Resin composition for forming odorant receiving layer, sensor element, odor sensor, and odor measuring device using same

Abstract

To provide a resin composition for forming an odor substance receptive layer that offers improved odor recognition performance, and to provide a sensor element using the same, an odor sensor, and an odor measurement device.SOLUTION: One aspect of the present invention relates to: a resin composition containing (A) polyester, (B) a surfactant, and (C) a conductive carbon material; a sensor element containing the same; an odor sensor; and an odor measurement device.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a resin composition for forming an odorant receiving layer, and a sensor element, an odor sensor, and an odor measuring device each using the same. [Background technology]

[0002] With the recent development of information processing technology, if it were possible to somehow quantify the sense of smell, which is one of the five human senses that cannot be adequately measured mechanically, it is expected that this could be used in a wide range of industrial fields, including the medical field, the environment and safety field, and marketing field. Up until now, methods for detecting specific gaseous substances (gases) have been achieved with high accuracy and sensitivity using semiconductor gas sensors and the like.

[0003] The invention described in Patent Document 1 proposes a mechanism for detecting the adsorption of odor components on the surface of a conductive polymer by replacing the semiconductor of a semiconductor gas sensor with a conductive polymer. Patent Document 1 reports that it is possible to detect odor components that are easily thermally decomposed and substances that do not cause an oxidation-reduction reaction on the surface of the detection part of the sensor.

[0004] Patent Document 2 also focuses on the property that the electrical resistance of a mixture of an organic polymer and a conductive material changes when exposed to an organic gas. Patent Document 2 describes that when a plurality of combinations of organic polymer / conductive material in which the organic polymer composition is different from that of the mixture is prepared and these are used as an electrical resistance array in a sensor, the electrical resistance changes when exposed to the same organic gas are different for each combination. Patent Document 2 reports that this can be used to identify odors by attributing the pattern of electrical resistance changes to the type of odor (= organic gas mixture).

[0005] Furthermore, Patent Document 3 reports that the response speed of the sensor can be improved by adding a plasticizer to the above-mentioned organic polymer. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Japanese Patent Application Publication No. 11-23508 [Patent Document 2] Special Publication No. 11-503231 [Patent Document 3] Special Publication No. 2002-519633 Summary of the Invention [Problem to be solved by the invention]

[0007] However, the above-mentioned conventional techniques have room for improvement in terms of odor discrimination performance.

[0008] One aspect of the present invention aims to provide a resin composition for forming an odorant receiving layer with improved odor discrimination performance, and a sensor element, odor sensor, and odor measuring device using the same. [Means for solving the problem]

[0009] The present inventors conducted research aimed at achieving the above-mentioned object and arrived at the present invention.

[0010] That is, one aspect of the present invention is a resin composition for forming an odorant receiving layer, the resin composition comprising a polyester (A), a surfactant (B) and a conductive carbon material (C), and a sensor element, an odor sensor and an odor measuring device using the same. Effect of the Invention

[0011] According to one aspect of the present invention, it is possible to provide a resin composition for forming an odorant receiving layer with improved odor discrimination performance, and a sensor element, odor sensor, and odor measuring device using the same. [Brief description of the drawings]

[0012] [Figure 1]1 is a block diagram showing an example of the configuration of an odor measuring device according to one embodiment of the present invention. [Diagram 2] FIG. 2 is a top view showing an example of the configuration of a sensor element. [Diagram 3] 3 is a cross-sectional view showing an example of the configuration of the sensor element shown in FIG. 2. [Figure 4] 1 is a functional block diagram showing an example of the configuration of an odor measurement device. [Diagram 5] 11 is a flowchart showing an example of a process flow in which the estimation device generates an estimation model. [Figure 6] 1 is a functional block diagram showing an example of the configuration of an odor measurement device. [Figure 7] 11 is a flowchart showing an example of a process flow in which the estimation device estimates an odor substance. [Figure 8] FIG. 2 is a block diagram showing an example of the configuration of an odor measuring device according to another embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] An embodiment of the present invention will be described below, but the present invention is not limited thereto. In addition, unless otherwise specified in this specification, "A to B" representing a numerical range means "A or more and B or less."

[0014] [1. Resin composition] A resin composition according to one embodiment of the present invention is a resin composition for forming an odorant receiving layer, and contains a polyester (A), a surfactant (B) and a conductive carbon material (C).

[0015] In this specification, "odor substance" means a substance that can be adsorbed to an odor substance receiving layer in a broad sense. Therefore, it also includes substances that are not generally considered to be the cause of odor. "Odor" often contains multiple odor substances that cause it, and there are also substances that are not recognized as odor substances or unknown odor substances. One embodiment of the present invention focuses on the fact that the amount of odor substance adsorbed to the odor substance receiving layer differs depending on the type of odor substance.

[0016] In addition, even when the term "odor substance" is simply used in this specification, it may mean a "collection of odor substances" that may contain multiple odor substances, rather than an individual odor substance.

[0017] Examples of "odor substances" include, but are not limited to, hexane, ethyl acetate, methanol, diethyl carbonate, toluene, d-limonene, bornan-2-one, cis-3-hexenol, β-phenylethyl alcohol, citral, L-carvone, γ-undecalactone, eugenol, linalyl acetate, menthol, benzaldehyde, vanillin, hexanal, ethanol, pentyl valerate, linalool, and 2-propanol.

[0018] In addition, in this specification, the term "odorant receiving layer" refers to a layer that adsorbs the odorant to be recognized. The odorant receiving layer is formed from the above-mentioned resin composition. The odorant receiving layer can be provided as a part of the sensor element described below.

[0019] It is believed that the sensor described in the cited document 1 can detect odors consisting of single compounds. On the other hand, many odors are mixtures of multiple substances. The sensor described in the cited document 1 does not have a function to distinguish odor components in the detection unit, so the odor discrimination performance for mixtures is insufficient. In the cited document 2, it is shown that it is possible to recognize odors as mixtures by making the response of the detection unit to various compounds different through each conductive polymer by utilizing the difference in the chemical structure of the conductive polymer used in the detection unit. However, the chemical structure of the conductive polymer is limited, and it is difficult to separate the response of the detection unit to any odor component with high sensitivity, and it is difficult to distinguish between odors consisting of similar components. In the cited document 3, a method is proposed in which a mixture consisting of an organic polymer, a plasticizer, and a conductive substance is used as a detection material in the detection unit, and the penetration of odor components into the organic polymer is detected as a change in the electrical resistance of the mixture. By utilizing the fact that the odor components that penetrate are different when organic polymers of different compositions are used, it is possible to recognize odors as mixtures by using multiple detection units made of the above-mentioned detection material containing organic polymers of different compositions in parallel to form an array. However, in the case of the aforementioned organic polymers and organic polymers containing plasticizers, even if multiple combinations of organic polymers and conductive materials are prepared, the difference in chemical properties between the organic polymers is small, so the odor discrimination performance is insufficient. These conventional technologies cannot accurately detect, for example, real odor patterns in which multiple substances interact with each other or real odor patterns caused by substances with unknown compositions.

[0020] The present inventors have focused on the fact that the electrical conductivity of the resin composition varies depending on the amount of odorant adsorbed in the resin composition, and that the adsorption process to the resin composition differs for each odorant, and have invented a resin composition and a sensor element according to one embodiment of the present invention. By using such a resin composition, the odor discrimination performance can be improved. For example, it is possible to discriminate a real odor pattern in which multiple substances interact with each other, or a real odor pattern caused by a substance whose composition is unknown.

[0021] <Polyester (A)> The polyester (A) may, for example, be a polymer comprising a portion derived from a polycarboxylic acid and a portion derived from a polyhydric alcohol, that is, a polymer obtained by polymerizing a polycarboxylic acid and a polyhydric alcohol.

[0022] The polyester (A) may consist of one type of polyester, or may be a mixture of two or more types of polyester.

[0023] The polycarboxylic acid may be, for example, one or more polycarboxylic acids selected from the group consisting of dicarboxylic acids and trivalent or higher polycarboxylic acids.

