Sensitivity prediction and analysis of nanofiber-based gas sensors using solubility and vapor pressure parameters

Rianjanu, Aditya and Hidayat, Shidiq Nur and Yulianto, Nursidik and Majid, Nurhalis and Triyana, Kuwat and Wasisto, Hutomo Suryo (2021) Sensitivity prediction and analysis of nanofiber-based gas sensors using solubility and vapor pressure parameters. JAPANESE JOURNAL OF APPLIED PHYSICS, 60 (10). ISSN 0021-4922

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Abstract

Here, we propose a simple yet effective method to predict gas sensor sensitivity based on solubility and vapor pressure. As sensing devices for the case study, we employed quartz crystal microbalance sensors coated with polyvinyl acetate (PVAc) nanofibers. The solubility was represented by the relative energy density (RED), while the vapor pressure was expressed by the logarithm of the vapor pressure (log P). To create a prediction model, a chemometric technique involving a machine learning algorithm of k-nearest neighbor (KNN) regression was used in the analysis. Using both parameters (i.e., RED and log P) as input, a determination coefficient (R (2)) of up to 1 was obtained, indicating highly correlated parameters. This proposed method could not only enable an accurate prediction of sensor sensitivity, but also provide a path to select the suitable sensing materials for specific target analytes in high-performance gas sensors.

Item Type: Article
Uncontrolled Keywords: vapor pressure; QCM; nanofiber; chemometrics; KNN regression
Subjects: Q Science > QC Physics
Divisions: Faculty of Mathematics and Natural Sciences > Physics Department
Depositing User: Sri JUNANDI
Date Deposited: 15 Oct 2024 00:52
Last Modified: 15 Oct 2024 00:52
URI: https://ir.lib.ugm.ac.id/id/eprint/9342

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