JP2022045328A - Resin composition for forming odor substance receptive layer, sensor element using the same, odor sensor, and odor measurement device - Google Patents

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) a diene polymer, (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

The present invention relates to a resin composition for forming an odor substance receiving layer, a sensor element using the same, an odor sensor, and an odor measuring device.

With the development of information processing technology in recent years, if the sense of smell, which has not been sufficiently mechanically measured among the five human senses, can be quantified in some way, it will cover a wide range of fields such as medical fields, environment / safety fields, and marketing fields. Expected to be available in the industrial field. So far, as a method for detecting a specific gas substance (gas), high-precision and high-sensitivity measurement has been realized by a semiconductor gas sensor or the like.

The invention described in Patent Document 1 proposes a mechanism for detecting the adsorption of an odorous component on the surface of a conductive polymer by replacing the semiconductor of the semiconductor gas sensor with a conductive polymer. Patent Document 1 reports that it is possible to detect odorous components that are easily thermally decomposed and substances that do not cause a redox reaction on the surface of the detection unit of the sensor.

Further, in Patent Document 2, attention is paid to the property that the electric resistance of a mixture of an organic polymer and a conductive substance changes when exposed to an organic gas. In Patent Document 2, when a plurality of combinations of organic polymers / conductive substances having different organic polymer compositions are prepared from the mixture and these are used as an electric resistance array in a sensor, electricity when exposed to the same organic gas is obtained. It is stated that the resistance changes are different. It is reported in Patent Document 2 that an odor can be identified by assigning a pattern of change in electrical resistance and a type of odor (= a mixture of organic gases) by utilizing this.

Further, in Patent Document 3, it is reported that the response speed of the sensor is improved by adding a plasticizer to the above-mentioned organic polymer.

Japanese Unexamined Patent Publication No. 11-23508 Special Table No. 11-503231 Special Table 2002-51963 Gazette

However, there is room for improvement in the above-mentioned prior art from the viewpoint of odor discrimination performance.

One aspect of the present invention is to provide a resin composition for forming an odor substance receiving layer having improved odor discrimination performance, a sensor element using the same, an odor sensor, and an odor measuring device.

The present inventors have arrived at the present invention as a result of studies for achieving the above-mentioned object.

That is, one aspect of the present invention is a resin composition for forming an odorant receiving layer, which comprises a diene polymer (A), a surfactant (B) and a conductive carbon material (C). Objects, sensor elements using them, odor sensors, and odor measuring devices.

According to one aspect of the present invention, it is possible to provide a resin composition for forming an odor substance receiving layer having improved odor discrimination performance, a sensor element using the same, an odor sensor, and an odor measuring device.

It is a block diagram which shows an example of the structure of the odor measuring apparatus which concerns on one Embodiment of this invention. It is a top view which shows an example of the structure of a sensor element. It is sectional drawing which shows an example of the structure of the sensor element shown in FIG. It is a functional block diagram which shows an example of the structure of an odor measuring device. It is a flowchart which shows an example of the flow of the process which an estimation apparatus generates an estimation model. It is a functional block diagram which shows an example of the structure of an odor measuring device. It is a flowchart which shows an example of the flow of the process of estimating an odor substance by an estimation device. It is a block diagram which shows an example of the structure of the odor measuring apparatus which concerns on another Embodiment of this invention.

An embodiment of the present invention will be described below, but the present invention is not limited thereto. Further, unless otherwise specified in the present specification, "AB" representing a numerical range is intended to be "A or more and B or less".

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

As used herein, the term "odorant" means a substance that can be adsorbed on the odorant receiving layer in a broad sense. Therefore, substances that are not generally regarded as the causative substances of odor are also included. The "odor" often contains a plurality of causative odorous substances, and there are also substances that are not recognized as odorous substances or unknown odorous substances. One embodiment of the present invention focuses on the fact that the amount of an odorant adsorbed on the odorant receiving layer differs depending on the type of the odorant.

It should be noted that even when the term "odorous substance" is simply described in the present specification, it may mean an "aggregate of odorous substances" which may contain a plurality of odorous substances instead of individual odorous substances.

The "odorant" is not particularly limited, but is not limited to, for example, hexane, ethyl acetate, methanol, diethyl carbonate, toluene, d-lymonen, bornan-2-one, cis-3-hexenol, β-phenylethyl alcohol, citral, L-. Carvone, γ-undecalactone, eugenol, linalyl acetate, menthol, benzaldehyde, vanillin, hexanal, ethanol, pentyl valerate, linalol, 2-propanol and the like can be mentioned.

Further, in the present specification, the "odor substance receiving layer" means a layer that adsorbs an odor substance to be identified. 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 later.

It is considered that the sensor described in Cited Document 1 can detect an odor composed of a single compound. On the other hand, many odors are a mixture of multiple substances. Since the sensor described in Cited Document 1 does not have a function of causing the detection unit to identify the odor component, the odor discrimination performance for the mixture is not sufficient. In Cited Document 2, the difference in the chemical structure of the polymer showing conductivity used in the detection part is utilized, and the mixture is made to have a difference in the response to various compounds shown by the detection part through each conductive polymer. It has been shown that the odor can be recognized. However, the chemical structure of the polymer exhibiting conductivity is limited, it is difficult to sensitively separate the response of the detection unit to an arbitrary odor component, and it is difficult to distinguish odors composed of similar components. Reference 3 proposes a method in which a mixture consisting of an organic polymer, a plasticizer, and a conductive substance is used as a detection material in a detection unit, and permeation of an odor component into the organic polymer is detected as a change in electrical resistance of the mixture. There is. By utilizing the fact that different odor components permeate when organic polymers having different compositions are used, an array using a plurality of detection units made of the above-mentioned detection materials containing organic polymers having different compositions can be used in parallel to produce an odor as a mixture. Can be recognized. However, in the above-mentioned organic polymers and organic polymers containing plasticizers, even if a plurality of combinations of organic polymers / conductive substances are prepared, the difference in chemical properties between the organic polymers is small, so that the odor discrimination performance is poor. not enough. With these conventional techniques, for example, it is not possible to accurately detect an actual odor pattern in which a plurality of substances interact with each other or an actual odor pattern due to a substance whose composition is unknown.

The present inventors have different electrical conductivity of the resin composition depending on the amount of the odorous substance adsorbed on the above-mentioned resin composition, and the adsorption process on the above-mentioned resin composition is different for each odorous substance. Focusing on this, we have invented the resin composition, the sensor element, and the like according to the embodiment of the present invention. Then, by using such a resin composition, the odor discrimination performance can be improved. For example, it is possible to identify a real odor pattern in which a plurality of substances interact with each other or a real odor pattern due to a substance whose composition is unknown.