[0024] Examples of the dicarboxylic acid include aromatic dicarboxylic acids having 8 to 12 carbon atoms (phthalic acid, isophthalic acid, terephthalic acid, 2,6-naphthalenedicarboxylic acid, etc.), alkane dicarboxylic acids having 4 to 12 carbon atoms (succinic acid, adipic acid, sebacic acid, 1,12-dodecanedioic acid, etc.), and alkene dicarboxylic acids having 4 to 12 carbon atoms (alkenyl succinic acid, maleic acid, fumaric acid, citraconic acid, mesaconic acid, etc.). Examples of the alkenyl succinic acid include dodecenyl succinic acid. The dicarboxylic acid may be one type of dicarboxylic acid or a mixture of two or more types of dicarboxylic acids.

[0025] When the polycarboxylic acid contains a dicarboxylic acid, from the viewpoint of repeated measurement stability, the dicarboxylic acid is preferably at least one selected from the group consisting of aromatic dicarboxylic acids having 8 to 12 carbon atoms, alkane dicarboxylic acids having 4 to 12 carbon atoms, and alkene dicarboxylic acids having 4 to 12 carbon atoms, and more preferably at least one selected from the group consisting of phthalic acid, isophthalic acid, terephthalic acid, succinic acid, adipic acid, maleic acid, and fumaric acid.

[0026] Examples of the trivalent or higher polycarboxylic acid include aromatic carboxylic acids having 9 to 12 carbon atoms (trimellitic acid, pyromellitic acid, etc.). The trivalent or higher polycarboxylic acid is preferably a trivalent polycarboxylic acid, and particularly preferably trimellitic acid.

[0027] The polyhydric alcohol may be, for example, one or more polyhydric alcohols selected from the group consisting of diols and trihydric or higher polyols.

[0028] Examples of the diol include alkylene glycols having 2 to 10 carbon atoms (ethylene glycol, propylene glycol, 1,3-propanediol, 1,4-butanediol, neopentyl glycol, 1,6-hexanediol, 1,9-nonanediol, 1,10-decanediol, etc.), alkylene ether glycols having 4 to 8 carbon atoms (diethylene glycol, triethylene glycol, tetraethylene glycol, dipropylene glycol, etc.), polyether diols having 12 to 44 carbon atoms (polyethylene glycol, polypropylene glycol, polytetramethylene ether glycol, etc.), alicyclic diols having 6 to 18 carbon atoms (1,4-cyclohexanedimethanol, hydrogenated bisphenol A, etc.), and polyoxyalkylene ethers of bisphenols having an added mole number of oxyalkylene (AO) groups of 2 to 30 (bisphenol A propylene oxide adduct, bisphenol A ethylene oxide adduct, etc.). Examples of the bisphenols include bisphenol A, bisphenol F, and bisphenol S. The diol may be one type of diol or a mixture of two or more types of diols.

[0029] When the polyhydric alcohol contains a diol, from the viewpoint of repeatability of the measurement, the diol is preferably at least one selected from the group consisting of ethylene glycol, 1,2-propylene glycol, 1,3-propylene glycol, 1,4-butanediol, 1,6-hexanediol, 1,10-decanediol, diethylene glycol, polyethylene glycol, cyclohexanedimethanol, and polyoxyalkylene ethers of bisphenols having an added mole number of 2 to 30 AO groups.

[0030] Examples of the trihydric or higher polyols include aliphatic polyhydric alcohols having 3 to 10 carbon atoms and having 3 to 6 hydric or more hydric ...

[0031] When the polyhydric alcohol contains a trihydric or higher polyol, from the viewpoint of repeated measurement stability, the trihydric or higher polyol is preferably one or more selected from tri- to hexahydric or higher aliphatic polyhydric alcohols having 3 to 10 carbon atoms, and more preferably one or more selected from the group consisting of glycerin, trimethylolethane, trimethylolpropane, and pentaerythritol.

[0032] The number average molecular weight of the polyester (A) is preferably from 2,000 to 20,000, more preferably from 3,000 to 15,000, and even more preferably from 4,000 to 10,000, from the viewpoint of repeatability of the measurement.

[0033] The method for measuring the number average molecular weight of the polyester (A) is not particularly limited, but for example, it is measured using gel permeation chromatography (GPC) under the following conditions. As a sample to be subjected to GPC, for example, a filtrate obtained by dissolving the polyester (A) in a THF (tetrahydrofuran) solution and then filtering the THF solution through a glass filter can be used.

[0034] Equipment (example): Tosoh Corporation HLC-8120 Column (example): 2 TSK GEL GMH6 (manufactured by Tosoh Corporation) Measurement temperature: 40℃ Sample solution: 0.25% by weight THF (tetrahydrofuran) solution Solution injection volume: 100μl Detector: Refractive index detector

[0035] In addition, a calibration curve for calculating the number average molecular weight can be prepared by using the least squares method based on the measured values ​​of 12 number average molecular weights obtained by using 12 types of standard polystyrene (TSKstandard POLYSTYRENE, manufactured by Tosoh Corporation) having different number average molecular weights (500, 1050, 2800, 5970, 9100, 18100, 37900, 96400, 190000, 355000, 1090000, or 2890000) as reference substances.

[0036] The polyester (A) can be obtained by, for example, a known production method. Specifically, a method of introducing polycarboxylic acid and polyhydric alcohol as constituent monomers and a polymerization catalyst into a reaction vessel equipped with a cooling tube, a stirrer and a nitrogen inlet tube, reacting the polycarboxylic acid and the polyhydric alcohol at a predetermined temperature while distilling off by-product water while passing a nitrogen stream, and then reducing the pressure inside the reaction vessel to 0.5 to 2.5 kPa and further reacting for 1 hour to obtain the polyester (A) can be mentioned.

[0037] In the above-mentioned method for producing polyester (A), polycarboxylic acid and polyhydric alcohol may be reacted using an organic solvent having a boiling point of 100° C. or higher as a reaction solvent. Examples of the organic solvent having a boiling point of 100° C. or higher include toluene, xylene, N-methylpyrrolidone, dimethylformamide, and 1,4-dioxane.

[0038] In the above-mentioned method for producing polyester (A), the reaction temperature may be equal to or higher than the boiling point of water (100°C), and is preferably 150°C to 250°C, more preferably 150°C to 220°C, from the viewpoint of promoting the reaction.

[0039] As the polymerization catalyst in the above-mentioned method for producing polyester (A), a catalyst that can be generally used as an esterification catalyst can be used. Examples of the polymerization catalyst include tin-containing catalysts (dibutyltin oxide, etc.), antimony trioxide, titanium-containing catalysts (titanium alkoxide, potassium oxalate titanate, titanium terephthalate, catalysts described in JP-A-2006-243715 and catalysts described in JP-A-2007-11307), zirconium-containing catalysts (zirconyl acetate, etc.) and zinc acetate. Specific examples of the catalysts described in JP-A-2006-243715 include titanium dihydroxybis(triethanolamine). Specific examples of the catalysts described in JP-A-2007-11307 include titanium tributoxyterephthalate, titanium triisopropoxyterephthalate, titanium diisopropoxyditerephthalate, etc. Of these, the polymerization catalyst is preferably a tin-containing catalyst or a titanium-containing catalyst from the viewpoint of reaction promotion effect.

[0040] <Surfactant (B)> The surfactant (B) is not particularly limited, but preferably has an HLB value of 8 to 18, more preferably 9 to 17, and particularly preferably 10 to 16. By using a surfactant (B) having such an HLB value, good odor discrimination performance can be obtained.

[0041] The "HLB value" here is an index showing the balance between hydrophilicity and lipophilicity, and is known as a value calculated by the Oda method described, for example, in "Introduction to Surfactants" [published by Sanyo Chemical Industries, Ltd. in 2007, written by Takehiko Fujimoto], page 212, and is not a value calculated by the Griffin method.

[0042] The HLB value can be calculated from the ratio of the organic value to the inorganic value of an organic compound.

[0043] HLB=10×Inorganic / Organic Here, the inorganic and organic values ​​in the above formula represent index values ​​expressing organic and inorganic properties proposed by Fujita et al., and can be calculated using the values ​​in the table on page 213 of the aforementioned "Introduction to Surfactants."

[0044] Examples of the surfactant (B) include anionic surfactants, cationic surfactants, amphoteric surfactants and nonionic surfactants.

[0045] Examples of the anionic surfactant include alkali metal salts of carboxylic acids having 10 to 24 carbon atoms and alkali metal salts of alkylsulfonic acids having 14 to 24 carbon atoms.

