GAS SENSOR ARRAY BASED ON GRAPHENE-DOPED TIO2 FOR ACETONE VAPOR WITH DEEP NEURAL NETWORK
Jurnal Teknologi, vol.88, no.5, pp.941-950, 2026 (ESCI, Scopus)
- Publication Type: Article / Article
- Volume: 88 Issue: 5
- Publication Date: 2026
- Doi Number: 10.11113/jurnalteknologi.v88.24681
- Journal Name: Jurnal Teknologi
- Journal Indexes: Emerging Sources Citation Index (ESCI), Scopus
- Page Numbers: pp.941-950
- Keywords: artificial intelligence, binder, gas classification, screen printing, VOC gases
- Open Archive Collection: AVESIS Open Access Collection
- Ataturk University Affiliated: Yes
Abstract
Acetone is categorized as a volatile organic compound (VOC) gas. Exposure to it for a long period harms human health and the environment. Commonly, a single sensor is used to sense the target gas. However, it always suffered from low selectivity, making it difficult to analyze and differentiate various gases. To overcome this problem, a gas sensor array is proposed in this work to investigate the response value for each array to the acetone vapor. Six sensor arrays based on various ratios of TiO2 doped with graphene were deposited using the screen-printing technique on an FR3-printed circuit board (PCB). The gas sensor arrays were exposed to two different concentrations of acetone vapor at room temperature. Next, the response values were classified using a deep neural network (DNN). The results revealed that the T95_G5 gas sensor responds more to 25 mL and 35 mL of acetone gas, with 1.0966 and 1.2074, respectively. The overall DNN accuracy for acetone vapor was 82.70%.