<Diene polymer (A)>
Examples of the diene polymer (A) include isoprene polymer, butadiene polymer, chloroprene polymer, styrene-butadiene polymer, styrene-isoprene polymer, acrylonitrile-butadiene polymer and the like.

The diene polymer (A) may be composed of one kind of polymer or may be a mixture of two or more kinds of polymers.

The diene polymer (A) may be an isoprene polymer, a butadiene polymer, a styrene-butadiene polymer, a styrene-isoprene polymer, or a mixture of any two or more polymers selected from these. It is preferable from the viewpoint of identification accuracy of odorous substances.

The number average molecular weight of the diene polymer (A) is preferably 50,000 to 300,000, more preferably 100,000 to 250,000, and 120, from the viewpoint of repeatability of measurement. It is more preferably 000 to 200,000.

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

Equipment (example): HLC-8120 manufactured by Tosoh Corporation
Column (example): TSK GEL GMH6 2 pieces [manufactured by Tosoh Corporation]
Measurement temperature: 40 ° C
Sample solution: 0.25 wt% THF (tetrahydrofuran) solution Injection amount: 100 μl
Detection device: Refractive index detector

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

The diene compound (A) can be obtained, for example, by a known production method. Specific examples of the known production method include a method of obtaining a diene polymer (A) by solution polymerization or emulsion polymerization of a constituent monomer in the presence of a polymerization catalyst in a solvent.

The solvent is not particularly limited, and is, for example, a non-polar solvent (n-hexane, n-heptane, cyclohexane, benzene, toluene, xylene, mineral oil, etc.) and a polar solvent (1,4-dioxane, tetrahydrofuran, etc.). Anisol, ethylene glycol dimethyl ether, 18-crown-6-ether, pyridine, N, N, N', N'-tetramethylethylenediamine, N, N'-dimethylformamide, N-methylpyrrolidone, ion-exchanged water, etc.) Be done.

The polymerization catalyst is not particularly limited, but for example, an alkali metal catalyst (lithium, sodium, potassium, etc.), an organic metal compound catalyst (n-butyl lithium, sec-butyl lithium, triethyl aluminum, diethyl zinc, etc.), and the like. Grinard reagent-based catalysts (tert-butylmagnesium bromide, etc.), inorganic compound-based catalysts (sodium naphthalenide, potassium naphthalenide, sodium amide, known titanium catalysts, known nickel catalysts, known cobalt catalysts, etc.), inorganic catalysts, etc. Examples thereof include oxide-based catalysts (hydrogen peroxide, sodium persulfate, potassium persulfate, etc.) and redox-based catalysts (mixtures of hydroperoxide and divalent iron ions, etc.). Further, if necessary, a known chain transfer agent (alkyl mercaptan having 2 to 20 carbon atoms, etc.) may be used.

From the viewpoint of industrialization, the polymerization temperature in the solution polymerization or the emulsion polymerization is preferably 0 to 140 ° C, more preferably 40 to 130 ° C.

<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 the surfactant (B) having such an HLB value, good odor discrimination performance can be obtained.

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

The HLB value can be calculated from the ratio of the organic value and the inorganic value of the organic compound.

HLB = 10 × Inorganic / Organicity Here, the inorganic and organic values in the above equation represent the index values that express the organic and inorganic properties proposed by Fujita et al., And the above-mentioned “surfactant”. It can be calculated using the values in the table shown on page 213 of "Introduction".

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

Examples of the anionic surfactant include an alkali metal salt of a carboxylic acid having 10 to 24 carbon atoms and an alkali metal salt of an alkyl sulfonic acid having 14 to 24 carbon atoms.

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 and henicosanoic acid. Examples thereof include docosanoic acid, tricosanoic acid and tetracosanoic acid.

Examples of the alkyl group contained in the alkyl sulfonic 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. The group etc. can be mentioned.

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

Examples of the cationic surfactant include a halide salt of quaternary ammonium having an alkyl group having 12 to 24 carbon atoms.

Examples of the quaternary ammonium having an alkyl group having 12 to 24 carbon atoms include tetrapropylammonium, tetrabutylammonium, tetrapentylammonium, tetrahexylammonium, dimethyldioctylammonium, didecyldimethylammonium, decyltrimethylammonium, and dodecyltrimethyl. Ammonium, Tridecyltrimethylammonium, Hexadecyltrimethylammonium, Methyltrioctylammonium, Octyltrimethylammonium, Tributylmethylammonium, Octadecyltrimethylammonium, Tetradecyltrimethylammonium, Nonadesyltrimethylammonium, Icosyltrimethylammonium, Henicosyltrimethylammonium, Examples thereof include heptadecyltrimethylammonium and pentadecyltrimethylammonium.

Examples of the halide salt include fluoride salts, chloride salts, bromide salts, iodide salts and the like.

Examples of the amphoteric tenside include an intramolecular salt of dimethyl (3-sulfopropyl) ammonium having an alkyl group having 10 to 22 carbon atoms and N-alkyl-N, N-dimethyl having an alkyl group having 10 to 22 carbon atoms. Examples include glycine.

Examples of the dimethyl (3-sulfopropyl) ammonium hydroxide intramolecular salt having an alkyl group having 10 to 22 carbon atoms include decyldimethyl (3-sulfopropyl) ammonium hydroxide intramolecular salt and undecyldimethyl (3-sulfopropyl). ) Ammonium Hydroxide Intramolecular Salt, Dodecyldimethyl (3-sulfopropyl) Ammonium Hydroxide Intramolecular Salt, Tridecyldimethyl (3-sulfopropyl) Ammonium Hydroxide Intramolecular Salt, Tetradecyldimethyl (3-sulfopropyl) Ammonium Hydroxyl Intramolecular salt, pentadecyldimethyl (3-sulfopropyl) ammonium hydroxide intramolecular salt, hexadecyldimethyl (3-sulfopropyl) ammonium hydroxide intramolecular salt, heptadecyldimethyl (3-sulfopropyl) ammonium hydroxyd molecule Internal salt, octadecyldimethyl (3-sulfopropyl) ammonium hydroxide intramolecular salt, nonadesyldimethyl (3-sulfopropyl) ammonium hydroxyd intramolecular salt, icosyldimethyl (3-sulfopropyl) ammonium hydroxide intramolecular salt, Examples thereof include henicosyldimethyl (3-sulfopropyl) ammonium hydroxide intramolecular salt and dococildimethyl (3-sulfopropyl) ammonium hydroxide intramolecular salt.

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

Examples of the nonionic surfactant include higher alcohol ethylene oxide adducts and the like.

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-tetra Examples thereof include decyl alcohol, 1-pentadecyl alcohol, 1-hexadecyl alcohol, 1-heptadecyl alcohol, 1-octadecyl alcohol and the like.

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.

