about 0.1 mm 2 to about 50 mm 2 .
  • the composite CO2 sensing layer comprises from about
  • the composite CO2 sensing layer comprises from about 20 to about 30 ⁇ g PIL in the SWCNTs based sensors.
  • the sensing layer comprises from about 0.0005 to about 0.1 ⁇ g PIL in the SWCNTs based sensors.
  • the sensing layer comprises from about 0.001 to about 1 ⁇ g PIL in the SWCNTs based sensors.
  • the sensing layer comprises from about 0.001 to about 0.1 ⁇ g PIL in the SWCNTs based sensors.
  • the sensing layer comprises from about 0.001 to about 0.05 ⁇ g PIL in the SWCNTs based sensors. According to certain embodiments, the sensing layer comprises from about 0.005 to about 0.01 ⁇ g PIL in the SWCNTs based sensors. In some embodiments, the area of the sensing layer ranges from about 0.1 mm 2 to about 50 mm 2 .
  • the composite CO2 sensing layer comprises from about 0.001 to about 100 ⁇ g PIL in the carbon black particles based sensors. According to further embodiments, the composite CO2 sensing layer comprises from about 0.001 to about 50 ⁇ g PIL in the carbon black particles-based sensors. According to some embodiments, the composite CO2 sensing layer comprises from about 0.001 to about 10 ⁇ g PIL in the carbon black particles based sensors. According to some embodiments, the composite CO2 sensing layer comprises from about 0.01 to about 50 ⁇ g PIL in the carbon black particles based sensors. In some exemplary embodiments, the composite CO2 sensing layer comprises from about 0.5 to about 50 ⁇ g PIL in the carbon black particles based sensors.
  • the composite CO2 sensing layer comprises from about 1 to about 3 ⁇ g PIL in the carbon black particles based sensors.
  • the area of the sensing layer ranges from about 0.1 mm 2 to about 50 mm 2 .
  • the composite CO2 sensing layer comprises from about 0.00001 to about 1000 ⁇ g/mm 2 of PIL. According to further embodiments, the composite CO2 sensing layer comprises from about 0.0001 to about 100 ⁇ g/mm 2 of PIL. According to some embodiments, the composite CO2 sensing layer comprises from about 0.00002 to about 1 ⁇ g PIL, from about 0.002 ⁇ g PIL to about 10 ⁇ g PIL, from about 0.02 ⁇ g PIL to about 100 ⁇ g PIL, or from about 0.2 ⁇ g PIL to about 1000 ⁇ g PIL. In some embodiments, the composite C0 2 sensing layer comprising a combination of the PIL and the conductive material is in a form of a film.
  • said film is disposed on the substrate of the sensor.
  • the term "film”, as used herein, corresponds to a configuration of well-arranged assembly of PIL and the conductive material. 2D or 3D films of combination of the PIL and the conductive material can be used.
  • said conductive material comprises SWCNTs. In some embodiments, said conductive material comprises carbon black particles.
  • the thickness of the composite CO2 sensing layer or film is in the range of about 100 nm to about 10 mm. According to further embodiments, the thickness of the composite CO2 sensing layer or film ranges from about 500 nm to about 1 mm, or from about 1 ⁇ to about 500 ⁇ .
  • the sensing layer has a resistance ranging from about 20 to 1,500,000 ohm ( ⁇ ). In further embodiments, the resistance of the sensing layer ranges from about 20 to about 1,000 ⁇ , from about 1,000 to about 10,000 ⁇ , from about 10,000 to about 100,000 ⁇ , or from about 100,000 to about 1,500,000 ⁇ . In certain embodiments, the sensing layer has a resistance ranging from about 100 to 100,000 ⁇ .
  • the sensing layer comprises a PIL comprising an organic anion and SWCNTs.
  • the organic anion is selected from acetate, trifluoroacetate, methanesulfonate, trifluoromethanesulfonate, and benzoate. In certain embodiments the organic anion is benzoate.
  • the sensing layer comprises a PIL comprising an organic anion and carbon black particles.
  • the organic anion is selected from trifluoroacetate and benzoate. In certain embodiments the organic anion is benzoate.
  • the sensing layer comprises diallyldimethylammonium trifluoroacetate and SWCNTs (as termed herein "SWCNTs/PIL 3 [TFAc]").
  • the weight percent of the SWCNTs in the sensing layer ranges between about 45% and about 55%.
  • the sensor is configured to detect from about 0.05% to about 20% of C0 2 .
  • the sensing layer comprises diallyldimethylammonium methanesulfonate and SWCNTs (also termed herein “SWCNTs/PIL 3 [MS]").
  • SWCNTs/PIL 3 [MS] diallyldimethylammonium methanesulfonate
  • the weight percent of the SWCNTs in the sensing layer ranges between about 45% and about 55%.
  • the sensor is configured to detect from about 0.05% to about 20% of C0 2 .
  • the sensing layer comprises diallyldimethylammonium benzoate and SWCNTs (also termed herein "SWCNTs/PIL 3 [Bz]").
  • SWCNTs/PIL 3 [Bz] also termed herein "SWCNTs/PIL 3 [Bz]”
  • the weight percent of the SWCNTs in the sensing layer ranges between about 45% and about 55%.
  • the sensor is configured to detect from about 1.5% to about 20% of CO2.
  • the sensing layer comprises diallyldimethylammonium nitrate and SWCNTs (also termed herein "SWCNTs/PIL 3 [N0 3 ]").
  • SWCNTs/PIL 3 [N0 3 ] diallyldimethylammonium nitrate and SWCNTs
  • the weight percent of the SWCNTs in the sensing layer ranges between about 45% and about 55%.