[0046] Examples of the carboxylic acid having 10 to 24 carbon atoms include decanoic acid, undecanoic acid, dodecanoic acid, tridecanoic acid, tetradecanoic acid, hexadecanoic acid, heptadecanoic acid, octadecanoic acid, pentadecanoic acid, nonadecanoic acid, icosanoic acid, henicosanoic acid, docosanoic acid, tricosanoic acid, and tetracosanoic acid.

[0047] Examples of the alkyl group contained in the alkylsulfonic acid having 14 to 24 carbon atoms include a tetradecyl group, a pentadecyl group, a hexadecyl group, a heptadecyl group, an octadecyl group, a nonadecyl group, an icosyl group, a henicosyl group, a docosyl group, a tricosyl group, and a tetracosyl group.

[0048] Examples of the alkali metal contained in the alkali metal salt include sodium and potassium.

[0049] Examples of the cationic surfactant include halide salts of quaternary ammonium having an alkyl group having 12 to 24 carbon atoms.

[0050] Examples of the quaternary ammonium having an alkyl group having 12 to 24 carbon atoms include tetrapropylammonium, tetrabutylammonium, tetrapentylammonium, tetrahexylammonium, dimethyldioctylammonium, didecyldimethylammonium, decyltrimethylammonium, dodecyltrimethylammonium, tridecyltrimethylammonium, hexadecyltrimethylammonium, methyltrioctylammonium, octyltrimethylammonium, tributylmethylammonium, octadecyltrimethylammonium, tetradecyltrimethylammonium, nonadecyltrimethylammonium, icosyltrimethylammonium, henicosyltrimethylammonium, heptadecyltrimethylammonium, and pentadecyltrimethylammonium.

[0051] The halide salts include, for example, fluoride salts, chloride salts, bromide salts, and iodide salts.

[0052] Examples of amphoteric surfactants include dimethyl(3-sulfopropyl)ammonium inner salts having an alkyl group with 10 to 22 carbon atoms, and N-alkyl-N,N-dimethylglycines having an alkyl group with 10 to 22 carbon atoms.

[0053] Examples of dimethyl(3-sulfopropyl)ammonium hydroxide inner salts having an alkyl group having 10 to 22 carbon atoms include decyldimethyl(3-sulfopropyl)ammonium hydroxide inner salt, undecyldimethyl(3-sulfopropyl)ammonium hydroxide inner salt, dodecyldimethyl(3-sulfopropyl)ammonium hydroxide inner salt, tridecyldimethyl(3-sulfopropyl)ammonium hydroxide inner salt, tetradecyldimethyl(3-sulfopropyl)ammonium hydroxide inner salt, pentadecyldimethyl(3-sulfopropyl)ammonium hydroxide inner salt, Examples of such ammonium hydroxide inner salt include hexadecyldimethyl(3-sulfopropyl)ammonium hydroxide inner salt, heptadecyldimethyl(3-sulfopropyl)ammonium hydroxide inner salt, octadecyldimethyl(3-sulfopropyl)ammonium hydroxide inner salt, nonadecyldimethyl(3-sulfopropyl)ammonium hydroxide inner salt, icosyldimethyl(3-sulfopropyl)ammonium hydroxide inner salt, henicosyldimethyl(3-sulfopropyl)ammonium hydroxide inner salt, and docosyldimethyl(3-sulfopropyl)ammonium hydroxide inner salt.

[0054] Examples of N-alkyl-N,N-dimethylglycines having an alkyl group having 10 to 22 carbon atoms include N-dodecyl-N,N-dimethylglycine and N-octadecyl-N,N-dimethylglycine.

[0055] Examples of nonionic surfactants include higher alcohol ethylene oxide adducts.

[0056] Examples of higher alcohols include 1-hexyl alcohol, 1-heptyl alcohol, 1-octyl alcohol, 1-nonyl alcohol, 1-decyl alcohol, 1-undecyl alcohol, 1-dodecyl alcohol, 1-tridecyl alcohol, 1-tetradecyl alcohol, 1-pentadecyl alcohol, 1-hexadecyl alcohol, 1-heptadecyl alcohol, and 1-octadecyl alcohol.

[0057] The number of moles of ethylene oxide added is preferably 5 to 50, more preferably 5 to 40, and even more preferably 5 to 30, from the viewpoint of odor discrimination performance.

[0058] From the viewpoint of odor discrimination performance, the surfactant (B) is preferably an anionic surfactant, a cationic surfactant or a nonionic surfactant, more preferably a nonionic surfactant or a cationic surfactant, and most preferably a cationic surfactant.

[0059] The weight ratio of the polyester (A) to the surfactant (B) [(A) / (B)] is preferably 1.0 to 4.0, more preferably 1.0 to 2.3, and most preferably 1.0 to 1.5, from the viewpoint of odor discrimination performance.

[0060] The polyester (A) and the surfactant (B) may or may not be compatible with each other.

[0061] <Conductive carbon material (C)> In this specification, the conductive carbon material (C) refers to a carbon material having a volume resistivity of 0.1 Ω·cm or less. The above-mentioned resin composition is in a state in which the conductive carbon material (C) is dispersed in a mixture of the polyester (A) and the surfactant (B). The conductive carbon materials (C) come into contact with each other to form a conductive path, which gives the resin composition electrical conductivity.

[0062] Examples of the conductive carbon material (C) include carbon black, carbon nanotubes, and graphene.

[0063] Commercially available carbon black products include Ketjenblack EC (trade name manufactured by Akzo, Netherlands), Ketjenblack EC-300J (trade name manufactured by Lion Specialty Chemicals Co., Ltd.), Ketjenblack EC-600JD (trade name manufactured by Lion Specialty Chemicals Co., Ltd.), Seast G116, 116 (trade names manufactured by Tokai Carbon Co., Ltd.), Niteron #10 (trade name manufactured by Nippon Steel Chemical Co., Ltd.), Denka Black (trade name manufactured by Denki Kagaku Kogyo Kabushiki Kaisha), and SUPER C-65 (trade name manufactured by MTI Corporation, USA).

[0064] Commercially available carbon nanotubes include VGCF-H (product name manufactured by Showa Denko KK).

[0065] Commercially available graphene products include those manufactured by Sigma-Aldrich.

[0066] The conductive carbon material (C) is preferably in the form of fibers or spheres.

[0067] In the case of a fibrous form, the fiber diameter is preferably 0.1 to 10 μm, more preferably 0.1 to 5 μm, and the fiber length is preferably 0.1 to 10 μm, more preferably 1 to 10 μm.

[0068] When it is spherical, the primary particle size is preferably 10 nm to 200 nm, and more preferably 20 nm to 150 nm.

[0069] From the viewpoint of keeping the measurement time and measurement error small and thereby maintaining the accuracy rate, the content of the conductive carbon material (C) is preferably 25 to 75% by weight, more preferably 30 to 65% by weight, and most preferably 35 to 55% by weight, based on 100% by weight of the total of the polyester (A), the ionic surfactant (B), and the conductive carbon material (C). Alternatively, from the viewpoint of the receptive sensitivity of the odorant, the content of the conductive carbon material (C) may be 5 to 30% by weight, 5 to 20% by weight, or 5 to 10% by weight, based on 100% by weight of the total of the polyester (A), the surfactant (B), and the conductive carbon material (C).

[0070] <Method of producing resin composition> A specific example of the method for producing a resin composition according to one embodiment of the present invention is as follows.

[0071] The resin composition is obtained as a slurry by mixing polyester (A), surfactant (B), conductive carbon material (C) and solvent (D) and kneading them uniformly with a stirrer. This is applied to the gap between a pair of metal wirings, and then dried by heating to obtain a dried product, which is the odorant receiving layer.

[0072] The solvent (D) is not particularly limited as long as it is a medium that can be removed by drying, and preferred examples thereof include N-methylpyrrolidone, N,N-dimethylformamide, N,N-dimethylacetamide, ethylene acetate, water, toluene, and xylene.

[0073] [2. Sensor element 31] The above-mentioned resin composition exhibits different changes in electrical conductivity over time when odorant A is adsorbed to the resin composition than when odorant B, which is different from odorant A, is adsorbed to the resin composition. By utilizing this property, a sensor element 31 capable of detecting and identifying odorants can be realized.

[0074] The outline and effects of a sensor element 31 to which a resin composition according to one embodiment of the present invention is applied will be described below.

[0075] The sensor element 31 includes an odorant receiving layer 315 containing the resin composition described above, a first metal wiring 313A, and a second metal wiring 313B. In the following description, when there is no need to distinguish between the first metal wiring 313A and the second metal wiring 313B, they may be referred to as metal wiring 313.