The surfactant (B) is preferably an anionic surfactant, a cationic surfactant or a nonionic surfactant, and more preferably a nonionic surfactant or a cationic surfactant from the viewpoint of odor discrimination performance. There are agents, most preferably cationic surfactants.

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

The diene polymer (A) and the surfactant (B) may or may not be compatible with each other.

<Conductive carbon material (C)>
In the present specification, the conductive carbon material (C) is 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 the mixture of the diene polymer (A) and the surfactant (B). The resin composition has conductivity because the conductive carbon materials (C) come into contact with each other to form a conductive path.

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

Commercially available carbon black products include Ketjen Black EC (trade name manufactured by Akzo, Netherlands), Ketjen Black EC-300J (trade name manufactured by Lion Specialty Chemicals Co., Ltd.), and Ketjen Black EC-600JD (Lion Specialty Chemicals). (Product name manufactured by Nittetsu Chemical Co., Ltd.), Seest G116, 116 (Product name manufactured by Tokai Carbon Co., Ltd.), Niteron # 10 (Product name manufactured by Nippon Steel Chemical Co., Ltd.), Denka Black (Product name manufactured by Denki Kagaku Kogyo Co., Ltd.) And SUPER C-65 (trade name of MTI Corporation, USA) and the like.

Commercially available products of carbon nanotubes include VGCF-H (trade name manufactured by Showa Denko KK) and the like.

As a commercial product of graphene, there is a product manufactured by Sigma-Aldrich.

The shape of the conductive carbon material (C) is preferably fibrous or spherical.

When fibrous, the fiber diameter is preferably 0.1 to 10 μm, more preferably 0.1 to 5 μm. The fiber length is preferably 0.1 to 10 μm, more preferably 1 to 10 μm.

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

The content of the conductive carbon material (C) is the above-mentioned diene polymer (A), ionic surfactant (B) and from the viewpoint of maintaining the correct answer rate by keeping the measurement time and measurement error small. It 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 conductive carbon material (C). Alternatively, from the viewpoint of the acceptance sensitivity of the odorous substance, the content of the conductive carbon material (C) is 100% by weight in total of the diene polymer (A), the surfactant (B) and the conductive carbon material (C). On the other hand, it may be 5 to 30% by weight, 5 to 20% by weight, or 5 to 10% by weight.

<Manufacturing method of resin composition>
A specific example of the method for producing a resin composition according to an embodiment of the present invention is as follows.

The resin composition is obtained as a slurry by mixing the diene polymer (A), the surfactant (B), the conductive carbon material (C) and the solvent (D) and kneading them uniformly with a stirrer. .. The odorant receiving layer is a dry substance obtained by applying this to the gaps between two sets of metal wiring and then heating and drying it.

The solvent (D) is not particularly limited as long as it can be removed by drying, but is preferably N-methylpyrrolidone, N, N-dimethylformamide, N, N-dimethylacetamide, ethylene acetate, water, toluene and xylene. And so on.

[2. Sensor element 31]
In the above-mentioned resin composition, the change in electrical conductivity with time differs depending on whether the odorant A is adsorbed on the resin composition or the odorant B different from the odorant A is adsorbed. By utilizing this property, it is possible to realize a sensor element 31 capable of detecting and identifying an odorous substance.

Hereinafter, the outline and the effect of the sensor element 31 to which the resin composition according to the embodiment of the present invention is applied will be described.

The sensor element 31 includes an odorant receiving layer 315 containing the above-mentioned resin composition, a first metal wiring 313A, and a second metal wiring 313B. In the following, when the first metal wiring 313A and the second metal wiring 313B are not distinguished, they may be referred to as metal wiring 313.

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

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 (that is, the resin composition). That is, the first metal wiring 313A and the second metal wiring 313B are separated from each other, and the odor substance receiving layer 315 is in contact with at least a part of the first metal wiring and at least a part 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 substantially parallel to each other as shown in FIG.

As shown in FIG. 2, the metal wiring 313 including the first metal wiring 313A and the second metal wiring 313B may be arranged on the substrate 311. The substrate 311 can 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 viewed from the direction perpendicular to the surface of the substrate is preferably 10 μm to 2 mm, more preferably 10 μm to 1 mm. The height, that is, the thickness of each of the first metal wiring 313A and the second metal wiring 313B viewed from the direction parallel to the surface of the substrate is preferably 1 μm to 100 μm, and more preferably 10 μm to 50 μm. The distance 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, more preferably 10 μm to 30 mm.

The metal wiring 313 may be arranged on the seal substrate 312. FIG. 3 shows a cross section taken along the line AA of FIG. As shown in FIG. 3, the sealed substrate 312 may be arranged on the substrate 311 such as glass epoxy, and the metal wiring 313 may be arranged on the sealed substrate 312. Vinyl tape 314 may be used to fix the seal substrate 312 on the substrate 311. The vinyl tape 314 can also be used to adjust the length of the exposed portion of the metal wiring 313 by masking the extra portion of the metal wiring 313. Here, the exposed portion of the metal wiring 313 is a portion where the metal wiring 313 and the odorant receiving layer 315 are in contact with each other. The vinyl tape 314 can also be an insulator for adjusting the length of the portion where the metal wiring 313 and the odorant receiving layer 315 are in contact with each other.

The odorant receiving layer 315 may be in contact with at least a part of the first metal wiring 313A and at least a part of the second metal wiring 313B. The odorant receiving layer 315 may be arranged so as to fill the region sandwiched between the first metal wiring 313A and the second metal wiring 313B, for example, as shown in FIGS. 2 and 3.

When the electrical conductivity of the odorant receiving layer 315 (that is, the electrical conductivity of the sensor element 31) is low, the distance between the first metal wiring 313A and the second metal wiring 313B is a predetermined distance (for example, 500 μm) or less. Is desirable.

The sensor element 31 varies depending on the application of a resin composition in which the change in electrical conductivity with time differs depending on whether the odor substance A is adsorbed or the odor substance B different from the odor substance A is adsorbed. It is possible to detect and identify odorous substances.

[3. Smell sensor 30]
Hereinafter, the outline and effect of the odor sensor 30 to which the sensor element 31 is applied will be described with reference to FIG. FIG. 1 is a block diagram showing an example of the configuration of an odor measuring device 100 including an odor sensor 30 to which a sensor element 31 is applied. In the sensor element 31 shown in FIG. 1, the vinyl tape 314 is not shown for the sake of simplicity.

The odor sensor 30 includes a sensor element 31 for detecting an odor substance, a constant current source 32 (power supply), and a voltmeter 33 (measuring device).

The first metal wiring 313A and the second metal wiring 313B of the sensor element 31 are connected by a lead wire W. FIG. 1 shows an example in which a constant current source 32 and a voltmeter 33 are arranged on a lead wire W.