  • the sensor is configured to detect from about 1.5% to about 20% of CO2.
  • ppm ppm
  • % % concentration
  • the sensing layer comprises diallyldimethylammonium trifluoroacetate and carbon black particles (as termed herein "PIL 3 [TFAc]/ carbon black”).
  • PIL 3 [TFAc]/ carbon black the weight percent of the carbon black particles in the sensing layer is about 35%.
  • the sensing layer comprises diallyldimethylammonium benzoate and carbon black particles (also termed herein "PIL 3 [Bz]/ carbon black”).
  • the weight percent of the carbon black particles in the sensing layer ranges between about 5% and 55%. In certain embodiments, the weight percent of the carbon black particles in the sensing layer is about 10%, about 30%, and/or about 50%. Each possibility represents a separate embodiment of the invention. In some currently preferred embodiments, the weight percent of the carbon black particles in the sensing layer is about 10%. In certain such embodiments, the sensor is configured to detect from about 2.75% to about 20% of CO2.
  • the sensing layer comprises diallyldimethylammonium nitrate and carbon black particles (as termed herein "PIL 3 [NO3]/ carbon black”).
  • PIL 3 [NO3]/ carbon black carbon black particles
  • the weight percent of the carbon black particles in the sensing layer is about 10%.
  • the sensor is configured to detect from about 0.05% to about 20% of CO2.
  • the sensors comprise diallyldimethylammonium cation and at least one anion selected from MS, TFMS, TFAc, Bz, NO3 and CI.
  • the TFAc- and MS-based sensors are configured to discriminate between low concentrations of CO2.
  • Bz-, MS-, TFAc-, TFMS-, NO3- and Cl-based sensors are configured to discriminate between high concentrations of CO2.
  • the sensing layer is typically deposited on top of a substrate. Suitable substrates within the scope of the present invention include rigid substances or flexible substrates. Exemplary substrates include, but are not limited to, metals, insulators, semiconductors, semimetals, polymers, and combinations thereof.
  • the substrate is a polymer which may be polyimide (e.g. Kapton), polyamide, polyimine (e.g. polyethyleneimine), polyester (e.g. polyethylene terephthalate, polyethylene naphthalate), polydimethylsiloxane, polyvinyl chloride (PVC), polystyrene and the like.
  • the substrate is rigid.
  • the substrate comprises silicon dioxide (for example glass or a silicon wafer coated with S1O2).
  • the substrate comprises indium tin oxide.
  • the area of the substrate ranges from about 0.1 mm 2 to about 50 mm 2 . In further embodiments, the area of substrate ranges from about 1 mm 2 to about 10 mm 2 .
  • the senor comprises at least two electrodes.
  • the electrodes are disposed on the substrate and are in electric contact with the sensing layer.
  • the sensing layer is disposed on the at least two electrodes.
  • the sensing layer is disposed on the substrate between the at least two electrodes.
  • the at least two electrodes are disposed on top of the sensing layer.
  • the senor of the present invention comprises a pair of electrodes (a positive electrode and a negative electrode) or a plurality of pairs of electrodes.
  • the amount of pairs of electrodes range between about 10 to about 100 pairs of electrodes.
  • the amount of pairs of electrodes range between about 20 to about 50 pairs of electrodes.
  • the amount of pairs of electrodes range between about 20 to about 30 pairs of electrodes.
  • the distance between adjacent electrodes ranges between about 0.5 ⁇ to about 3 mm.
  • the distance between adjacent electrodes ranges between about 5 ⁇ to about 35 ⁇ .
  • the width of each electrode ranges between about 0.05 ⁇ to about 3 mm.
  • the width of each electrode ranges between about 1 ⁇ to about 50 ⁇ .
  • the electrodes can comprise patterned electrodes, for example, interdigitated electrodes.
  • the electrodes include a plurality of sets of interdigitated electrodes.
  • the interdigitated electrodes can have any shape known in the art, such as, but not limited to circular or rectangular shapes.
  • the spacing between adjacent electrodes of the interdigitated electrodes ranges between about 0.5 ⁇ to about 3 mm.
  • the distance between adjacent electrodes ranges between about 1 ⁇ to about 50 ⁇ .
  • the electrodes may include a source and a drain electrode separated from one another by a source-drain gap.
  • the electrode array may further comprise a gate electrode wherein the electric signal may be indicative of a certain property of the sensing layer under the influence of a gate voltage.
  • the area of the at least two electrodes ranges from about 0.1 mm 2 to about 50 mm 2 . In further embodiments, the area of the at least two electrodes ranges from about 1 mm 2 to about 10 mm 2 .
  • FIG. 1 A schematically illustrates sensor 10 according to some embodiments of the invention.
  • Sensor 10 includes substrate 12, two electrodes 14, and sensing layer 16.
  • Sensing layer is disposed on substrate 12 between two electrodes 14.
  • the term "area of the at least two electrodes" refers to the rectangular area formed between electrodes 14.
  • Sensor 20 includes substrate 22, plurality of pairs of electrodes 24, and sensing layer 26. Electrodes pairs 24 are disposed on the substrate and sensing layer 26 is disposed on top of the electrodes.
  • the electrodes are circular interdigitated electrodes. In certain such embodiments, the term "area of the at least two electrodes" refers to the area formed within the perimeter of the pair of electrodes having the greatest diameter.
  • Sensor 30 includes substrate 32, plurality of pairs of electrodes 34, and sensing layer 36.
  • Sensing layer 36 is disposed on the substrate, wherein electrode pairs 34 are disposed on top of sensing layer 36.