[0076] Here, the first metal wiring 313A and the second metal wiring 313B will be described with reference to Fig. 2 and Fig. 3. Fig. 2 is a top view showing an example of the configuration of sensor element 31, and Fig. 3 is a cross-sectional view showing an example of the configuration of sensor element 31 shown in Fig. 2.

[0077] The first metal wiring 313A and the second metal wiring 313B are metal wirings that function as electrodes for measuring changes in the electrical conductivity of the odorant receiving layer 315 (i.e., the resin composition). That is, the first metal wiring 313A and the second metal wiring 313B are spaced apart from each other, and the odorant receiving layer 315 is in contact with at least a portion of the first metal wiring and at least a portion of the second metal wiring. In one example, the first metal wiring 313A and the second metal wiring 313B are metal wirings that are not in direct contact with each other, and may be metal wirings that are approximately parallel to each other, as shown in FIG. 2.

[0078] As shown in FIG. 2, the metal wiring 313 including the first metal wiring 313A and the second metal wiring 313B may be disposed on the substrate 311. The substrate 311 may be a substrate such as glass epoxy generally used for electronic circuits. The metal wiring 313 may be a metal wiring such as copper or gold. The thickness of each of the first metal wiring 313A and the second metal wiring 313B as viewed from a direction perpendicular to the substrate surface is preferably 10 μm to 2 mm, more preferably 10 μm to 1 mm. The height, i.e., thickness, of each of the first metal wiring 313A and the second metal wiring 313B as viewed from a direction parallel to the substrate surface is preferably 1 μm to 100 μm, more preferably 10 μm to 50 μm. The interval between the first metal wiring 313A and the second metal wiring 313B is preferably 1 μm to 1 mm, more preferably 1 μm to 100 μm. The length of the metal wiring 313 is preferably 10 μm to 50 mm, and more preferably 10 μm to 30 mm.

[0079] The metal wiring 313 may be disposed on a seal substrate 312. FIG. 3 shows the AA cross section of FIG. 2. As shown in FIG. 3, the seal substrate 312 may be disposed on a substrate 311 such as glass epoxy, and the metal wiring 313 may be disposed on the seal substrate 312. A vinyl tape 314 may be used to fix the seal substrate 312 on the substrate 311. The vinyl tape 314 may also be used to adjust the length of the exposed portion of the metal wiring 313 by masking the excess portion of the metal wiring 313. Here, the exposed portion of the metal wiring 313 is the portion where the metal wiring 313 and the odorant receiving layer 315 contact each other. The vinyl tape 314 may also be an insulator for adjusting the length of the portion where the metal wiring 313 and the odorant receiving layer 315 contact each other.

[0080] The odorant receiving layer 315 may be in contact with at least a portion of the first metal wiring 313A and at least a portion of the second metal wiring 313B. The odorant receiving layer 315 may be arranged to fill the area between the first metal wiring 313A and the second metal wiring 313B, as shown in Figures 2 and 3, for example.

[0081] When the electrical conductivity of the odorant receiving layer 315 (ie, the electrical conductivity of the sensor element 31) is low, it is desirable that the distance between the first metal wiring 313A and the second metal wiring 313B be a predetermined distance (eg, 500 μm) or less.

[0082] The sensor element 31 is capable of detecting and identifying various odorous substances by applying a resin composition that exhibits different changes in electrical conductivity over time when odorous substance A is adsorbed and when odorous substance B, which is different from odorous substance A, is adsorbed.

[0083] [3. Odor Sensor 30] The outline and effects of an odor sensor 30 employing a sensor element 31 will be described below with reference to Fig. 1. Fig. 1 is a block diagram showing an example of the configuration of an odor measuring device 100 including an odor sensor 30 employing a sensor element 31. Note that in the sensor element 31 shown in Fig. 1, the vinyl tape 314 is omitted for simplification.

[0084] The odor sensor 30 includes a sensor element 31 that detects an odor substance, a constant current source 32 (power supply), and a voltmeter 33 (measuring device).

[0085] The first metal wiring 313A and the second metal wiring 313B of the sensor element 31 are connected by a lead wire W. An example in which a constant current source 32 and a voltmeter 33 are arranged on the lead wire W is shown in FIG.

[0086] The constant current source 32 is a power source for supplying power to the sensor element 31. The constant current source 32 supplies a constant current (for example, a direct current of 1 mA) to the sensor element 31 via a lead wire.

[0087] The voltmeter 33 measures the potential difference generated between the first metal wiring 313A and the second metal wiring 313B when a constant current supplied from the constant current source 32 is supplied to the odorant receiving layer 315.

[0088] Although not a required component, the odor sensor 30 may further include a housing 34. The housing 34 is a container capable of containing air containing an odor substance. When the odor sensor 30 includes the housing 34, the sensor element 31 is disposed within the housing 34.

[0089] The housing 34 has an inlet 341 for introducing an odorant and an outlet 342 for discharging air containing the odorant. The odorant may be introduced by inserting filter paper P or the like soaked in the odorant into the housing 34 from the inlet 341, or by inserting air containing the odorant into the housing 34 from the inlet 341. The housing 34 is a container for containing air containing the odorant at a predetermined concentration (e.g., 200 ppm) or more.

[0090] Although not essential, an airflow generating fan 35 may be provided at the exhaust port 342 of the housing 34. The airflow generating fan 35 is for generating an airflow inside the housing 34 and discharging the gas inside the housing 34 from the exhaust port 342 to the outside of the housing 34.

[0091] The odor sensor 30 may include a constant voltage source (power supply) (not shown) instead of the constant current source 32, and an ammeter (measuring device) (not shown) instead of the voltmeter 33. In this case, the constant voltage source functions as a power supply for supplying power to the sensor element 31, and applies a constant voltage to the sensor element 31 via the lead wires. Meanwhile, the ammeter measures the value of the current flowing between the first metal wiring 313A and the second metal wiring 313B when a constant voltage is applied to the odorant receiving layer 315.

[0092] The odor sensor 30 outputs a measurement value that indicates the change over time in the electrical conductivity of the sensor element 31 before and after an odor substance is adsorbed to the sensor element 31. This makes it possible to detect and identify various odor substances.

[0093] 4. Odor measuring device 100 The odor sensor 30 described above can output the change over time in the electrical conductivity of the sensor element 31 for each odor substance when various odor substances are adsorbed to the sensor element 31. By applying this odor sensor 30, it is possible to compare the change over time in the electrical conductivity of the sensor element 31 when odor substance A is adsorbed to the sensor element 31 with the change over time in the electrical conductivity of the sensor element 31 when odor substance B is adsorbed to the sensor element 31. Based on such a comparison result, it is possible to realize an odor measurement device 100 that can estimate the odor substance adsorbed to the sensor element 31.

[0094] Furthermore, the odor measuring device 100 can estimate odor substances with high accuracy by using the estimation model 22 generated by machine learning. The estimation model 22 can be generated using learning data including a combination of measurement values ​​measured when each of a plurality of odor substances is adsorbed to at least one sensor element 31, and identification information unique to the odor substance that provided the measurement value.

[0095] The following describes the outline and effects of an odor measuring device 100 that uses the odor sensor 30. The odor measuring device 100 is a device that estimates the odor substance adsorbed to a sensor element 31 based on a change in electrical conductivity that occurs in the sensor element 31 to which the above-mentioned resin composition is applied.

[0096] First, the configuration of an odor measurement device 100 according to one embodiment of the present invention will be described with reference to Fig. 1. Fig. 1 is a block diagram showing an example of the configuration of the odor measurement device 100.

[0097] As shown in FIG. 1, the odor measurement device 100 includes an estimation device 10 and an odor sensor 30.

[0098] (Estimation device 10) The estimation device 10 is a device that estimates an odor substance detected by an odor sensor 30. The estimation device 10 is, for example, a computer, and includes a CPU and a memory (not shown). The estimation device 10 is communicatively connected to the odor sensor 30. Specifically, the estimation device 10 performs estimation of an odor substance by analyzing a measurement value acquired from the odor sensor 30. The configuration of the estimation device 10 will be described later.

[0099] <Generation of Estimation Model 22> Next, the configuration of the odor measuring device 100 that performs the process of generating the estimation model 22 used to estimate odor substances, and the process of generating the estimation model 22 will be described with reference to FIGS. 4 and 5. FIG.

