# Pattern Recognition Using Chemical Sensor for Identification of Solid Materials by Responses to Multiple Probe Gases

https://mdr.nims.go.jp/datasets/19c67826-cbb0-4913-851e-cb7a9eb17cbb

## File

- [LSEN_Minami_MS_rev.pdf](https://mdr.nims.go.jp/filesets/e88cb1cc-b8a1-41f4-b12a-4012a888e87d/download) ([Detail](https://mdr.nims.go.jp/filesets/e88cb1cc-b8a1-41f4-b12a-4012a888e87d.md))

## Id

19c67826-cbb0-4913-851e-cb7a9eb17cbb

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2023-11-30T05:13:18.776977Z

## Updated at

2025-10-21T07:16:25.655577Z

## Published at

2025-10-21T07:15:46.220339Z

## Doi

https://doi.org/10.48505/nims.4868

## First published url

https://doi.org/10.1109/LSENS.2023.3300802

## Date published

2023-08-01

## Recorded date published



## Resource type

journal_article

## Manuscript type

accepted_manuscript

## Collection



## Title

- title: Pattern Recognition Using Chemical Sensor for Identification of Solid Materials
    by Responses to Multiple Probe Gases
  title_type: original
  lang: en

## Description

- description: A conventional approach to the analysis of solid materials generally
    focuses on their physical and chemical properties, and hence requires a corresponding
    analysis method. Recently, we have developed a novel sensing approach for materials
    analysis based on pattern recognition using chemical sensor arrays. Since sensing
    responses of a solid receptor material to gaseous molecules are unique to the
    combination of the solid materials and the gaseous molecules, solid materials
    can be identified by analyzing their response patterns to known “probe gases”.
    Here, we demonstrated the identification of solid materials with their chemical
    or physical properties using this approach. Using a nanomechanical sensor as a
    sensing platform, we succeeded in simultaneously identifying differences between
    organic polymers and inorganic nanoparticles and their respective hydrophilicity.
    Moreover, we even identified the differences of polymer blends, which contain
    different amounts of plasticizers. Any kinds of gaseous and volatile molecules
    can be utilized as a probe gas, and hence, the number of response patterns can
    be tremendously increased by simply increasing the number of probe gases. Combined
    with a machine learning-based pattern recognition model, the present approach
    can be applied to a wide range of solid material analyses with high accuracy.
    This approach is expected to have potential applications in various fields such
    as materials science, chemistry, food, and environment.
  description_type: abstract
  lang: eng

## Creator

- name: Kosuke Minami
  role: author
  orcid: https://orcid.org/0000-0003-4145-1118
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26
- name: Kota Shiba
  role: author
  orcid: https://orcid.org/0000-0001-7775-0318
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26
- name: Genki Yoshikawa
  role: author
  orcid: https://orcid.org/0000-0002-9136-8964
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26

## Contact agent

- name: 南皓輔
  orcid: https://orcid.org/
  ror: https://ror.org/

## Publisher

organization: Institute of Electrical and Electronics Engineers (IEEE)

## Managing organization



## Keyword

- subject: Chemical and biological sensors
  schema: not_defined
- subject: solid sensing
  schema: not_defined
- subject: chemical sensors
  schema: not_defined
- subject: membrane-type surface stress sensor (MSS)
  schema: not_defined
- subject: nanomechanical sensors
  schema: not_defined
- subject: pattern recognition
  schema: not_defined

## Rights

- description: "© 2023 IEEE. Personal use of this material is permitted.  Permission
    from IEEE must be obtained for all other uses, in any current or future media,
    including reprinting/republishing this material for advertising or promotional
    purposes, creating new collective works, for resale or redistribution to servers
    or lists, or reuse of any copyrighted component of this work in other works."
  identifier: http://rightsstatements.org/vocab/InC/1.0/

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo

start_date: 2023-08-01
end_date: 2025-08-01

## Journal

- title: IEEE Sensors Letters
  issn: '24751472'
  volume: '7'
  issue: '9'
  start_page: 1
  end_page: 4
  article_number: '4502404'

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## Fileset

- id: e88cb1cc-b8a1-41f4-b12a-4012a888e87d
  filename: LSEN_Minami_MS_rev.pdf
  content_type: application/pdf
  size: 1386064
  md5: 8b63e98285ca6f52db24ac6f8eefb801

## Thumbnail

fileset_id: e88cb1cc-b8a1-41f4-b12a-4012a888e87d
filename: LSEN_Minami_MS_rev.pdf