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.

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

Although the odor sensor 30 is not an essential configuration, it may further include a housing 34. The housing 34 is a container that can contain air containing an odorant. When the housing 34 is provided, the sensor element 31 is installed in the housing 34.

The housing 34 includes an introduction port 341 for introducing an odorous substance and an discharge port 342 for discharging air containing the odorous substance. The odorous substance may be introduced by inserting a filter paper P or the like in which the odorous substance is immersed from the introduction port 341 into the housing 34, or air containing the odorous substance may be introduced into the housing 34 from the introduction port 341. It may be done by inserting. The housing 34 is a container for containing air containing an odorant having a predetermined concentration (for example, 200 ppm) or more.

Although not essential, an airflow generation fan 35 may be arranged at the discharge port 342 of the housing 34. The airflow generation fan 35 is for generating an airflow inside the housing 34 and for discharging the gas inside the housing 34 from the discharge port 342 to the outside of the housing 34.

The odor sensor 30 may be provided with a constant voltage source (power supply) (not shown) as an alternative to the constant current source 32, and an ammeter (measuring device) (not shown) as an alternative to the voltmeter 33. In this case, the constant voltage source functions as a power source for supplying power to the sensor element 31, and applies a constant voltage to the sensor element 31 via a lead wire. On the other hand, the ammeter measures the current value 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.

The odor sensor 30 outputs a measured value indicating a change over time in the electrical conductivity of the sensor element 31 before and after the odor substance is adsorbed on the sensor element 31. This makes it possible to detect and identify various odorous substances.

[4. Odor measuring device 100]
When various odorous substances are adsorbed on the sensor element 31, the odor sensor 30 described above can output a change over time in the electrical conductivity of the sensor element 31 for each odorous substance. When this odor sensor 30 is applied, the change over time in the electrical conductivity of the sensor element 31 when the odor substance A is adsorbed on the sensor element 31, and the sensor when the odor substance B is adsorbed on the sensor element 31. It can be compared with the change over time in the electrical conductivity of the element 31. Based on such a comparison result, it is possible to realize an odor measuring device 100 capable of estimating an odor substance adsorbed on the sensor element 31.

Further, the odor measuring device 100 can estimate the odor substance with high accuracy by using the estimation model 22 generated by machine learning. The estimation model 22 includes a combination of a measured value measured when each of a plurality of odorous substances is adsorbed on at least one sensor element 31 and identification information unique to the odorous substance given the measured value. Can be generated using the data for.

Hereinafter, the outline and the effect of the odor measuring device 100 to which the odor sensor 30 is applied will be described. The odor measuring device 100 is a device that estimates the odorous substance adsorbed on the sensor element 31 from the change in electrical conductivity generated in the sensor element 31 to which the above-mentioned resin composition is applied.

First, the configuration of the odor measuring device 100 according to the embodiment of the present invention will be described with reference to FIG. FIG. 1 is a block diagram showing an example of the configuration of the odor measuring device 100.

As shown in FIG. 1, the odor measuring device 100 includes an estimation device 10 and an odor sensor 30.

(Estimator 10)
The estimation device 10 is a device that estimates an odorous substance detected by the 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 communicably connected to the odor sensor 30. Specifically, the estimation device 10 executes estimation of the odor substance by analyzing the measured value acquired from the odor sensor 30. The configuration of the estimation device 10 will be described later.

<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 for estimating the odor substance, and the process of generating the estimation model 22 will be described with reference to FIGS. 4 and 5.

The estimation model 22 is a combination of the measured value measured by the voltmeter 33 when each of the plurality of odorous substances is adsorbed on at least one sensor element, and the identification information unique to the odorous substance giving the measured value. It is generated by machine learning using training data including. Here, the identification information unique to the odorant may be, for example, the name of the odorant, the CAS number, the chemical formula, or the like.

(Configuration of 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 convenience of explanation, the same reference numerals are given to the members having the same functions as the members described with reference to FIG. 1, and the description thereof will not be repeated.

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

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

The control unit 1 includes a measured value acquisition unit 11 (acquisition unit), a change pattern analysis unit 12 (analysis unit), a learning control unit 13, and an estimation model generation unit 14.

The measured value acquisition unit 11 acquires the measured value from the voltmeter 33. Further, the measured value acquisition unit 11 calculates a value (for example, a resistance value, an impedance, etc.) indicating the electrical conductivity of the sensor element 31 by using the acquired measured value. The measured value acquisition unit 11 may acquire measured values from the voltmeter 33 at predetermined time intervals (for example, 0.1 second intervals).

The change pattern analysis unit 12 analyzes changes in the electrical conductivity of at least one sensor element 31 over time. The change pattern analysis unit 12 uses the resistance value calculated by the measurement value acquisition unit 11 to calculate a value indicating the amount of change in the electrical conductivity of the sensor element 31 due to the adsorption of the odorous substance. The change pattern analysis unit 12 generates data showing a change pattern showing a time change of the calculated change amount of electrical conductivity. When the generated change pattern is a known odor substance, the change pattern analysis unit 12 associates the generated change pattern with the identification information peculiar to the known odor substance and adds it to the change pattern database 21 (learning data). May be stored.

The learning control unit 13 reads 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 includes a combination of a measured value measured when a plurality of odorous substances are adsorbed on the sensor element 31 and identification information unique to the known odorous substance to which the measured value is given. It is a database. The learning control unit 13 inputs the change pattern read from the change pattern database 21 to the estimation model generation unit 14. Further, the learning control unit 13 compares the identification information of the odorant 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 corrects according to the comparison result. The instruction is output to the estimation model generation unit 14.

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

(Process to generate estimation model 22)
Hereinafter, specific processing performed by each unit of the control unit 1 will be described with reference to FIG. FIG. 5 is a flowchart showing an example of the flow of processing in which the estimation device 10 generates the estimation model 22.

First, the measured value acquisition unit 11 acquires the voltage value V0 measured by the odor sensor 30 before inserting the filter paper P in which the odorous substance is immersed 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.

On the other hand, the input unit 15 receives an input such as the name of a known odorous substance immersed in the filter paper P inserted in the housing 34 (step S12). The process of step S12 may be performed before step S11.

Next, the measured value acquisition unit 11 acquires the voltage value V measured by the odor sensor 30 immediately after inserting the filter paper P in which the known odorous substance is immersed into the housing 34, and calculates the resistance value R. (Step S13).

Subsequently, 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 the electrical conductivity of the sensor element 31 due to the adsorption of a known odorous substance. 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 input name of the known odorant (step S15).

If the change pattern is not stored for the existing odorant of a predetermined type (NO in step S16), that is, if the data used for machine learning is still insufficient, the process returns to step S11.