[0100] The estimation model 22 is generated by machine learning using learning data including a combination of measurement values ​​measured by the voltmeter 33 when each of a plurality of odor substances is adsorbed to at least one sensor element, and identification information specific to the odor substance that gave the measurement value. Here, the identification information specific to the odor substance may be, for example, the name, CAS number, and chemical formula of the odor substance.

[0101] (Configuration of the Estimation Device 10 (Generation of Estimation Model 22)) Fig. 4 is a functional block diagram showing an example of the configuration of the odor measuring device 100. For ease of explanation, the same reference numerals are given to members having the same functions as those explained in Fig. 1, and the explanation thereof will not be repeated.

[0102] As shown in FIG. 4, the estimation device 10 includes an input unit 15, a control unit 1, and a storage unit 2.

[0103] The input unit 15 is for accepting various input operations from a user, and may be, for example, a keyboard, a mouse, a touch panel, or the like.

[0104] The control unit 1 includes a measurement value acquiring unit 11 (acquiring unit), a change pattern analyzing unit 12 (analyzing unit), a learning control unit 13, and an estimation model generating unit .

[0105] The measurement value acquiring unit 11 acquires a measurement value from the voltmeter 33. Furthermore, the measurement value acquiring unit 11 uses the acquired measurement value to calculate a value (e.g., resistance value, impedance, etc.) indicating the electrical conductivity of the sensor element 31. The measurement value acquiring unit 11 may acquire the measurement value from the voltmeter 33 at a predetermined time interval (e.g., 0.1 second interval).

[0106] The change pattern analysis unit 12 analyzes the change over time in the electrical conductivity of at least one sensor element 31. Using the resistance value calculated by the measurement value acquisition unit 11, the change pattern analysis unit 12 calculates a value indicating the amount of change in the electrical conductivity of the sensor element 31 due to the adsorption of an odorant. The change pattern analysis unit 12 generates data indicating a change pattern indicating the time change in the calculated amount of change in electrical conductivity. When the generated change pattern is a known odorant, the change pattern analysis unit 12 may store the generated change pattern in the change pattern database 21 (learning data) in association with identification information specific to the known odorant.

[0107] The learning control unit 13 reads out the change pattern database 21 from the storage unit 2 and controls the generation of the estimation model 22 by machine learning. Here, the change pattern database 21 is a database that includes combinations of measurement values ​​measured when multiple odor substances are adsorbed to the sensor element 31 and identification information unique to the known odor substances that provided the measurement values. The learning control unit 13 inputs the change pattern read out from the change pattern database 21 to the estimation model generation unit 14. In addition, the learning control unit 13 compares the identification information of the odor substance corresponding to the change pattern input to the estimation model generation unit 14 with the estimation result output from the estimation model generation unit 14, and outputs a correction instruction according to the comparison result to the estimation model generation unit 14.

[0108] The estimation model generation unit 14 generates the estimation model 22 by a machine learning algorithm using the change patterns stored in the change pattern database 21. The estimation model generation unit 14 may be configured to generate the estimation model 22 by using a known supervised machine learning algorithm. Examples of machine learning algorithms that can be applied to the estimation model generation unit 14 include the k-nearest neighbor method, logistic regression, support vector machine, random forest, and neural network.

[0109] (Process for generating estimation model 22) Specific processes performed by each part of the control unit 1 will be described below with reference to Fig. 5. Fig. 5 is a flowchart showing an example of the flow of processes performed by the estimation device 10 to generate the estimation model 22.

[0110] First, the measurement value acquiring unit 11 acquires the voltage value V0 measured by the odor sensor 30 before the filter paper P soaked in the odor substance is inserted into the housing 34, and calculates the resistance value R0 (step S11). The resistance value R0 is preferably 200 to 1000 Ω, more preferably 250 to 900 Ω, and most preferably 300 to 800 Ω.

[0111] Meanwhile, the input unit 15 accepts input of the name of a known odor substance that has been soaked into the filter paper P inserted in the housing 34 (step S12). The process of step S12 may be performed before step S11.

[0112] Next, the measurement value acquiring unit 11 acquires the voltage value V measured by the odor sensor 30 immediately after the filter paper P soaked with the known odor substance is inserted into the housing 34, and calculates the resistance value R (step S13).

[0113] Next, the change pattern analysis unit 12 calculates R / R0 using the resistance value R0 and the resistance value R (step S14). R / R0 is a value indicating the amount of change in electrical conductivity of the sensor element 31 due to the adsorption of a known odorant. The change pattern analysis unit 12 may calculate R-R0 instead of R / R0. The change pattern analysis unit 12 stores the change pattern of R / R0 over time in the change pattern database 21 in association with the name of the input known odorant (step S15).

[0114] If no change pattern has been stored for a given type of existing odor substance (NO in step S16), that is, if there is still insufficient data to use for machine learning, the process returns to step S11.

[0115] If a change pattern is stored for a predetermined type of existing odor substance (YES in step S16), the learning control unit 13 reads out the change pattern for the known odor substance stored in the change pattern database 21 and inputs it to the estimation model generation unit 14. The estimation model generation unit 14 generates an estimation model 22 by a machine learning algorithm using the change pattern stored in the change pattern database 21 (step S17).

[0116] The estimation model generation unit 14 stores the estimation model 22 generated by a predetermined machine learning in the storage unit 2 (step S18).

[0117] 4 and 5, the estimation device 10 generates the estimation model 22, but is not limited to this. For example, the estimation device 10 may provide an external computer different from the estimation device 10, which has the same functions as the learning control unit 13 and the estimation model generation unit 14, with the same data as the change pattern database 21 to generate the estimation model 22.

[0118] <Identification of odor substances> Next, the configuration of the odor measuring device 100a that estimates an odor substance using the estimation model 22 and the estimation process will be described with reference to FIGS.

[0119] (Configuration of Estimation Device 10a (Execution of Estimation Process)) Fig. 6 is a functional block diagram showing an example of the configuration of an odor measuring device 100a. For ease of explanation, the same reference numerals are given to members having the same functions as those described in Fig. 1 and Fig. 4, and the description thereof will not be repeated.

[0120] As shown in Fig. 6, the estimation device 10a includes a control unit 1a, a storage unit 2a, and an output unit 18. Here, Fig. 6 shows a configuration example in which the estimation device 10 shown in Fig. 4 is used for odor substance estimation processing. That is, the estimation device 10 shown in Fig. 4 and the estimation device 10a shown in Fig. 6 may be computers having the same hardware configuration.

[0121] The output unit 18 is for presenting the estimation result to the user, and may be, for example, a display, a speaker, a lamp, or the like.

[0122] The control unit 1 a includes a measurement value acquiring unit 11 (acquiring unit), a change pattern analyzing unit 12 (analyzing unit), an estimating unit 16, and an output control unit 17.

[0123] The estimation unit 16 uses the estimation model 22 to estimate the odor substance from the analysis results obtained by analyzing the measurement values ​​obtained from the odor sensor 30.

[0124] The output control unit 17 controls the output unit 18 to output the estimation result.

[0125] (Estimation process) Specific processes performed by each part of the control unit 1a will be described below with reference to Fig. 7. Fig. 7 is a flow chart showing an example of the process flow when the estimation device 10a estimates an odor substance.

[0126] First, the measurement value acquiring unit 11 acquires the voltage value V0 measured in the odor sensor 30 before the filter paper P soaked with the odor substance is inserted into the housing 34, and calculates the resistance value R0 (step S1).

[0127] Next, the measurement acquisition unit 11 acquires the voltage value V measured by the odor sensor 30 immediately after inserting the filter paper P soaked with the unknown (i.e., the odor substance to be estimated) into the housing 34, and calculates the resistance value R (step S2).

[0128] Next, the change pattern analysis section 12 calculates R / R0 using the resistance value R0 and the resistance value R (step S3).

[0129] Next, the estimation unit 16 estimates the unknown odor substance from the pattern of change in R / R0 over time based on the estimation model 22 (step S4).

[0130] The output control unit 17 controls the output unit to output the estimation result (step S5).