  • the electrodes are circular interdigitated electrodes.
  • the term "area of the at least two electrodes" refers to the area formed within the perimeter of the pair of electrodes having the greatest diameter.
  • the electrodes can comprise any metal having high conductivity.
  • the non-limiting examples of metals suitable for use for the electrodes of the present invention include Au, Ti, Cu, Ag, Pd, Pt, Ni, Al.
  • the metal is selected from Au, Ti, Pt, Pd and combinations thereof.
  • the electrode array comprises Pd and/or Ti.
  • the area of the sensing layer is essentially similar to the area of the substrate and/or of the at least two electrodes.
  • the sensing layer generates an electric signal upon adsorption and/or absorbance of C0 2 molecules.
  • the electrodes, which are coupled to the sensing layer enable the measurement and transmittance of the electric signals generated by the sensing layer.
  • the senor is configured in a form selected from the group consisting of a resistive sensor, a chemiresi stive sensor, a capacitive sensor, an impedance sensor, and a field effect transistor sensor.
  • the sensing layer signal may be induced by a change in any one or more of conductivity, resistance, impedance, capacitance, and inductance of the sensing layer upon exposure to CO2.
  • Said signal can be transmitted by the electrodes, being in electric contact with the sensing layer.
  • the signal includes changes in resistance or conductance of the sensing layer upon CO2 adsorption and/or absorption.
  • the electrodes can be further used to apply a constant current or potential to the sensing layer.
  • the measured signal is a change in potential or current, respectively.
  • the signals of the sensing layer can be detected and/or measured by a suitable detection device.
  • the sensing system and/or the sensor are coupled to the signal detection and/or measuring device.
  • Suitable detection and/or measuring devices should be susceptible to a change in any one or more of resistance, conductance, alternating current (AC), direct current (DC), capacitance, impedance, inductance, electrical potential, and voltage threshold. Each possibility represents a separate embodiment of the present invention.
  • the measuring devices are susceptible to swelling or aggregation of conducting polymer and/or conductive nanostructures. Each possibility represents a separate embodiment of the present invention.
  • Changes in the electric properties of the sensing layer can be measured by any suitable device known in the art, including, inter alia, a sourcemeter, a data logger, a potentiostat, a voltmeter, a conductivity meter, an LCR meter or a millimeter.
  • the sensor according to various embodiments of the inventions enables direct sensing of CO2 from gaseous atmospheres, such as, but not limited to air or breath samples.
  • the sensor enables direct sensing of CO2 from a mixed environment.
  • the mixed environment can include gases or vapors selected from air, breathe sample, modified atmosphere packaging (MAP), fresh produce head space, and automobile emission.
  • the present invention provides a sensing system for the detection of CO2 comprising at least one sensor, as described hereinabove.
  • the sensing system further comprises a sample feeding system.
  • the sample feeding system comprises at least one sample feeding tube, which is in contact with the at least one sensor.
  • the sample feeding system comprises at least two sample feeding tubes, which are in contact with the at least one sensor.
  • at least one tube from the at least two sample feeding tubes comprises a CO2 adsorbent material.
  • said adsorbent material comprises a solid amine sorbent.
  • the solid amine sorbent comprises a porous support onto which an amine is attached or immobilized.
  • the non-limiting examples of said porous support materials include mesoporous silica and polymeric supports.
  • the polymeric supports can include, inter alia, poly(methyl methacrylate) (PMMA) and polystyrene (PS).
  • suitable amines include tetraethylenepentamine (TEPA), polyethyleneimine (PEI) and (3- aminopropyl)triethoxysilane (APTES).
  • TEPA tetraethylenepentamine
  • PEI polyethyleneimine
  • APTES (3- aminopropyl)triethoxysilane
  • said CO2 adsorbent material is removable and/or reusable.
  • at least one tube from the at least two sample feeding tubes does not include a CO2 adsorbent material.
  • the sample feeding system is configured to alternately prevent the exposure of the at least one sensor to a gas sample through each one of the sample feeding tubes.
  • the sample feeding system is a dual feeding system comprising one tube, which includes a CO2 adsorbent material and one tube which does not include a CO2 adsorbent material.
  • each tube includes at least one valve, which can allow and prevent the flow of the gas sample to the at least one sensor.
  • the dual feeding system can be used to calibrate the sensing system.
  • the dual feeding system can be further used to reduce or eliminate the effect of the confounding factors on the detection of CO2, for example, in complex environment comprising additional gases or vapors.
  • An output signal of a sensor being exposed to a sample through a tube comprising a C0 2 adsorbent material can be used as a blank reading.
  • Said blank reading can be subtracted from the output signal of a sensor being exposed to a sample through a tube which does not comprise a CO2 adsorbent material (also termed herein "test signal").
  • test signal also termed herein "test signal”
  • said blank reading can be eliminated from the test signal in any other way, as known in the art.
  • the blank reading can further be used for a discriminant analysis.
  • the sensing system comprises a signal detection and/or measuring device, as described hereinabove.
  • Said device can be selected from a sourcemeter, a data logger, a potentiostat, a voltmeter, a conductivity meter, an LCR meter or a multimeter.
  • the sensing system further comprises a computing system configured for executing at least one algorithm stored on a non-transitory memory.
  • the at least one algorithm provides a discriminant analysis of test data.
