Aditya Rianjanu, Shidiq Nur Hidayat, Nursidik Yulianto, Nurhalis Majid, Kuwat Triyana, Hutomo Suryo Wasisto
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 (R2) 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. © 2021 The Japan Society of Applied Physics.
Department of Materials Engineering, Institut Teknologi Sumatera, Way Hui, Jati Agung, Lampung, Terusan Ryacudu, 35365, Indonesia; Research and Innovation Center for Advanced Materials, Institut Teknologi Sumatera, Terusan Ryacudu, Jati Agung, Lampung, Way Hui, 35365, Indonesia; Department of Physics, Universitas Gadjah Mada, Sekip Utara PO Box BLS 21, Yogyakarta, 55281, Indonesia; PT Nanosense Instrument Indonesia, Yogyakarta, Umbulharjo, 55167, Indonesia; Institute of Semiconductor Technology (IHT), Technische Universität Braunschweig, Hans-Sommer-Straße 66, Braunschweig, 38106, Germany; Research Center for Physics, Indonesian Institute of Sciences (LIPI), Kawasan Puspiptek Gd. 441-442, Tangerang Selatan, 15314, Indonesia; Institute of Energy Research and Physical Technologies, Technische Universität Clausthal, Leibnizstraße 4, Clausthal-Zellerfeld, 38678, Germany
Research at a Glance
Register to unlock
Topics & SDG Alignment
Register to unlock
Collaboration
Register to unlock
Author Profile (Selected)
Register to unlock
References Overview
Register to unlock
Journal & Source
Register to unlock
Metadata & Integrity
Register to unlock