When the change pattern is stored for the existing odorant of a predetermined type (YES in step S16), the learning control unit 13 reads out the change pattern for the known odorant stored in the change pattern database 21. , Is input 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).

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

In the examples shown in FIGS. 4 and 5, the estimation device 10 generates the estimation model 22, but is not limited to this. For example, an external computer different from the estimation device 10 and having the same functions as the learning control unit 13 and the estimation model generation unit 14 is provided with the same data as the change pattern database 21 to create the estimation model 22. You may.

<Estimation of odorous substances>
Next, the configuration of the odor measuring device 100a for estimating the odor substance using the estimation model 22 and the estimation process will be described with reference to FIGS. 6 and 7.

(Configuration of estimation device 10a (execution of estimation processing))
FIG. 6 is a functional block diagram showing an example of the configuration of the odor measuring device 100a. For convenience of explanation, the same reference numerals are added to the members having the same functions as the members described with reference to FIGS. 1 and 4, and the description thereof will not be repeated.

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 when the estimation device 10 shown in FIG. 4 is used for the estimation processing of the odorous substance. 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.

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.

The control unit 1a includes a measured value acquisition unit 11 (acquisition unit), a change pattern analysis unit 12 (analysis unit), an estimation unit 16, and an output control unit 17.

The estimation unit 16 estimates the odorant substance from the analysis result obtained by analyzing the measured value acquired from the odor sensor 30 by using the estimation model 22.

The output control unit 17 controls the output unit 18 so as to output the estimation result.

(Estimation processing)
Hereinafter, specific processing performed by each unit of the control unit 1a will be described with reference to FIG. 7. FIG. 7 is a flowchart showing an example of a flow of processing in which the estimation device 10a estimates an odorous substance.

First, the measured value acquisition unit 11 acquires the voltage value V0 measured by the odor sensor 30 before inserting the filter paper P in which the odorous substance is immersed into the housing 34, and calculates the resistance value R0 (step S1). ..

Next, the measured value acquisition unit 11 acquires the voltage value V measured by the odor sensor 30 immediately after inserting the filter paper P in which the unknown (that is, the estimation target) odor substance is immersed into the housing 34. Then, the resistance value R is calculated (step S2).

Subsequently, the change pattern analysis unit 12 calculates R / R0 using the resistance value R0 and the resistance value R (step S3).

Next, the estimation unit 16 estimates an unknown odorant from the time-dependent change pattern of R / R0 based on the estimation model 22 (step S4).

The output control unit 17 controls the output unit and outputs an estimation result (step S5).

<Embodiment 2>
In the above-described 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 for the odor substance receiving layer 315 are different from each other. This will be described with reference to FIG. FIG. 8 is a block diagram showing an example of the configuration of the odor measuring device 100b according to another embodiment of the present invention. For convenience of explanation, the same reference numerals are given to the members having the same functions as the members described with reference to FIG. 1, and the description thereof will not be repeated.

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 is different between the odor substance receiving layer 315 of the sensor element 31 and the odor substance receiving layer 315b of the sensor element 31b. You may.

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

By providing a plurality of sensor elements using resin compositions having different characteristics for adsorbing odorous substances in the odorant receiving layer, the odor measuring device 100b can simultaneously perform estimation of a plurality of odorous substances. In addition to the sensor element according to the embodiment of the present invention, a sensor element that does not contain the surfactant (B) in the odorant receiving layer may be used in combination.

Further, if the odor measuring device 100b is used, the first change pattern showing the change in the electric conductivity of the sensor element 31 and the second change pattern showing the change in the electric conductivity of the sensor element 31b are used for each of the known odor substances. And can be obtained. The estimation model 22 may be generated by machine learning using both the first change pattern and the second change pattern. Since the odor measuring device 100b estimates the odorous substance using the estimation model 22 generated in this way, it is possible to discriminate each odorous substance more accurately.

<Example of realization by software>
The control blocks (particularly the control unit 1) of the estimation devices 10, 10a and 10b may be realized by a logic circuit (hardware) formed in an integrated circuit (IC chip) or the like, or may be realized by software. ..

In the latter case, the estimation devices 10, 10a, and 10b include a computer that executes a program instruction, which is software that realizes each function. The computer comprises, for example, one or more processors and a computer-readable recording medium that stores the program. Then, in the computer, the processor reads the program from the recording medium and executes the program, thereby achieving the object of the present invention. As the processor, for example, a CPU (Central Processing Unit) can be used. As the recording medium, a "non-temporary tangible medium", for example, a ROM (Read Only Memory) or the like, a tape, a disk, a card, a semiconductor memory, a programmable logic circuit, or the like can be used. Further, a RAM (Random Access Memory) or the like for expanding the program may be further provided. Further, the program may be supplied to the computer via any transmission medium (communication network, broadcast wave, etc.) capable of transmitting the program. It should be noted that one aspect of the present invention can also be realized in the form of a data signal embedded in a carrier wave, in which the program is embodied by electronic transmission.

The present invention is not limited to the above-described embodiments, and various modifications can be made within the scope of the claims, and the embodiments obtained by appropriately combining the technical means disclosed in the different embodiments. Is also included in the technical scope of the present invention.

Hereinafter, the present invention will be further described with reference to Examples and Comparative Examples, but the present invention is not limited thereto. Hereinafter, unless otherwise specified,% indicates weight% and parts indicate parts by weight.

[Manufacturing Examples 1 to 9]
<Preparation of Diene Polymer (A) 1> (Production Examples 1, 2, 4, 6 and 7)
A heating / cooling device, a thermometer, a temperature controller, a polymerization catalyst introduction tube and a nitrogen introduction tube were attached to a reaction vessel equipped with a stirring blade, a stirring device, a nitrogen inlet and an outlet. The reaction vessel has a seal structure capable of depressurizing the inside of the reaction vessel. After adding 200 parts of the dehydrated cyclohexane as the reaction solvent and the dehydrated monomer in the amount shown in Table 1 to the reaction vessel, nitrogen substitution (gas phase oxygen concentration: 100 ppm or less). Was done. After that, bubbling was performed using a nitrogen introduction tube. Subsequently, the reaction vessel was heated to 60 ° C. with stirring in a closed state to polymerize the amount of sodium naphthalene, n-butyllithium or sec-butyllithium as a polymerization catalyst shown in Table 1. It was dropped from the catalyst introduction tube, and the polymerization reaction was carried out at 60 ° C. for 5 hours. Then, 0.1 part of methanol was added to the reaction vessel to stop the reaction, and then steam stripping was performed to recover the reaction solvent and the unreacted monomer to obtain a crumb-shaped hot water slurry. The product was obtained. Finally, the obtained product was dried to obtain a diene polymer (A). The number average molecular weight of the obtained copolymer was measured by gel permeation chromatography (GPC) under the following conditions, and the results are shown in Table 1. Hereinafter, the above-mentioned method will be referred to as “manufacturing method 1”.