  • said at least one algorithm is selected from the group consisting of: principal component analysis (PCA), artificial neural network (ANN) algorithm, support vector machine (SVM), discriminant function analysis (DFA), multi-layer perceptron (MLP), generalized regression neural network (GRNN), fuzzy inference system (FIS), self-organizing map (SOM), radial basis function (RBF), genetic algorithm (GA), neuro-fuzzy system (NFS), adaptive resonance theory (ART), partial least squares (PLS), multiple linear regression (MLR), principal component regression (PCR), linear discriminant analysis (LDA), cluster analysis, Fisher linear discriminant analysis (FLDA), soft independent modeling of class analogies (SEVICA), K-nearest neighbors (KNN), fuzzy logic algorithms and canonical discriminant analysis (CD A).
  • PCA principal component analysis
  • ANN artificial neural network
  • SVM support
  • the at least one algorithm is principal component analysis (PCA).
  • PCA is an unsupervised learning algorithm used to reduce data dimensionality obtained from a sensor array and allow better visualization of multidimensional data sets.
  • This algorithm computes variables called principle components (PCs), which are linear combinations of the original data and are on mutually orthogonal dimensions. These variables are computed so that the maximum response variance is contained in these new dimensions, when the first PC (PCI) contains the largest possible variance.
  • said computing system receives output signals from the at least one sensor.
  • the computing system analyzes the output signals by said at least one algorithm. Said analysis can be used to reduce or eliminate the effect of the confounding factors on the detection of CO2, for example, in complex environment comprising additional gases or vapors.
  • said analysis is used to discriminate between the response of the at least one sensor to C0 2 and the response thereof to other gases or vapors, present in the sample.
  • said analysis is performed by PCA.
  • the computing system compares the signal obtained from exposure of the at least one sensor to the gas sample through the at least one tube, which does not include a CO2 adsorbent to the signal obtained from exposure of the at least one sensor to the gas sample through the at least one tube, which comprises a CO2 adsorbent. Said comparison can be performed by using an algorithm as listed hereinabove. In certain embodiments, said algorithm is PCA.
  • Sample feeding system 100 comprises two sample feeding tubes comprising tube 130 and tube 131, which are connected to CO2 sensor 140.
  • Tube 130 includes CO2 adsorbent material 120, valves 110 and 112, and is connected to CO2 sensor 140.
  • Tube 131 comprises valve 111 and valve 113, and is connected to CO2 sensor 140.
  • Valves 110, 111, 112, and 113 are configured to regulate the pressure and/or the gas flow through system 100.
  • Sensor 140 can transmit output signals to computing system 150, wherein computing system 150 analyzes said output signals by at least one algorithm, as presented hereinabove.
  • System 100 is configured to alternately prevent the exposure of sensor 140 to a gas sample by closing at least one of the valves connected to each one of the sample feeding tubes.
  • tube 130 comprising CO2 adsorbent material 120 provides gas sample to sensor 140, thus exposing the sensor to the gas only through tube 130.
  • CO2 adsorbent material 120 adsorbs essentially all carbon dioxide being present in the gas sample and sensor 140 generates an output signal in response to the other gases being present in the sample.
  • sensor 140 By exposing the sensor to the gas sample solely through tube 130, sensor 140 provides an output signal which can be used as a blank reading for the gas sample.
  • tube 131 provides gas sample to sensor 140, thus exposing the sensor to the gas only through tube 131, which does not include a CO2 adsorbent material.
  • sensor 140 provides an output signal which can be used as the actual reading of the gas sample (test signal).
  • Computing system 150 receives the blank output signal and the test output signal from sensor 140 and compares said signals, for example by utilizing the at least one algorithm. Computing system 150 can thus provide an accurate CO2 reading from the gas sample.
  • the computing system obtains the signal from exposure of the at least one sensor to the gas sample, without using the dual tube feeding system.
  • the sensing system has a response and recovery time between measurements of up to 10 minutes.
  • the sensing system is configured to detect C0 2 concentrations from about 0.05 % to at about 20% CO2 in a gaseous atmosphere.
  • the sensing system is configured to detect CO2 concentrations in a gaseous atmosphere from about 0.05 %> to about 0.6%>, from about 0.6 %> to about 1.5%, from about 1.5 %> to about 2.75%>, or from about 2.75%> to about 20%> of CO2.
  • the sensing system is configured to detect CO2 concentrations from about 0.05 %> to at about 1.5% CO2 in a gaseous atmosphere.
  • the sensing system is configured to detect CO2 concentrations from about 0.5 %> to at about 20%> CO2 in a gaseous atmosphere. In additional embodiments, the sensing system is configured to detect CO2 concentrations from about 0.5 %> to at about 15%> CO2 in a gaseous atmosphere. In certain embodiments, the sensing system is configured to detect CO2 concentrations from about 1.5 %> to at about 20%> CO2 in a gaseous atmosphere.
  • the sensing system of the present invention is portable. According to some embodiments, the sensing system of the present invention is capable of field operation and/or indoor operation. According to some embodiments, the sensing system is reusable, since the non-destructive detection of CO2 does not damage the system.
  • the sensing system is configured to work at ambient atmosphere and/or at room temperature. According to additional embodiments, the sensing system is configured to work at pressures ranging from 0.01 to 10 atm. According to certain embodiments, the sensing system is configured to work at temperatures ranging from 0 to 90 °C.
  • the sensing system comprises an array of sensors. In certain embodiments, the sensing system comprises at least two sensors. In additional embodiments, the sensing system comprises at least three sensors, at least four sensors or at least five sensors. Each possibility represents a separate embodiment of the invention. In some embodiments the sensing system comprises SWCNTs-based sensors. In some embodiments the sensing system comprises carbon black-based sensors.
  • the sensing system comprise a sensor array comprising diallyldimethylammonium trifluoroacetate and SWCNTs-based sensor, diallyldimethylammonium methanesulfonate and SWCNTs-based sensor, diallyldimethylammonium benzoate and SWCNTs-based sensor, and diallyldimethylammonium nitrate and SWCNTs-based sensor.
