Citation

Kok, Soo Ping and Go, Yun Li and Anas, Siti Barirah Ahmad and Wong, M. L. Dennis and Chan, Kah Yoong (2025) Additive-Enhanced SnO2 FBG Sensor with Optimized Annealing Time, Temperature, and Multilayer Coating for High-Performance Humidity Sensing. Nanomaterials, 15 (19). p. 23. ISSN 20794991

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Abstract

TCoating plays an important role in advancing sensing technology by significantly enhancing sensitivity, stability, and response time. The unique properties of nanostructures, including high surface-to-volume ratio and tunable porosity, make them suitable candidates for improving sensor performance. By optimizing nanostructure coatings, advancements in high-precision humidity sensing devices are achievable, enabling a wide range of industrial applications, especially in humidity-controlled industries. In this study, the effects of annealing time, annealing temperature, and the number of coating layers on the properties of additive-enhanced SnO2 nanostructure were investigated. The experiment was carried out by subjecting the additive-enhanced SnO2 nanostructure to different annealing times and annealing temperatures to analyze its impact on crystallinity, porosity, and moisture adsorption properties. Upon optimizing the annealing parameters, multilayer coatings were carried out to assess the effect of the total number of coating layers on hygroscopic behavior. A hygroscopicity test was carried out on each sample to evaluate its moisture adsorption and desorption capabilities. The results demonstrated that controlled annealing conditions significantly improve the nanostructure’s hygroscopic properties, and the optimized coating layers further enhanced the moisture retention, making the developed SnO2 nanostructure a promising candidate for advanced sensing applications.

Item Type:	Article
Uncontrolled Keywords:	Nanostructures, precision, reactive material, sensitivity
Subjects:	Q Science > QC Physics > QC170-197 Atomic physics. Constitution and properties of matter Including molecular physics, relativity, quantum theory, and solid state physics
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Nor Afiqah Mohd Adnan
Date Deposited:	06 Nov 2025 03:12
Last Modified:	06 Nov 2025 03:12
URII:	http://shdl.mmu.edu.my/id/eprint/14705

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