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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.

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  © 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.

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

Date published: 2023-08-01

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

Journal:

• IEEE Sensors Letters (ISSN: 24751472) vol. 7 issue. 9 p. 1-4 4502404

Funding:

Manuscript type: Author's version (Accepted manuscript)

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

First published URL: https://doi.org/10.1109/LSENS.2023.3300802

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Contact agent: 南皓輔 ()

Updated at: 2025-10-21 16:16:25 +0900

Published on MDR: 2025-10-21 16:15:46 +0900