[0131] <Embodiment 2> In the above embodiment, the odor sensor 30 including one sensor element 31 has been described, but the odor sensor 30 may include two or more sensor elements 31. For example, the odor sensor 30b may include sensor elements 31 and 31b in which the resin compositions used in the odorant receiving layer 315 are different from each other. This will be described with reference to FIG. 8. FIG. 8 is a block diagram showing an example of the configuration of an odor measuring device 100b according to another embodiment of the present invention. For ease of explanation, the same reference numerals are used for components having the same functions as the components described in FIG. 1, and the description thereof will not be repeated.

[0132] For example, the odor measuring device 100b shown in Fig. 8 includes odor sensors 30 and 30b and an estimation device 10b. The odor sensor 30b includes a sensor element 31 and a sensor element 31b, and the resin composition used in the odorant receiving layer 315 of the sensor element 31 and the odorant receiving layer 315b of the sensor element 31b may be different.

[0133] The estimation device 10b may be a computer having the same configuration as the estimation devices 10 and 10a. The estimation device 10b acquires and analyzes a first measurement value measured by a voltmeter 33 when a constant current is supplied from a constant current source 32 to a sensor element 31, and a second measurement value measured by a voltmeter 33b when a constant current is supplied from a constant current source 32b to a sensor element 31b.

[0134] By providing multiple sensor elements in which resin compositions with different odorant adsorption properties are used in the odorant receiving layer, the odor measuring device 100b can simultaneously perform estimations for multiple odorants. In addition to the sensor element according to one embodiment of the present invention, a sensor element that does not contain a surfactant (B) in the odorant receiving layer may be used in combination.

[0135] Furthermore, by using the odor measurement device 100b, it is possible to obtain a first change pattern indicating a change in the electrical conductivity of the sensor element 31 and a second change pattern indicating a change in the electrical conductivity of the sensor element 31b for each known odor substance. The estimation model 22 may be generated by machine learning using both the first change pattern and the second change pattern. The odor measurement device 100b estimates odor substances using the estimation model 22 generated in this way, and therefore is able to more precisely identify each odor substance.

[0136] <Example of software implementation> The control block (particularly the control unit 1) of the estimation devices 10, 10a, 10b may be realized by a logic circuit (hardware) formed on an integrated circuit (IC chip) or the like, or may be realized by software.

[0137] In the latter case, the estimation device 10, 10a, 10b includes a computer that executes instructions of a program, which is software that realizes each function. The computer includes, for example, one or more processors, and a computer-readable recording medium that stores the program. The object of the present invention is achieved by the processor reading the program from the recording medium and executing it in the computer. The processor may be, for example, a CPU (Central Processing Unit). The recording medium may be a "non-transient tangible medium", such as a ROM (Read Only Memory), as well as a tape, a disk, a card, a semiconductor memory, a programmable logic circuit, or the like. The device may further include a RAM (Random Access Memory) that expands the program. The program may be supplied to the computer via any transmission medium (such as a communication network or a broadcast wave) that can transmit the program. Note that one aspect of the present invention may also be realized in the form of a data signal embedded in a carrier wave, in which the program is embodied by electronic transmission.

[0138] The present invention is not limited to the above-described embodiments, and various modifications are possible within the scope of the claims. Embodiments obtained by appropriately combining the technical means disclosed in different embodiments are also included in the technical scope of the present invention. EXAMPLES

[0139] The present invention will be further described below with reference to examples and comparative examples, but the present invention is not limited thereto. In the following, % means % by weight, and parts means parts by weight, unless otherwise specified.

[0140] [Manufacturing Examples 1-16] <Preparation of polyester (A) 1> A heating / cooling device, a thermometer, a temperature control device, a polymerization catalyst introduction tube, and a nitrogen introduction tube were attached to a reaction vessel equipped with an agitator, an agitator, a nitrogen inlet, and an outlet. This reaction vessel has a seal structure that allows the inside of the reaction vessel to be depressurized. The polycarboxylic acid and polyhydric alcohol in the amounts shown in Table 1 were added to the reaction vessel, and 0.5 parts by weight of titanium diisopropoxy bistriethanolamine was further added as a polymerization catalyst. Thereafter, the reaction vessel was heated to 210° C. with stirring, and the polycarboxylic acid and the polyhydric alcohol were reacted for 5 hours while distilling off the by-product water while flowing a nitrogen stream. Next, the pressure inside the reaction vessel was reduced to 0.5 kPa to 2.5 kPa, and the above reaction was allowed to proceed for another hour, thereby obtaining polyesters (A-1) to (A-16). The number average molecular weight of each of the obtained polyesters (A) was measured by gel permeation chromatography (GPC) under the following conditions, and the results are shown in Table 1.

[0141] Equipment: Tosoh Corporation HLC-8120 Column: TSK GEL GMH6 x 2 (Tosoh Corporation) Measurement temperature: 40℃ Sample solution: 0.25% by weight THF (tetrahydrofuran) solution Solution injection volume: 100μl Detector: Refractive index detector The number average molecular weight was measured by dissolving the polyester (A) in THF and filtering off the insoluble matter using a glass filter to prepare a sample solution.

[0142] The polycarboxylic acids (terephthalic acid, phthalic acid, isophthalic acid, succinic acid, adipic acid, maleic acid, fumaric acid, and trimellitic acid) used in Production Examples 1 to 16 were all purchased as reagents from Tokyo Chemical Industry Co., Ltd. and used as they were. The polyhydric alcohols (ethylene glycol, 1,3-butanediol, 1,6-hexanediol, 1,10-decanediol, diethylene glycol, polyethylene glycol having a number average molecular weight of 300, and 1,4-cyclohexanedimethanol) used in Production Examples 1 to 16 were all purchased as reagents from Tokyo Chemical Industry Co., Ltd. and used as they were. The bisphenol A propylene oxide 2-mol adduct used in Production Examples 1 to 16 was Epolite 3002 (N) manufactured by Kyoeisha Chemical Co., Ltd.

[0143] [Table 1]

[0144] [Examples 17-29] <Preparation of higher alcohol ethylene oxide adduct> A reaction vessel equipped with a stirring blade, a stirring device, a nitrogen inlet, an outlet, and an ethylene oxide inlet, each having a seal structure capable of reducing or pressurizing the inside of the reaction vessel, was equipped with a heating / cooling device, a thermometer, a pressure gauge, a temperature control device, and a nitrogen inlet. In the reaction vessel, parts by weight of higher alcohols listed in Table 2, which had been dried for 2 hours with 30 parts of molecular sieves 3A, and 3 parts of potassium hydroxide as a catalyst, were charged under a nitrogen atmosphere, and the atmosphere was purged with reduced pressure and nitrogen. The inside of the reaction vessel was heated to 160°C, and parts by weight of ethylene oxide listed in Table 2 were added dropwise while adjusting the flow rate so that the pressure inside the reaction vessel was 0.5 MPa (G), to cause a reaction. After the end of the dropwise addition, stirring was continued for 1 hour, and then the temperature inside the reaction vessel was lowered to room temperature, to obtain higher alcohol ethylene oxide adducts (NS-1) to (NS-13).

[0145] [Table 2]

[0146] [Examples 1 to 173, Comparative Examples 1 to 16] <Preparation of slurry> The polyester (A), surfactant (B), conductive carbon material (C) and ethyl acetate as a solvent (D) were weighed out in the amounts shown in Tables 3 to 10 into a polypropylene container to obtain a mixture. The mixture was stirred at 2000 rpm for 60 minutes using a centrifugal mixer (ARE-310 manufactured by Thinky Corporation) to obtain a slurry. The slurry was used as a resin composition for forming an odorant receiving layer. In Tables 3 to 13 below, the "(A) / (B) ratio" refers to the weight ratio of the polyester (A) to the surfactant (B).

[0147] The surfactants (B) other than the higher alcohol ethylene oxide adducts shown in Tables 3 to 13 were commercially available from Tokyo Chemical Industry Co., Ltd. The higher alcohol ethylene oxide adducts used were (NS-1) to (NS-13) prepared by the above-mentioned method.

[0148] As for the conductive carbon materials (C) shown in Tables 3 to 13, SUPER-C65 (carbon black) manufactured by MTI Corporation, VGCF-H (carbon nanotubes) manufactured by Showa Denko K.K., Denka Black (carbon black) manufactured by Denka Co., Ltd., Ketjen Black EC-300J (carbon black) and Ketjen Black EC-600JD (carbon black) manufactured by Lion Specialty Chemical Co., Ltd. were used.

