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Khan AA, Syarifah Adilah MY, Mamat MH, Yahaya SZ, Setumin S, Ibrahim MN, Daud K, Abdullah MH. Magnesium sulfate as a potential dye additive for chlorophyll-based organic sensitiser of the dye-sensitised solar cell (DSSC). Spectrochim Acta A Mol Biomol Spectrosc 2022; 274:121140. [PMID: 35305518 DOI: 10.1016/j.saa.2022.121140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 03/03/2022] [Accepted: 03/09/2022] [Indexed: 06/14/2023]
Abstract
In this work, a new chlorophyll dye-sensitiser derived from mitragyna speciosa (MS) leaves, also known as Kratom, was employed for dye-sensitised solar cells (DSSCs). The influence of magnesium sulfate (MgSO4), a low-cost dye additive, and suitable extraction solvents on the performance of DSSCs were examined. Here, the optical properties were investigated using UV-Visible spectroscopy and the functional anchoring group were investigated by FTIR spectroscopy. Meanwhile, the photovoltaic parameters were investigated by I-V measurements. The highest conversion efficiency is obtained when using a dye extracted from methanol solvent in combination with MgSO4 additive, namely methanolic magnesium sulfate (MMSO). This higher power conversion efficiency is mainly attributed to the enhancement of the hydroxyl group in the MMSO dye solutions, which promotes higher dye adsorption and provides an organic dye passivation layer that reduces back-recombination in the cell. Furthermore, MgSO4 aids in the replenishment of magnesium lost in the chlorophyll porphyrin ring during the degradation process. These combined effects have contributed to the overall conversion efficiency of the MMSO cell at 0.26 %, followed by 0.24 % for ethanolic magnesium sulfate (EMSO), respectively.
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Affiliation(s)
- A A Khan
- Center for Electrical Engineering Studies, Universiti Teknologi MARA, Cawangan Pulau Pinang, 13500 Permatang Pauh, Pulau Pinang, Malaysia
| | - M Y Syarifah Adilah
- Department of Computer and Mathematical Sciences, Universiti Teknologi MARA, Cawangan Pulau Pinang, 13500 Permatang Pauh, Pulau Pinang, Malaysia
| | - M H Mamat
- NANO-ElecTronic Centre (NET), School of Electrical Engineering, College of Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
| | - S Z Yahaya
- Center for Electrical Engineering Studies, Universiti Teknologi MARA, Cawangan Pulau Pinang, 13500 Permatang Pauh, Pulau Pinang, Malaysia
| | - S Setumin
- Center for Electrical Engineering Studies, Universiti Teknologi MARA, Cawangan Pulau Pinang, 13500 Permatang Pauh, Pulau Pinang, Malaysia
| | - M N Ibrahim
- Center for Electrical Engineering Studies, Universiti Teknologi MARA, Cawangan Pulau Pinang, 13500 Permatang Pauh, Pulau Pinang, Malaysia
| | - K Daud
- Center for Electrical Engineering Studies, Universiti Teknologi MARA, Cawangan Pulau Pinang, 13500 Permatang Pauh, Pulau Pinang, Malaysia
| | - M H Abdullah
- Center for Electrical Engineering Studies, Universiti Teknologi MARA, Cawangan Pulau Pinang, 13500 Permatang Pauh, Pulau Pinang, Malaysia..
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Daud K, Mansor MBM, Soh ZHC, Samat AAA, Shafie MA, Ismail AP, Abdullah MH. Windowing Based Continuous S-Transform (ST) with Deep Learning for Detection and Classifying Power Quality Disturbances (PQDs). Technological Innovation in Engineering Research Vol. 2 2022:120-131. [DOI: 10.9734/bpi/tier/v2/6132f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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Daud K, Mansor MBM, Soh ZHC, Samat AAA, Shafie MA, Ismail AP, Abdullah MH. Evaluating windowing based continuous ST with deep learning for detection and classifying PQDs. IOP Conf Ser : Mater Sci Eng 2021; 1088:012060. [DOI: 10.1088/1757-899x/1088/1/012060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
Abstract
This paper discusses the performance of evaluating a windowing based continuous S-Transform (ST) with deep learning classifier for the detection and classifying of interrupt and transient disturbances. The primary purpose is to analyze the detection and classification of voltage interrupt and transient using ST as a signal processing technique. The detection technique is divided into half-cycle and one-cycle windowing techniques (WT with both cycles used for the purpose of comparison. The disturbances signal was create using MATLAB programming language and set in the form of m-file. ST was used to extract the significant feature in a form of scattering data from the disturbances signal. Then, the scattering data was used to build the detection interface inside the disturbances signal. The scattering data is an input for neural network (NN) to classify the percentage accuracy of the disturbances signal. This analysis presents the suitable windowing technique that can provide smooth detection and suitable characteristics to produce high accuracy percentages in the classification of power quality disturbances (PQDs).
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