Equipment: HLC-8120 manufactured by Tosoh Corporation
Column: 2 TSK GEL GMH6 [manufactured by Tosoh Corporation]
Measurement temperature: 40 ° C
Sample solution: 0.25 wt% THF (tetrahydrofuran) solution Injection amount: 100 μl
Detection device: Refractive index detector

<Preparation of Diene Polymer (A) 2> (Production Examples 3, 5, 8 and 9)
A heating / cooling device, a thermometer, a temperature controller, a polymerization catalyst introduction tube and a nitrogen introduction tube were attached to a reaction vessel equipped with a stirring blade, a stirring device, a nitrogen inlet and an outlet. The reaction vessel has a seal structure capable of depressurizing the inside of the reaction vessel. 180 parts of ion-exchanged water, 4 parts of sodium oleate as an emulsifier, and potassium persulfate as a polymerization catalyst in the amounts shown in Table 1 were introduced into the reaction vessel from the polymerization catalyst introduction tube, and the amounts shown in Table 1 were introduced. After adding the monomer, nitrogen substitution (gas phase oxygen concentration: 100 ppm or less) was performed. After that, bubbling was performed using a nitrogen introduction tube. Subsequently, the reaction vessel was heated to 40 ° C. with stirring in a closed state, and the polymerization reaction was carried out at 40 ° C. over 10 hours. Then, the reaction vessel was cooled to stop the reaction, and then purified by the reprecipitation method to obtain a diene polymer (A). The number average molecular weight of the obtained copolymer was measured by gel permeation chromatography (GPC) under the following conditions, and the results are shown in Table 1. Hereinafter, the above-mentioned method will be referred to as “manufacturing method 2”.

Equipment: HLC-8120 manufactured by Tosoh Corporation
Column: 2 TSK GEL GMH6 [manufactured by Tosoh Corporation]
Measurement temperature: 40 ° C
Sample solution: 0.25 wt% THF (tetrahydrofuran) solution Injection amount: 100 μl
Detection device: Refractive index detector

<Preparation of higher alcohol ethylene oxide adduct> (Production Examples 10 to 23)
A reaction vessel equipped with a stirring blade, a stirring device, a nitrogen inlet, an outlet and an ethylene oxide inlet having a sealing structure capable of depressurizing or pressurizing the inside of the reaction vessel, a heating / cooling device, a thermometer, a pressure gauge, and temperature control. The device and nitrogen inlet tube were installed. The reaction vessel was charged with 30 parts of the higher alcohol shown in Table 2 and 3 parts of potassium hydroxide as a catalyst, which had been dried with 30 parts of Molecular Sieves 3A for 2 hours under a nitrogen atmosphere, and replaced with nitrogen under reduced pressure. The temperature inside the reaction vessel was raised to 160 ° C., and the weight of ethylene oxide shown in Table 2 was dropped and reacted while adjusting the flow rate so that the pressure inside the reaction vessel became 0.5 MPa (G). After the completion of the dropping, 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).

<Preparation of Slurry> (Examples 1 to 152, Comparative Examples 1 to 9)
The diene polymer (A), the surfactant (B), the conductive carbon material (C) and the ethyl acetate as the solvent (D) are weighed in a polypropylene container in the amounts shown in Tables 3 to 12 to obtain a mixture. rice field. The mixture was stirred at 2000 rpm for 60 minutes using a rotation / revolution mixer (ARE-310 manufactured by Shinky Co., Ltd.) to obtain a slurry. The slurry was used as a resin composition for forming an odorant receiving layer. In Tables 3 to 12 below, the "(A) / (B) ratio" means the weight ratio of the diene polymer (A) and the surfactant (B).

As the surfactants (B) other than the higher alcohol ethylene oxide adducts shown in Tables 3 to 12, those commercially available from Tokyo Chemical Industry Co., Ltd. were used. As the higher alcohol ethylene oxide adduct, (NS-1) to (NS-13) prepared by the above method were used.

The conductive carbon materials (C) shown in Tables 3 to 12 are SUPER-C65 (carbon black) manufactured by MTI Corporation, VGCF-H (carbon nanotubes) manufactured by Showa Denko Co., Ltd., and Denka Black (carbon black). ) Was manufactured by Denka Co., Ltd., and Ketjen Black EC-300J (carbon black) and Ketjen Black EC-600JD (carbon black) were manufactured by Lion Specialty Chemical Co., Ltd., respectively.

[Examples 1 to 152, Comparative Examples 1 to 9]
<Manufacturing of sensor element>
A seal substrate containing a set of two metal wirings was cut out from a seal substrate (ICB-073, manufactured by Sanhayato Co., Ltd.) having a plurality of metal wirings having a gap width of 500 μm. The cut-out seal substrate was further cut so that the length of the metal wiring was 3.5 cm.

The cut seal substrate was attached on a glass plate with double-sided tape so that the metal wiring was on top. Further, a vinyl tape was attached to the excess portion of the metal wiring and masked so that the length of the exposed portion of the metal wiring was 3.0 cm. Then, each slurry prepared by the above method was applied to the exposed portion of the metal wiring using a bar coater (No. 4). After the coating, it was dried for 3 hours in a smooth air dryer heated to 100 ° C. After drying, the metal wiring provided with the odor substance receiving layer is peeled off from the glass plate after cooling to room temperature, and the sensor elements (E-1) to (E-152) and the comparative sensor element (E'-1) are removed. )-(E'-9) was obtained.

[Examples 153 to 294, Comparative Examples 10 to 18]
<Evaluation of resin composition and sensor element>
The evaluation of the resin composition can be performed by comparing the data obtained from the sensor element (E) and the comparison sensor element (E').

<Measurement method>
We made a housing equipped with an inlet for introducing the sample (odorous substance) and a fan for creating an airflow so that the sample spreads evenly. Of the sensor elements (E-1) to (E-142) to which the lead wire for taking out the terminal is soldered and the comparison sensor elements (E'-1) to (E'-9), the evaluation target The sensor element was installed in the housing.

A 1 mA constant current power supply and a voltmeter for measuring the voltage applied to both terminals of the lead wire were attached to the end of the lead wire taken out of the housing, and the measured value of the voltmeter was recorded by a computer.

The sample was immersed in a filter paper so that the concentration of the sample in the housing was 200 ppm, and the filter paper was inserted through the introduction port. Measurement was started immediately after inserting the filter paper. Sixty seconds after the start of the measurement, the fan was rotated again for 60 seconds, and the measurement was performed while discharging the steam in the housing to the outside. The voltage was measured at 0.1 second intervals. Moreover, the measurement was repeated 100 times under the same conditions.