  • the sensing system comprise a sensor array comprising diallyldimethylammonium trifluoroacetate and carbon black particles- based sensor and diallyldimethylammonium benzoate and carbon black particles-based sensor, and diallyldimethylammonium nitrate-based sensor.
  • the sensing system comprises a combination of SWCNTs-based sensors and carbon black-based sensors.
  • the sensing system comprise a sensor array comprising diallyldimethylammonium trifluoroacetate and SWCNTs- based sensor, diallyldimethylammonium methanesulfonate and SWCNTs-based sensor, diallyldimethylammonium benzoate and SWCNTs-based sensor, diallyldimethylammonium nitrate and SWCNTs-based sensor, diallyldimethylammonium trifluoroacetate and carbon black particles-based sensor, and diallyldimethylammonium benzoate and carbon black particles-based sensor.
  • said method comprises: providing a substrate; forming at least two electrodes; preparing a composite CO2 sensing layer composition comprising a CO2 absorbing poly(ionic liquid) (PIL) comprising diallyldimethylammonium (DADMA) cation and an anion; and a conductive material comprising at least one of single walled carbon nanotubes (SWCNTs), and carbon black particles; and applying said composite CO2 sensing layer composition onto the substrate and/or the at least two electrodes, thereby forming a composite CO2 sensing layer which is in electric contact with the at least two electrodes.
  • PIL poly(ionic liquid)
  • DADMA diallyldimethylammonium
  • the sensing layer comprises SWCNTs.
  • the step of preparing a composite CO2 sensing layer composition comprises dispersing SWCNTs in an organic solvent to form a mixture; adding the PIL to the mixture; grinding the mixture for at least about 20 minutes, to form a paste; and washing the paste with the appropriate solvent to form a substantially stable dispersion comprising from about 5 to about 150 ppm SWCNTs and PIL.
  • the dispersion comprises from about 5 to about 100 ppm SWCNTs and PIL.
  • the dispersion comprises from about 5 to about 50 ppm SWCNTs and PIL.
  • the dispersion comprises from about 10 to about 25 ppm SWCNTs and PIL
  • substantially stable refers in some embodiments, to a dispersion which is able to resist phase separation and maintain equilibrium for at least about 24 hours.
  • the organic solvent is selected from dimethylformamide (DMF), or Methanol.
  • DMF dimethylformamide
  • Methanol Methanol
  • the non-limiting example of a suitable washing solvent is water.
  • the grinding is performed for at least about 30 minutes.
  • the sensing layer comprises carbon black particles.
  • the step of preparing a composite C0 2 sensing layer composition comprises dispersing carbon black particles in a solvent to form a mixture; adding the PIL to the mixture to form a dispersion; and sonicating the dispersion for at least about 0.5 hours.
  • said solvent is polar.
  • said solvent is non-polar.
  • the solvent comprises a mixture of polar and non-polar solvents.
  • suitable solvents include water, DMF, methanol, or any combination thereof.
  • the method comprises sonicating the dispersion for at least about 1 hour. In further embodiments, the method comprises sonicating the dispersion for at least about 2 hours.
  • the weight percent of the carbon black particles in the dispersion is between about 1% to about 50% of the total weight of the solid matter of the dispersion. In further embodiments, the weight percent of the carbon black particles in the dispersion is between about 5%) to about 40%) of the total weight of the solid matter of the dispersion. In still further embodiments, the weight percent of the carbon black particles in the dispersion is between about 10%) to about 30%> of the total weight of the solid matter of the dispersion. In some embodiments, the weight percent of the PIL in the dispersion is between about 50% to about 99% of the total weight of the solid matter of the dispersion.
  • the weight percent of the PIL in the dispersion is between about 40% to about 95% of the total weight of the solid matter of the dispersion. In still further embodiments, the weight percent of the PIL in the dispersion is between about 70%) to about 90% of the total weight of the solid matter of the dispersion. In some embodiments, the solid matter comprises carbon black particles and PIL.
  • the weight percent of the carbon black particles in the dispersion is between about 0.4% to about 10% of the total weight of the dispersion. In some embodiments, the weight percent of the PIL in the dispersion is between about 0.5% to about 6% of the total weight of the dispersion.
  • the PIL comprising diallyldimethylammonium (DADMA) cation and an anion can be synthesized as known in the art.
  • the anions can be selected from commercially available silver (Ag) salts, such as acetate (Ac), trifluoroacetate (TFAc), methanesulfonate (MS), trifluoromethanesulfonate (TFMS), benzoate (Bz) and nitrate (NO3), for an anion exchange reaction.
  • the synthesis method comprises: adding said Ag salt of the desired anion to a solution of poly(diallyldimethylammonium chloride) (P[DADMA][C1]) with a polymer concentration of about 1% to about 20% wt; stirring at ambient temperature for at least 24 hours while the reaction progresses and AgCl precipitates out from the mixture; and centrifuging the mixture for at least about 1000 rpm, for at least 30 minutes, to separate AgCl precipitate from the mixture.
  • the method comprises centrifuging the mixture for at least about 1600 rpm.
  • the method further comprises pouring the mixture on to a Teflon surface and drying at least at about 60°C for at least about 24 hours; drying in a vacuum oven at about at least 60°C for at least about 7 days; thereby obtaining brittle films with different morphologies, depending on the chosen anion.
  • the step of applying the composite CO2 sensing layer composition onto the substrate and/or the at least two electrodes comprises drop casting said composition.