[0149] <Fabrication of sensor element> A seal substrate including a pair of metal wirings was cut out from a seal substrate (ICB-073, manufactured by Sanhayato Co., Ltd.) with multiple metal wirings with a gap width of 500 μm. The cut seal substrate was further cut so that the length of the metal wiring was 3.5 cm.

[0150] The cut seal substrate was attached to a glass plate with double-sided tape so that the metal wiring was on top. In addition, the excess part of the metal wiring was masked with vinyl tape so that the length of the exposed part of the metal wiring was 3.0 cm. Next, each slurry prepared by the above method was applied to the exposed part of the metal wiring using a bar coater (No. 4). After application, it was dried for 3 hours in a wind dryer heated to 100°C. After drying, it was cooled to room temperature, and the metal wiring with the odorant receptive layer was peeled off from the glass plate to obtain sensor elements (E-1) to (E-173) and comparative sensor elements (E'-1) to (E'-16).

[0151] [Table 3]

[0152] [Table 4]

[0153] [Table 5]

[0154] [Table 6]

[0155] [Table 7]

[0156] [Table 8]

[0157] [Table 9]

[0158] [Table 10]

[0159] [Table 11]

[0160] [Table 12]

[0161] [Table 13]

[0162] [Examples 174 to 336, Comparative Examples 17 to 32] <Evaluation of resin composition and sensor element> The resin composition can be evaluated by comparing the data obtained from the sensor element (E) and the comparative sensor element (E').

[0163] <Measurement method> A housing was created that was equipped with an inlet for introducing the sample (odor substance) and a fan for creating an airflow to spread the sample evenly. Among the sensor elements (E-1) to (E-163) and the comparative sensor elements (E'-1) to (E'-16), which had lead wires soldered to take out the terminals to the outside, the sensor elements to be evaluated were placed in the housing.

[0164] A 1 mA constant current power supply and a voltmeter to measure the voltage across both terminals of the lead wire were attached to the ends of the lead wires taken out of the case, and the voltmeter readings were recorded by a computer.

[0165] The sample was immersed in filter paper so that the concentration of the sample in the case was 200 ppm, and the filter paper was inserted through the inlet. Measurements were started immediately after the filter paper was inserted. 60 seconds after the start of the measurement, the fan was rotated again for 60 seconds, and measurements were taken while discharging the steam inside the case to the outside. The voltage was measured at 0.1 second intervals. The measurement was also repeated 100 times under the same conditions.

[0166] The analytes used were hexane, ethyl acetate, methanol, diethyl carbonate, or toluene.

[0167] <Evaluation method> The electrical resistance R was calculated according to Ohm's law using the voltage measured at each time and the current value of 1 mA supplied from the constant current power supply. The resistance R0 before the introduction of the sample was measured in advance, and R / R0 was calculated.

[0168] The responsiveness of the sensor element to each sample was analyzed using the k-nearest neighbor method, using the time change of R / R0 at 0.1 second intervals. The above measurements were performed on the sensor elements (E-1) to (E-163) or the comparative sensor elements (E'-1) to (E'-16), and the measurement data for each example and each comparative example (100 times x 5 samples = 500 times in total) was randomly divided so that the number of learning data: the number of test data = 80:20, and a classifier (learning model) was created using the k-nearest neighbor method for the learning data. The accuracy rate when the classifier of each example and comparative example is made to classify the test data is used as a performance index of the sensor element, and it can be determined that the higher the accuracy rate, the higher the performance of the sensor element. For the creation of the classifier and the calculation of the accuracy rate in each example, both the data obtained by the corresponding sensor element (E) and the data obtained by the comparative sensor element (E') using the same polyester (A) as the sensor element (E) were used.

[0169] <Evaluation Results> The evaluation results are shown below.

[0170] [Table 14]

[0171] [Table 15]

[0172] [Table 16]

[0173] [Table 17]

[0174] [Table 18]

[0175] The accuracy rates calculated in the performance evaluation for the odor sensors using the sensor elements produced in Examples 1 to 163 and the odor sensors using the sensor elements produced in Comparative Examples 1 to 16 are compared.

[0176] For example, the accuracy rate of the odor sensor of Example 174, which used sensor element (E-1), was 71%, while the accuracy rate of the odor sensor of Comparative Example 30, which used a comparative sensor element (E'-14) that used the same polyester as sensor element (E-1), was 45%.

[0177] As shown in Tables 14 to 18, the accuracy rates of the odor sensors of Examples 174 to 336, which used sensor elements (E-1) to (E-163), were higher than the accuracy rates of the odor sensors of Comparative Examples 17 to 32, which used comparative sensor elements (E'-1) to (E'-16).

[0178] It can be said that the resin composition according to one embodiment of the present invention and an odor sensor using a sensor element using the same have good odor discrimination performance.

[0179] In addition, the Rmax / R0 value tended to decrease as the content of the conductive carbon material (C) increased. Since the smaller the Rmax / R0 value, the greater the measurement error, the better.

[0180] [Examples 337 to 346] <Evaluation of the content of conductive carbon material (C)> By changing the content of the conductive carbon material (C), the value of R / R0 immediately after the start of measurement can be controlled. R / R0 was measured using each of the sensor elements (E-143) to (E-152) in the same manner as described above. After the measurement was completed, R / R0 one second after the start of the measurement was extracted and listed in Table 19. The maximum value of R during the measurement was taken as Rmax, and the value of Rmax / R0 (%) was also listed in Table 19.

[0181] [Table 19]

[0182] As shown in Table 19, the more the conductive carbon material (C) is increased, specifically when it is 25% by weight or more, the value of R / R0 increases 1 second after the start of measurement, and when the content of the conductive carbon material (C) is in the range of 30% by weight or more, the value of R / R0 1 second after the start of measurement increases significantly. Since the larger R / R0 1 second after the start of measurement leads to a shorter measurement time and a reduced power consumption, it can be said that the content of the conductive carbon material (C) is preferably 25% by weight or more, and particularly preferably 30% by weight or more. In addition, when the content of the conductive carbon material (C) is 75% by weight or less, the adhesion of the conductive carbon material (C) to the seal substrate is better, and it is more suitable for the sensor element (E).

[0183] On the other hand, as shown in Table 19, the Rmax / R0 value decreases as the content of the conductive carbon material (C) increases, and the measurement error increases. However, in Examples 295 to 304, when the content of the conductive carbon material (C) is in the range of 75% by weight or less, the Rmax / R0 value does not decrease significantly, and it is considered that the measurement error is kept small. Therefore, from the viewpoint of maintaining the accuracy rate by keeping the measurement time and measurement error small, the content of the conductive carbon material (C) is preferably 25 to 75% by weight, more preferably 30 to 65% by weight, and most preferably 35 to 55% by weight.

[0184] [Example 347, Comparative Example 33] <Evaluation of odor sensors and odor measuring devices> The odor sensor and odor measuring device can be evaluated by comparing the accuracy rate when identifying odors using sensor elements (E-1) to (E-163) incorporated into the system described below with the accuracy rate when identifying odors using comparison sensor elements (E'-1) to (E'-16).

[0185] <Measurement method> We created a housing equipped with an inlet for introducing the sample (smell) and a fan for creating an airflow to spread the sample evenly. We used an odor sensor (F) in which sensor elements (E-1) to (E-163), each with soldered lead wires for taking out the terminals to the outside, were housed in the housing, and a comparative odor sensor (F') in which comparative sensor elements (E'-1) to (E'-16) were housed in the housing.

[0186] A 1 mA constant current power supply and a voltmeter to measure the voltage across both terminals of the lead wire were attached to the ends of the lead wires taken out of the case, and the voltmeter readings were recorded by a computer.

[0187] The sample was immersed in filter paper so that the concentration of the sample in the case was 200 ppm, and the filter paper was inserted through the inlet. Measurements were started immediately after the filter paper was inserted. 60 seconds after the start of the measurement, the fan was rotated again for 60 seconds, and measurements were taken while discharging the steam inside the case to the outside. The voltage was measured at 0.1 second intervals. The measurement was also repeated 100 times under the same conditions.

[0188] The samples used were d-limonene, bornan-2-one, cis-3-hexenol, β-phenylethyl alcohol, citral, L-carvone, γ-undecalactone, eugenol, and linalyl acetate. All samples were manufactured by Tokyo Chemical Industry Co., Ltd.