Hexane, ethyl acetate, methanol, diethyl carbonate, or toluene was used as the sample.

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

The responsiveness of the sensor element to each sample was analyzed using the k-nearest neighbor method using the time variation of R / R0 at 0.1 second intervals. The above measurements were made for the sensor elements (E-1) to (E-142) or the comparative sensor elements (E'-1) to (E'-9), and 100 corresponding to each Example and each Comparative Example. The measurement data for 5 times x 5 samples = 500 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) by the k-nearest neighbor method was created for the training data. The correct answer rate when the test data is classified for the classifiers of each example and the comparative example is used as a performance index of the sensor element, and it can be judged that the higher the correct answer rate is, the higher the performance as the sensor element is. For the creation of the classifier and the calculation of the correct answer rate in each embodiment, the data obtained by the corresponding sensor element (E) and the comparative sensor element using the same diene polymer (A) as the sensor element (E) are used. Both of the data obtained by (E') were used.

<Evaluation result>
The evaluation results are shown below.

The correct answer rate calculated in the performance evaluation is calculated for each of the odor sensor using the sensor element manufactured in Examples 1 to 142 and the odor sensor using the sensor element manufactured in Comparative Examples 1 to 9. Compare.

For example, the correct answer rate of the odor sensor of Example 108 using the sensor element (E-1) is 91%, while the comparative sensor using the same diene polymer as the sensor element (E-1). The correct answer rate of the odor sensor of Comparative Example 17 using the element (E'-8) was 63%.

As shown in Tables 13 to 17, the correct answer rates of the odor sensors of Examples 153 to 294 using the sensor elements (E-1) to (E-142) are the comparative sensor elements (E'-1) to. It was higher than the correct answer rate of the odor sensors of Comparative Examples 10 to 18 using (E'-9).

It can be said that the resin composition according to the embodiment of the present invention and the odor sensor using the sensor element using the resin composition have good odor discrimination performance.

Further, as the content of the conductive carbon material (C) increased, the value of Rmax / R0 tended to decrease. The smaller the value of Rmax / R0, the larger the measurement error. Therefore, the larger the value of Rmax / R0, the better.

[Examples 295 to 304]
<Evaluation of the content of the 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 sensor element (E-143) to (E-152) by the same method as described above. After the measurement was completed, R / R0 1 second after the start of the measurement was extracted and shown in Table 18. Further, the maximum value of R during measurement is Rmax, and the value of Rmax / R0 (%) is also shown in Table 18.

From Table 18, as the amount of the conductive carbon material (C) increases, specifically, when the content is 25% by weight or more, the value of R / R0 1 second after the start of measurement increases, and the conductive carbon material (C) increases. When the content is in the range of 30% by weight or more, the value of R / R0 1 second after the start of measurement is particularly significantly increased. The larger the R / R0 1 second after the start of measurement, the shorter the measurement time and the reduction in power consumption. Therefore, the content of the conductive carbon material (C) is preferably 25% by weight or more, preferably 30% by weight. It can be said that the above is particularly preferable. When the content of the conductive carbon material (C) was 75% by weight or less, the adhesion of the conductive carbon material (C) to the sealing substrate was better, and it was more suitable for the sensor element (E).

On the other hand, as shown in Table 18, as the content of the conductive carbon material (C) increases, the value of Rmax / R0 decreases and the measurement error increases. However, in the range of the conductive carbon material (C) content of 75% by weight or less in Examples 295 to 304, the value of Rmax / R0 does not decrease significantly, and the measurement error is kept small. it is conceivable that. Therefore, from the viewpoint of maintaining the correct answer rate by keeping the measurement time and the 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. %, Most preferably 35 to 55% by weight.

[Example 305, Comparative Example 19]
<Evaluation of odor sensor and odor measuring device>
For the evaluation of the odor sensor and the odor measuring device, the correct answer rate and the comparison sensor element (E'-) when odors are identified using the sensor elements (E-1) to (E-142) incorporated in the following system. It can be done by comparing with the correct answer rate when the odor is identified using 1) to (E'-9).

<Measurement method>
We made a housing equipped with an inlet for introducing the sample (smell) and a fan for creating an air flow so that the sample spreads evenly. An odor sensor (F) in which sensor elements (E-1) to (E-142) soldered with lead wires for taking out terminals to the outside are housed in a housing, and a comparison sensor element (E'-1) to. A comparative odor sensor (F') in which (E'-9) was stored in the housing was used.

A 1 mA constant current power supply and a voltmeter for measuring the voltage applied to both terminals of the lead wire were attached to the end of the lead wire taken out of the housing, and the measured value of the voltmeter was recorded by a computer.

The sample was immersed in a filter paper so that the concentration of the sample in the housing was 200 ppm, and the filter paper was inserted through the introduction port. Measurement was started immediately after inserting the filter paper. Sixty seconds after the start of the measurement, the fan was rotated again for 60 seconds, and the measurement was performed while discharging the steam in the housing to the outside. The voltage was measured at 0.1 second intervals. Moreover, the measurement was repeated 100 times under the same conditions.

As the sample, d-limonene, bornane-2-one, cis-3-hexenol, β-phenylethyl alcohol, citral, L-carboxylic, γ-undecalactone, eugenol, and linalyl acetate were used. All the samples used were manufactured by Tokyo Chemical Industry Co., Ltd.

<Evaluation method>
The electric resistance R was calculated from Ohm's law using the voltage measured at each time and the current value of 1 mA supplied from the constant current power source. 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 13-17. The maximum value of R during measurement is Rmax, and the value of Rmax / R0 (%) is also shown in Tables 13 to 17.

The responsiveness of the sensor element to each sample was analyzed using the k-nearest neighbor method using the time variation of R / R0 at 0.1 second intervals. The above measurements were made for the odor sensor (F) and the comparative odor sensor (F'), and measurement data for 100 times x 9 samples = 900 times in total was learned for each sensor. Number of data: Number of test data = 80:20 A classifier (learning model) by the k-neighborhood method was created for the training data by randomly dividing the data so as to be. The correct answer rate when the test data is classified for the classifiers of each example and the comparative example is used as the performance index of the odor sensor, and it can be judged that the higher the correct answer rate is, the higher the performance as the sensor is.

<Evaluation result>
As a result of the above-mentioned measurement, the correct answer rate of the odor sensor (F) was 90%, and the correct answer rate of the comparative odor sensor (F') was 55%. It can be said that the odor sensor using the sensor element has good odor discrimination performance.