  • the composition is applied by automated printing. In some embodiments from about 0.1 to about 5 ⁇ _, of the composite CO2 sensing layer composition is applied onto the substrate and/or the at least two electrodes. In further embodiments, from about 0.5 to about 2.5 ⁇ _, of the composite CO2 sensing layer composition is applied onto the substrate and/or the at least two electrodes. In some embodiments, the composite CO2 sensing layer composition is applied onto the substrate. In some embodiments, the composite CO2 sensing layer composition is applied onto the at least two electrodes.
  • the surface area to which the composite CO2 sensing layer composition is applied ranges from about 0.1 mm 2 to about 50 mm 2 . In further embodiments, the surface area to which the composite CO2 sensing layer composition is applied ranges from about 1 mm 2 to about 10 mm 2 .
  • the composite CO2 sensing layer composition is applied onto the substrate and/or the at least two electrodes within less than about 30 minutes following preparation of said composition.
  • the composition comprises carbon black particles.
  • the step of forming at least two electrodes comprises depositing a metal on the substrate or on the sensing layer by a method selected from the group consisting of e- beam evaporation, physical vapor deposition, sputter-deposition, drop-casting, field enhanced deposition, soft lithography, inkjet printing, screen printing and combinations thereof.
  • the metal can be selected from the group consisting of Au, Ti, Cu, Ag, Pd, Pt, Ni, Al, and combinations thereof. Each possibility represents a separate embodiment of the invention.
  • the step of forming the at least two electrodes comprises applying a shadow mask during the metal deposition.
  • the at least two electrodes are formed on the substrate.
  • the at least two electrodes are formed on the sensing layer.
  • the method further comprises a drying step, wherein the sensor is dried at about 20 to about 30°C and/or at ambient temperature, for at least about 15 minutes.
  • the drying step is conducted in a vacuum oven.
  • the drying step is conducted in a circulating oven.
  • the present invention provides a method for determining the concentration of C0 2 in a gas sample, comprising the steps of: providing a sensing system for the detection of CO2, comprising (a) at least one sensor comprising a composite CO2 sensing layer comprising: CO2 absorbing poly(ionic liquid) (PIL) comprising diallyldimethylammonium (DADMA) cation and an anion; and a conductive material comprising at least one of single walled carbon nanotubes (SWCNTs), and conductive carbon black particles; at least two electrodes; and a substrate, and (b) a sample feeding system comprising a first sample feeding tube comprising a CO2 adsorbent material and a second sample feeding tube, which does not include a CO2 adsorbent material, wherein said sample feeding tubes are in contact with the at least one sensor.
  • a sensing system for the detection of CO2 comprising (a) at least one sensor comprising a composite CO2 sensing layer comprising: CO2 absorbing poly(i
  • said method further comprises the steps of exposing the at least one sensor to the gas sample through the first sample feeding tube, thereby obtaining a confounding signal; exposing the at least one sensor to the gas sample through the second sample feeding tube, thereby obtaining a test signal; and comparing the test signal with the confounding signal.
  • said comparing comprises subtracting the confounding signal from the test signal.
  • the sensing system further comprises a computing system configured for executing at least one algorithm stored on a non-transitory memory, wherein said computing system receives the confounding signal and the test signal of the at least one sensor.
  • the step of comparing the test signal with the confounding signal is performed by the computing system using said at least one algorithm.
  • the at least one algorithm can be selected from the group consisting of: artificial neural network (ANN) algorithm, support vector machine (SVM), discriminant function analysis (DFA), principal component analysis (PCA), multi-layer perceptron (MLP), generalized regression neural network (GRNN), fuzzy inference system (FIS), self-organizing map (SOM), radial basis function (RBF), genetic algorithm (GA), neuro-fuzzy system (NFS), adaptive resonance theory (ART), partial least squares (PLS), multiple linear regression (MLR), principal component regression (PCR), linear discriminant analysis (LDA), cluster analysis, Fisher linear discriminant analysis (FLDA), soft independent modeling of class analogies (SEVICA), K-nearest neighbors ( N ), fuzzy logic algorithms, canonical discriminant analysis (CDA) and combinations thereof.
  • the algorithm is principal component analysis (PCA).
  • the output signal of the at least one sensor can be analyzed to extract a plurality of response induced parameters (also termed "response features" hereinbelow).
  • the output signal of the at least one sensor upon exposure to a test sample can change with time. Typically, the signal increases or decreases upon the exposure until reaching steady state and decreases and/or increases, respectively, upon terminating the exposure of the sensor to the test sample.
  • the output signal of the at least one sensor comprises a plurality of response induced parameters.
  • the computing system configured for executing at least one algorithm stored on a non-transitory memory extracts a plurality of response-induced parameters from the output signal of the at least one sensor.
  • the processing unit extracts a plurality of response-induced parameters from the output signal of the at least one sensor. In some embodiments, said plurality of response induced parameters extracted from the output signal of the at least one sensor, is analyzed by the at least one algorithm.
  • the plurality of response induced parameters can include at least two response induced parameters. According to other embodiment, the plurality of response induced parameters includes at least three response induced parameters, at least four response induced parameters, at least five response induced parameters, at least six response induced parameters, or at least seven response induced parameters. Each possibility represents a separate embodiment of the invention.
  • the response induced parameters can be selected from steady state normalized response, the time interval for obtaining steady state normalized response, and the time required to reach baseline after end of the exposure to the test sample.