[0189] <Evaluation method> The electrical resistance R was calculated according to Ohm's law using the voltage measured at each time and the current value of 1 mA supplied from the constant current power supply. The resistance R0 before the introduction of the sample was measured in advance, and R / R0 was calculated. The measured R0 values ​​are shown in Tables 14 to 18. The maximum value of R during the measurement is taken as Rmax, and the values ​​of Rmax / R0 (%) are also shown in Tables 14 to 18.

[0190] The responsiveness of the sensor element to each sample was analyzed using the k-nearest neighbor method, using the time change of R / R0 at 0.1 second intervals. The above measurements were performed on the odor sensor (F) and the comparative odor sensor (F'), and the measurement data for each sensor (100 times x 9 samples = 900 times in total) was randomly divided so that the number of training data: the number of test data = 80:20, and a classifier (learning model) was created using the k-nearest neighbor method for the training data. The accuracy rate when the test data was classified by the classifiers of each example and comparative example was used as a performance index of the odor sensor, and it can be determined that the higher the accuracy rate, the higher the performance of the sensor.

[0191] <Evaluation Results> As a result of the above-mentioned measurements, the accuracy rate of the odor sensor (F) was 88%, and the accuracy rate of the comparative odor sensor (F') was 49%, which indicates that the resin composition according to one embodiment of the present invention and the odor sensor using the sensor element using the same have good odor discrimination performance.

[0192] [Examples 348 to 510, Comparative Examples 34 to 49] <Evaluation when using a sample that is a mixture> <Method of preparing a mixture sample> In addition to the above-mentioned single substance samples, mixture samples were prepared in the following manner. Menthol, benzaldehyde, ethyl acetate, vanillin, hexanal, ethanol, pentyl valerate, linalool, and 2-propanol were each prepared in a desiccator to be 200 ppm gas, and used as the mixture raw material. Next, a 500 mL two-necked eggplant flask equipped with a three-way cock and a rubber septum was evacuated and then sealed. The following volumes were taken from each mixture raw material with a syringe and injected through the rubber septum of the sealed two-necked eggplant flask. Mixture Sample 1: Menthol (150mL) Benzaldehyde (150mL) Ethyl acetate (150 mL) Mixture Sample 2: Vanillin (150mL) Hexanal (150mL) Ethanol (150 mL) Mixture Sample 3: Pentyl valerate (150mL) Linalool (150mL) 2-Propanol (150 mL)

[0193] <Measurement method including a mixture of samples> As in Examples 174 to 336 and Comparative Examples 17 to 32, the sensor elements to be evaluated among the sensor elements (E-1) to (E-163) and the comparative sensor elements (E'-1) to (E'-16) were placed in the housing. A constant current power supply, a voltmeter, and a computer were also placed in the same manner.

[0194] 20 mL of the mixture sample prepared by the above method was taken with a syringe, and the mixture sample was injected from the inlet. Measurement was started immediately after the injection of the mixture sample. 60 seconds after the start of the measurement, the fan was rotated again for 60 seconds, and the measurement was performed while discharging the steam inside the case to the outside. The voltage was measured at 0.1 second intervals. The measurement was also repeated 100 times under the same conditions.

[0195] <Evaluation method when using a sample that is a mixture> Using the data obtained by the above-mentioned method and the data obtained by the measurement of the specimens as the single unit (Examples 174 to 336, Comparative Examples 17 to 32), evaluation was performed in the same manner as the evaluation method performed on the specimens as the single unit, and the accuracy rate was calculated. That is, the evaluation was performed in the following manner.

[0196] The electrical resistance R was calculated according to Ohm's law using the voltage measured at each time and the current value of 1 mA supplied from the constant current power supply. The resistance R0 before the introduction of the sample was measured in advance, and R / R0 was calculated.

[0197] The responsiveness of the sensor element to each sample was analyzed using the k-nearest neighbor method, using the time change of R / R0 at 0.1 second intervals. The above measurements were performed on the sensor elements (E-1) to (E-163) or the comparative sensor elements (E'-1) to (E'-16), and the results of each Example and each The measurement data for 100 times x 8 samples = a total of 800 times corresponding to the comparative example was randomly divided so that the number of training data: the number of test data = 80:20, and a classifier (learning model) was created using the k-nearest neighbor method for the training data. The accuracy rate when the classifier of each example and comparative example was made to classify the test data was used as a performance index of the sensor element, and it can be determined that the higher the accuracy rate, the higher the performance as a sensor. In creating the classifier and calculating the accuracy rate in each example, both the data obtained by the corresponding sensor element (E) and the data obtained by a comparative sensor element (E') using the same polyester (A) as the sensor element (E) were used.

[0198] <Evaluation results when using a mixture sample> The evaluation results are shown below.

[0199] [Table 20]

[0200] [Table 21]

[0201] [Table 22]

[0202] [Table 23]

[0203] [Table 24]

[0204] The accuracy rates calculated in the performance evaluation for each of the odor sensors described in Examples 348 to 510 and Comparative Examples 34 to 49 are compared.

[0205] For example, the accuracy rate of the odor sensor of Example 348, which used sensor element (E-1), was 74%, while the accuracy rate of the odor sensor of Comparative Example 47, which used a comparative sensor element (E'-14) that used the same polyester as sensor element (E-1), was 48%.

[0206] As shown in Tables 20 to 24, the accuracy rates of the odor sensors of Examples 348 to 510, which used sensor elements (E-1) to (E-163), were higher than the accuracy rates of the odor sensors of Comparative Examples 34 to 49, which used comparative sensor elements (E'-1) to (E'-16).

[0207] It can be said that an odor sensor using a resin composition according to one embodiment of the present invention and a sensor element using the same have good odor discrimination performance even when the sample is a mixture. [Industrial Applicability]

[0208] The present invention is useful as an odor identification sensor for medical use, gas detection, agriculture, and other industrial and daily uses. For example, farmers can use the odor identification sensor to determine the maturity of fragrant crops and manage the optimal harvest timing. In addition, the odor identification sensor can be used to digitize the odors of products such as food and cosmetics, helping to improve the efficiency of product development and stabilize quality. [Explanation of symbols]

[0209] 10, 10a, 10b Estimation device 11 Measurement value acquisition unit (acquisition unit) 12 Change pattern analysis section (analysis section) 16 Estimation part 30, 30b Odor sensor 31, 31b Sensor element 32, 32b Constant current source (power supply) 33, 33b Voltmeter (measuring instrument) 100, 100a, 100b Odor measuring device 313A 1st metal wiring 313B 2nd metal wiring 315, 315b Odorant receptor layer

Claims

1. A resin composition for forming an odorant receiving layer, A polyester (A), a surfactant (B) and a conductive carbon material (C), The resin composition, wherein the surfactant (B) has an HLB value of 8 to 18.

2. 2. The resin composition according to claim 1, wherein the weight ratio of the polyester (A) to the surfactant (B) [(A) / (B)] is 1.0 to 4.

0.

3. The resin composition according to claim 1 or 2, wherein the content of the conductive carbon material (C) is 25 to 75% by weight, relative to a total of 100% by weight of the polyester (A), the surfactant (B) and the conductive carbon material (C).

4. A sensor element comprising an odorant receiving layer containing the resin composition according to any one of claims 1 to 3, a first metal wiring, and a second metal wiring, the first metal wiring and the second metal wiring are spaced apart from each other, A sensor element, wherein the odorant receiving layer is in contact with at least a portion of the first metal wiring and at least a portion of the second metal wiring.

5. At least one sensor element according to claim 4; a power source for supplying power to the sensor element; An odor sensor comprising: a measuring device that outputs a measurement value indicating the electrical conductivity of the odorant receiving layer of a sensor element powered by the power source.

6. The odor sensor according to claim 5 , wherein the power source supplies a constant current or applies a constant voltage to the at least one sensor element.

7. An odor measuring device comprising the odor sensor according to claim 5 or 6 and an estimation device, The estimation device includes: an acquisition unit that acquires the measurement values ​​from the measuring device; an analysis unit that analyzes a change in electrical conductivity of the at least one sensor element over time; An estimation unit that estimates an odor substance based on an estimation model, An odor measuring device in which the estimation model is generated by machine learning using learning data that includes a combination of measurement values ​​measured by the measuring device when each of a plurality of odor substances is adsorbed to the at least one sensor element and identification information unique to the odor substance that provided the measurement value.

8. A control program for causing a computer to function as the odor measuring device according to claim 7, the control program causing a computer to function as the acquisition unit, the analysis unit, and the estimation unit.

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