[Examples 306 to 447, Comparative Examples 20 to 28]
<Evaluation when using a sample that is a mixture>
<Method of preparing a sample as a mixture>
In addition to the single sample, a sample as a mixture was prepared by the following method. Menthol, benzaldehyde, ethyl acetate, vanillin, hexanal, ethanol, pentyl pentanoate, linalol, and 2-propanol were prepared in a desiccator so as to be a gas of 200 ppm each, and used as a raw material for a mixture. Next, a 500 mL two-necked eggplant flask equipped with a three-way cock and a septum rubber was sealed after vacuum depressurization. The following volumes were collected from each mixture raw material with a syringe and injected from the rubber septum of a sealed two-necked eggplant flask.
Mixture sample 1:
Menthol (150 mL)
Benzaldehyde (150 mL)
Ethyl acetate (150 mL)
Mixture sample 2:
Vanillin (150 mL)
Hexanal (150 mL)
Ethanol (150 mL)
Mixture sample 3:
Pentyl pentanoate (150 mL)
Linalool (150 mL)
2-propanol (150 mL)

<Measurement method including a sample that is a mixture>
Evaluation among the sensor elements (E-1) to (E-142) and the comparative sensor elements (E'-1) to (E'-9) in the same manner as in Examples 153 to 294 and Comparative Examples 10 to 18. The target sensor element was installed in the housing. A constant current power supply, a voltmeter, and a computer were arranged in the same manner.

20 mL of the mixture sample prepared by the above method was collected with a syringe, and the mixture sample was injected through the inlet. Measurements were started immediately after injection of the mixture sample. Sixty seconds after the start of the measurement, the fan was rotated again for 60 seconds, and the measurement was performed while discharging the steam in the housing to the outside. The voltage was measured at 0.1 second intervals. Moreover, the measurement was repeated 100 times under the same conditions.

<Evaluation method when using a sample that is a mixture>
Using the data obtained by the above method and the data obtained by the measurement performed on the simple substance sample (Examples 153 to 294, Comparative Examples 10 to 18), the simple substance sample is used. Evaluation was performed by the same method as the evaluation method used, and the correct answer rate was calculated. That is, the evaluation was performed by the following method.

The electric resistance R was calculated from Ohm's law using the voltage measured at each time and the current value of 1 mA supplied from the constant current power source. The resistance R0 before the introduction of the sample was measured in advance, and R / R0 was calculated.

The responsiveness of the sensor element to each sample was analyzed using the k-nearest neighbor method using the time variation of R / R0 at 0.1 second intervals. The above measurements were made for the sensor elements (E-1) to (E-142) or the comparative sensor elements (E'-1) to (E'-9), and 100 corresponding to each Example and each Comparative Example. The measurement data for 8 times x 8 samples = 800 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) by the k-nearest neighbor method was created for the training data. The correct answer rate when the test data is classified for the classifiers of each example and the comparative example is used as a performance index of the sensor element, and it can be judged that the higher the correct answer rate is, the higher the performance as a sensor. For the creation of the classifier and the calculation of the correct answer rate in each embodiment, the data obtained by the corresponding sensor element (E) and the comparative sensor element using the same diene polymer (A) as the sensor element (E) are used. Both of the data obtained by (E') were used.

<Evaluation results when using a sample that is a mixture>
The evaluation results are shown below.

The correct answer rates calculated in the performance evaluation in each of the odor sensors described in Examples 306 to 447 and Comparative Examples 20 to 28 are compared.

For example, the correct answer rate of the odor sensor of Example 306 using the sensor element (E-1) is 74%, while the comparative sensor using the same diene polymer as the sensor element (E-1). The correct answer rate of the odor sensor of Comparative Example 27 using the element (E'-8) was 53%.

As shown in Tables 19 to 23, the correct answer rates of the odor sensors of Examples 306 to 447 using the sensor elements (E-1) to (E-142) are the comparative sensor elements (E'-1) to. It was higher than the correct answer rate of the odor sensors of Comparative Examples 20 to 28 using (E'-9).

It can be said that the resin composition according to the embodiment of the present invention and the odor sensor using the sensor element using the resin composition have good odor discrimination performance even when the sample is a mixture.

INDUSTRIAL APPLICABILITY The present invention is useful as an odor discrimination sensor for medical use, gas detection, agriculture, and other industrial and daily life. For example, a farmer can determine the maturity of a scented crop by using the scent identification sensor and manage the optimum harvest timing. It is also possible to convert the odor of a product such as food or cosmetics into data with an odor identification sensor to support improvement of product development efficiency and quality stabilization.

10, 10a, 10b Estimator 11 Measurement value acquisition unit (acquisition unit)
12 Change pattern analysis unit (analysis unit)
16 Estimator 30, 30b Smell sensor 31, 31b Sensor element 32, 32b Constant current source (power supply)
33, 33b Voltmeter (measuring equipment)
100, 100a, 100b Smell measuring device 313A First metal wiring 313B Second metal wiring 315, 315b Odor substance receiving layer

Claims (9)

A resin composition for forming an odorant receiving layer.
A resin composition containing a diene polymer (A), a surfactant (B) and a conductive carbon material (C). The resin composition according to claim 1, wherein the surfactant (B) has an HLB value of 8 to 18. The resin composition according to claim 1 or 2, wherein the weight ratio [(A) / (B)] of the diene polymer (A) to the surfactant (B) is 1.0 to 4.0. .. The content of the conductive carbon material (C) is 25 to 75% by weight based on 100% by weight of the total of the diene polymer (A), the surfactant (B) and the conductive carbon material (C). The resin composition according to any one of claims 1 to 3. A sensor element comprising the odorant receiving layer containing the resin composition according to any one of claims 1 to 4, a first metal wiring, and a second metal wiring.
The first metal wiring and the second metal wiring are separated from each other.
The sensor element in which the odorant receiving layer is in contact with at least a part of the first metal wiring and at least a part of the second metal wiring. The at least one sensor element according to claim 5 and
A power supply for supplying power to the sensor element and
An odor sensor comprising a measuring device that outputs a measured value indicating the electrical conductivity of the odor substance receiving layer of the sensor element supplied from the power source. The odor sensor according to claim 6, wherein the power supply supplies a constant current or applies a constant voltage to the at least one sensor element. An odor measuring device including the odor sensor according to claim 6 or 7, and an odor measuring device.
The estimation device is
An acquisition unit that acquires the measured value from the measuring device,
An analysis unit that analyzes changes in electrical conductivity of at least one sensor element over time, and
Equipped with an estimation unit that estimates odorous substances based on an estimation model,
In the estimation model, the measured value measured by the measuring device when each of the plurality of odorous substances is adsorbed on the at least one sensor element, and the identification information unique to the odorous substance giving the measured value. An odor measuring device generated by machine learning using learning data including combinations. The control program for operating a computer as the odor measuring device according to claim 8, wherein the computer functions as the acquisition unit, the analysis unit, and the estimation unit.

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