  • the response induced parameters include full non steady state response at the beginning of the signal, full non steady state response at the beginning of the signal normalized to baseline, full non steady state response at the middle of the signal, full non steady state response at the middle of the signal normalized to baseline, full steady state response, full steady state response normalized to baseline, area under non steady state response, area under steady state response, the gradient of the response upon exposure to the test sample, the gradient of the response upon removal of the test sample, the time required to reach a certain percentage of the response, such as the time required to reach 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% or 100% of the response upon exposure to the test sample, and the time required to reach a certain percentage of the response, such as the time required to reach 100%, 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20% or 10% of the response
  • the response-induced parameters are selected from the group consisting of full non-steady state response at the beginning of the signal normalized to baseline, full steady state response normalized to baseline, and area under steady state response.
  • the response-induced parameters are selected from the group consisting of the normalized change of sensor's resistance at the full non steady state response at the beginning of the signal normalized to baseline, the full steady state response at the end of the signal normalized to baseline, and the area under steady state response.
  • at least two of said response induced parameters are extracted from the output signals of the at least one sensor.
  • at least three of said response-induced parameters are extracted from the output signals.
  • the plurality of response induced parameters are extracted from the output signal of the at least one sensor, including a change in resistance, impedance, capacitance, inductance, conductivity, and optical properties of the sensor upon exposure thereof to the breath sample.
  • the plurality of response induced parameters are extracted from a change in resistance or conductivity of the at least one sensor.
  • the method comprises exposing the at least one sensor to the gas sample through the second sample feeding tube for at least about five minutes. In certain embodiments, the method comprises flushing the at least one sensor to remove the adsorbed CO2. In certain embodiments, the adsorbed CO2 is removed by vacuum. According to additional embodiments, the method comprises repeating the step of exposing the at least one sensor to the gas sample through the second sample feeding tube.
  • PIL 3 [X] The synthesis procedure of PIL 3 [X] is illustrated hereinbelow in Formula II.
  • a 20% aqueous solution of poly(diallyldimethylammonium chloride) or P[DADMA][C1] was diluted to 8% in order to lower the solution viscosity and ease the anion replacement.
  • Commercially available silver salts of acetate (Ac), trifluoroacetate (TFAc), methanesulfonate (MS), trifluoromethanesulfonate (TFMS), benzoate (Bz) and nitrate (N0 3 ) were used for the anion exchange.
  • the different anions are illustrated herein below.
  • a mixture of SWCNT and PIL was deposited on the micro-device by drop casting a dispersion of SWCNT/PIL.
  • the dispersion was prepared by grinding the polymer in a concentrated SWCNTs dispersion, allowing the polymer to interact with, and effectively wrap, the SWCNTs. 1 mg SWCNTs was dissolved in 20 ⁇ DMF followed by the addition of 1 mg PILs. The mixture was ground for 30 min to obtain a viscous black gel. The gel was washed in an appropriate solvent for the PIL (see Table 1), producing a stable PIL/SWNTs dispersion.
  • 0.5 ⁇ 1 of the SWCNT/PIL dispersion was drop-casted onto pre-prepared 24 pairs of circular Ti/Pd interdigitated array microelectrodes (5 ⁇ width and electrode spacing of 25 ⁇ ) deposited on a Si wafer substrate with a ⁇ thermal oxide layer.
  • the devices were dried at ambient temperature for 15 minutes and then dried under vacuum.
  • Example 2 fabrication of carbon black PILs composite sensing systems
  • a composite PIL/carbon black (from Cabot corporation) film was made by drop casting 0.5 ⁇ 1 dispersion onto pre-prepared 24 pairs of circular Ti/Pd interdigitated array microelectrodes (5 ⁇ width and electrode spacing of 25 ⁇ ) deposited on a Si wafer substrate with a ⁇ ⁇ thermal oxide layer. 5 mg of carbon black, and varying amounts of PIL were mixed in 1 ml DI water in a vial. The amounts of PIL (with desired anion) were 90 or 70 or 50 w/w relative to the constant amount of carbon black, meaning 45, 1 1.6 or 5 mg of PIL, respectively. The dispersion was sonicated for 2 h at room temperature and was immediately drop casted on Pd electrodes of a desired pattern deposited on a desired substrate for the fabrication of the sensor films.
  • Example 3 Exposure of sensors to CO2 and electrical measurements for PIL/SWCNTs and PIL/carbon black
  • the fabricated sensors were mounted on a polytetrafluoroethylene circuit board inside a custom made stainless steel exposure chamber with a volume of 330 cm 3 , capable of accommodating up to 40 sensors.
  • An automated, computer controlled pneumatic valves system regulates the introduction and evacuation of diluted CO2 mixtures into the test chamber according to a custom Lab VIEW program.
  • Dry air flowing through a calcium sulfate desiccant filled column is used to dilute compressed CO2 flow (99.995% purity).
  • Gas flows are regulated by mass flow controllers (MFCs), while the flow rate range of each MFC as well as the number of dilution varies according to the desired CO2 concentration. This method achieves a constant relative humidity of 3 to 4%.
  • the final CO2 mixture flow rate is regulated after dilution to a fixed value of 1 lit/min regardless of the concentration.
  • the mixture then fills a 750 cm 3 bag which is fed to the test chamber.
  • Exposure cycles were used in the sensing experiment, each cycle consisting of two steps: (i) evacuation of the chamber and adsorbed volatile organic compounds (VOCs) under vacuum for 5 minutes, thus establishing a base line resistance; and (ii) exposure of the sensors to the CO2 mixture for 5 minutes.
  • PIL 2 poly[(p-vinylbenzyl)trimethylammonium hexafluorophosphate] wrapped SWCNTs-based sensor is identical to the fabrication procedure presented in Example 1. Briefly, a mixture of SWCNT and PIL 2 was prepared and deposited on the micro-device by drop casting a dispersion of SWCNT/PIL. The resistance peaks of said sensor were tested as described in example 3.
