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Journal : Bulletin of Electrical Engineering and Informatics

Zinc oxide-coated fiber-optic sensors for monitoring of edible oil adulteration with internet of things integration Haroon, Hazura; Othman, Siti Khadijah Idris@; Razak, Hanim Abdul; Zain, Anis Suhaila Mohd; Salehuddin, Fauziyah; Mukhtar, Wan Maisarah
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7604

Abstract

The study proposes a novel approach for detecting adulteration in edible oils utilising a zinc oxide (ZnO)-coated optical sensor. The procedure included the development of a sensor probe using a plastic optical fiber (POF) with a ZnO nanolayer deposition. The ZnO nanorods were applied to the surface of the POF via a hydrothermal process. The sensitivity and accuracy of uncoated and ZnO-coated POFs were compared, and it was discovered that the ZnO-coated POF was more sensitive to changes in the refractive index of the samples under testing. The study ascertained a correlation between the optical power and voltage of the sensor and the refractive index of the medium. As the adulterant concentration in the oil mixture increased, the refractive index of the medium altered. As a result, both the sensor’s output voltage and optical power decreased. Upon completion, it was discovered that the uncoated POF had a sensitivity of 0.073 V/%, whereas the ZnO-coated POF had a sensitivity of 0.085 V/%. These findings highlight the effectiveness of ZnO-coated optical sensors, as well as their potential integration into internet of things (IoT) platforms for monitoring adulteration in edible oils.
Optimization of perovskite solar cell with MoS2-based HTM layer using hybrid L27 Taguchi-GRA based genetic algorithm Ezwan Kaharudin, Khairil; Salehuddin, Fauziyah; Ahmad Jalaludin, Nabilah; Suhaila Mohd Zain, Anis; Arith, Faiz; Aisah Mat Junos, Siti; Ahmad, Ibrahim
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8455

Abstract

This article proposes an optimization method to predictively model the perovskite solar cell with molybdenum disulfide (MoS2) based inorganic hole transport material (HTM) for improved fill factor (FF) and power conversion efficiency (PCE) by finding the most optimum thickness and donor/acceptor concentration for each layer via a hybrid L27 Taguchi grey relational analysis (GRA) based genetic algorithm (GA). Numerical simulation of the device is carried out by employing one-dimensional solar cell capacitance simulator (SCAPS-1D) while the optimization procedures are developed based on combination of multiple methods; L27 Taguchi orthogonal array, GRA, multiple linear regression (MLR), and GA. The results of post-optimization reveal that the most optimum layer parameters for improved FF and PCE are predicted as follows; SnO2F thickness (0.855 μm), SnO2F donor concentration (9.206×1018 cm-3), TiO2 thickness (0.011 μm), TiO2 donor concentration (9.306×1016 cm-3), CH3NH3PbI3 thickness (0.897 μm), CH3NH3PbI3 donor concentration (0.906×1013 cm-3), MoS2 thickness (0.154 μm), and MoS2 acceptor concentration (9.373×1017 cm-3). Both FF and PCE of the device are improved by ~1.1% and ~12.6% compared to the pre-optimization.