  • Figures 2A-2C depict the response of the PIL 2/SWCNTs sensor during exposure to increasing concentrations of CO2 measured as resistance peaks, as described in example 3.
  • the sensors resistance changed dramatically, increasing and decreasing with no clear trend upon changing the CO2 concentration.
  • PIL 2/SWCNTs sensor was not sensitive to various CO2 concentration and therefore, PIL 2 was unable to produce good sensor in combination with SWCNTs.
  • the PIL 3/SWCNTs-based sensors were found to be sensitive to various CO2 concentrations.
  • the resistance peaks of said sensors were tested as described in example 3.
  • the PIL 3 [Bz] sensor was able to discriminate between different high concentrations using each one of two different features: area under the curve (Figure 3A) and the relative resistance change at the beginning of the exposure step ( Figure 3C). These two features show a similar behavior and indicate a clear trend between the increasing CO2 concentrations and the increasing resistance, demonstrating that PIL 3 [Bz] is able to produce a reliable sensor for higher concentrations of CO2.
  • the negative response indicates reduced resistance upon exposure to CO2 compared to vacuum.
  • PIL 3 [TFAc] sensor was able to discriminate between all tested concentrations, demonstrating an increasing response to each CO2 concentration.
  • Relative resistance change features were able to discriminate between high concentrations (1.5-20%> CO2), showing an increasingly stronger negative response.
  • the resistance change at the beginning of the response shows stronger response and better discrimination compared to the resistance change at the end of the response ( Figures 4B), however the second feature is able to discriminate between lower concentrations as well (0.05-0.6% C0 2 ).
  • PIL 3 [Ac] sensor presented an unstable reaction to the increasing CO2 concentration, and no clear trend was observed upon changing the CO2 concentration.
  • the response of the PIL/SWCNTs sensors to CO2 may be attributed to a charge transfer from the SWCNTs through the electronegative anion of the polymer backbone located on the SWCNTs surface. This charge is transferred to the carbon atom of the CO2 via a unique Lewis acid-base interaction.
  • the hole concentration in the p-type SWCNTs increases, improving the film's conductivity. This mechanism predicts a negative response in the sensors upon exposure to CO2, meaning reduced electrical resistance as the CO2 concentration increases.
  • sensors based on PIL 3 [Bz] chemistry with different film compositions i.e. 90 and 70 w/w polymer relative to a constant amount of carbon black
  • Other PIL 3 [X]/carbon black sensors comprising anions other than Bz contained low carbon black mass fractions (i.e. 90% polymer). The experiment was conducted as described in Example 3 herein above.
  • the conductive carbon black particles were dispersed in the insulating PILs, resulting in a conductive film.
  • a composite film of PIL polymer and carbon black particles are exposed to the CO2 gas, sorption of the gas into the film induces swelling of the insulating polymeric phase. Swelling of the film breaks some of continuous conductive pathways, thus increasing the resistance of the composite film.
  • PIL3 [TFAc]/carbon black sensor was found suitable for high concentration of C0 2 using (2.75 - 17.5%), showing increased resistance upon exposure to increasing CO2 and a clear correlation was observed between the resistance and the CO2 concentrations.
  • PIL3 [Bz]/carbon black sensor with polymeric mass fraction of 90% was sensitive to CO2 concentrations greater than 2.75% as reflected by all three response features ( Figures 12A-12C), while the sensor with 70% polymeric mass fraction was sensitive to all of the tested concentrations ( Figures 13A-13C).
  • Example 7 Reproducibility and reversibility of the sensors
  • SWCNTs/PIL3 [TFAc], SWCNTs/PIL3 [MS], and SWCNTs/PIL3 [TFMS]-based sensors allowed detection of CO2, being sensitive to concentrations of 2.75% CO2 and higher, using each of the three response features, with small standard deviations ( Figures 15A-15C, 16A-16C, and 17A-17C, respectively).
  • PIL3 [Bz]/carbon black sensor comprising 70% wt. polymer allowed detection of CO2 and discrimination between different concentrations thereof. However it did not show a reversible response ( Figures 18A-18C) and it can be seen that the sensor did not regain its original response after exposure to 20% CO2. It is likely that this chemistry absorbs CO2 effectively and requires longer exposure to vacuum for full desorption.
  • PIL3 [TFAc]/carbon black sensor comprising 90% wt. polymer showed similar performance to its previous results, although the response was not completely reversible ( Figures 20A-20C).
  • PCA principal component analysis
  • Figures 22A-22C represent the response of the PIL2/carbon black sensor made of 90% polymer to the increasing and decreasing concentrations of CO2. Said sensor was unable to detect changes in the CO2 concentration in the confounding environment.

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Abstract

La présente invention concerne un capteur de dioxyde de carbone et un système de détection comprenant une couche de détection composite comprenant un polymère liquide ionique (PIL) comprenant un cation de diallyldiméthylammonium (DADMA) et un anion ; et un matériau électroconducteur choisi parmi des nanotubes de carbone à paroi simple et des particules de noir de carbone. L'invention concerne en outre un procédé de fabrication du capteur comprenant la couche de détection composite. L'invention concerne en outre un procédé de détermination de la concentration de dioxyde de carbone dans un échantillon de gaz à l'aide du système de détection comprenant le capteur comprenant la couche de détection composite comprenant le PIL à base de DADMA et le matériau conducteur.
PCT/IL2018/050564 2017-05-24 2018-05-24 Capteurs de dioxyde de carbone comprenant un polymère liquide ionique WO2018216017A1 (fr)

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