Machine vision based class attendance monitoring system

Proton conductors that proficiently operate at high temperatures (over 100oC) have been gaining increasing attention for proton exchange membrane fuel cells (PEMFCs). Many approaches have been taken to improve the proton conductivity of the membrane. The main challenge is in developing PEM with impr...

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Main Author:	Sean, Nor Arbani
Format:	Thesis
Language:	English
Published:	2021
Subjects:	QD Chemistry
Online Access:	http://eprints.utm.my/id/eprint/102259/1/NorArbaniSeanPFS2021.pdf.pdf
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spelling	my-utm-ep.1022592023-08-13T06:17:35Z Machine vision based class attendance monitoring system 2021 Sean, Nor Arbani QD Chemistry Proton conductors that proficiently operate at high temperatures (over 100oC) have been gaining increasing attention for proton exchange membrane fuel cells (PEMFCs). Many approaches have been taken to improve the proton conductivity of the membrane. The main challenge is in developing PEM with improved unidirectional proton-conducting channels through the membranes. Thus, this research has focussed on enhanced proton transport properties of ferrocene functionalized polybenzimidazole (Fc-PBI) membrane in oriented microstructures via magnetic field-assisted solvent casting method. Commercially available PBI solution (Celazole® PBI S26) and ferrocene carboxylic acid (FCA) were used as the polymer matrix and the alignment agent, respectively. Before the fabrication of the membrane, the magnetic properties of PBI and FCA were investigated using a vibrating sample magnetometer (VSM), and both displayed enough magnetic susceptibility for alignment under a magnetic field. Therefore, it was hypothesized that magnetic field-aligned Fc-PBI membrane will improve the proton conductivity through the alignment of the proton-conducting channel in PBI microstructures. Fc-PBI membrane was prepared from the mixture of FCA and PBI solution, followed by casting the mixture onto a glass plate using a scrapper. The casted solution underwent 0.3 Tesla magnetic field treatment in in- and through-plane directions. The physical properties of Fc-PBI membranes were characterized using Fourier transform infrared (FTIR) spectroscopy, X-ray photoelectron spectroscopy (XPS), diffuse reflectance Ultraviolet-visible (DR UV-vis) spectroscopy, X-ray diffraction (XRD) spectroscopy, scanning electron microscopy (SEM), atomic force microscopy (AFM), and thermogravimetry analysis (TGA). The FTIR analysis of the Fc-PBI membrane identified the appearance of N-H amide peaks, indicating that the ferrocene moiety successfully bonded to PBI main chain. The FTIR results were in good agreement with XPS data, detecting the N-(C=O) signal. SEM and AFM images showed that the polymer microstructure was aligned towards the magnetic field direction. The TGA results confirmed that the thermal stability of the membranes was satisfactorily high to operate at high temperatures. Fenton’s test was performed, and the results showed a decrease in oxidative stability with a high amount of Fc content. In this case, the bivalent state of iron (Fe2+) in Fc transforms to ferric ion (Fe3+), initiating the Fenton reaction to decompose the membranes. Even with low oxidative stability, the proton conductivity of aligned Fc-PBI in through-plane direction with 5 wt% FCA at 180oC is 0.024 Scm-1, which is better than that of pristine PBI. The protonic conductivity was found to increase with the formation of through-plane aligned proton channels, reflecting the ease of proton transportation through the short and continuous pathway through the membrane and the effect is more prominent at the high amount of Fc. Therefore, it is suggested that the magnetic field-aligned Fc-PBI would be a strong candidate for high-temperature PEMFC applications. 2021 Thesis http://eprints.utm.my/id/eprint/102259/ http://eprints.utm.my/id/eprint/102259/1/NorArbaniSeanPFS2021.pdf.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:145860 phd doctoral Universiti Teknologi Malaysia Faculty of Science
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QD Chemistry
spellingShingle	QD Chemistry Sean, Nor Arbani Machine vision based class attendance monitoring system
description	Proton conductors that proficiently operate at high temperatures (over 100oC) have been gaining increasing attention for proton exchange membrane fuel cells (PEMFCs). Many approaches have been taken to improve the proton conductivity of the membrane. The main challenge is in developing PEM with improved unidirectional proton-conducting channels through the membranes. Thus, this research has focussed on enhanced proton transport properties of ferrocene functionalized polybenzimidazole (Fc-PBI) membrane in oriented microstructures via magnetic field-assisted solvent casting method. Commercially available PBI solution (Celazole® PBI S26) and ferrocene carboxylic acid (FCA) were used as the polymer matrix and the alignment agent, respectively. Before the fabrication of the membrane, the magnetic properties of PBI and FCA were investigated using a vibrating sample magnetometer (VSM), and both displayed enough magnetic susceptibility for alignment under a magnetic field. Therefore, it was hypothesized that magnetic field-aligned Fc-PBI membrane will improve the proton conductivity through the alignment of the proton-conducting channel in PBI microstructures. Fc-PBI membrane was prepared from the mixture of FCA and PBI solution, followed by casting the mixture onto a glass plate using a scrapper. The casted solution underwent 0.3 Tesla magnetic field treatment in in- and through-plane directions. The physical properties of Fc-PBI membranes were characterized using Fourier transform infrared (FTIR) spectroscopy, X-ray photoelectron spectroscopy (XPS), diffuse reflectance Ultraviolet-visible (DR UV-vis) spectroscopy, X-ray diffraction (XRD) spectroscopy, scanning electron microscopy (SEM), atomic force microscopy (AFM), and thermogravimetry analysis (TGA). The FTIR analysis of the Fc-PBI membrane identified the appearance of N-H amide peaks, indicating that the ferrocene moiety successfully bonded to PBI main chain. The FTIR results were in good agreement with XPS data, detecting the N-(C=O) signal. SEM and AFM images showed that the polymer microstructure was aligned towards the magnetic field direction. The TGA results confirmed that the thermal stability of the membranes was satisfactorily high to operate at high temperatures. Fenton’s test was performed, and the results showed a decrease in oxidative stability with a high amount of Fc content. In this case, the bivalent state of iron (Fe2+) in Fc transforms to ferric ion (Fe3+), initiating the Fenton reaction to decompose the membranes. Even with low oxidative stability, the proton conductivity of aligned Fc-PBI in through-plane direction with 5 wt% FCA at 180oC is 0.024 Scm-1, which is better than that of pristine PBI. The protonic conductivity was found to increase with the formation of through-plane aligned proton channels, reflecting the ease of proton transportation through the short and continuous pathway through the membrane and the effect is more prominent at the high amount of Fc. Therefore, it is suggested that the magnetic field-aligned Fc-PBI would be a strong candidate for high-temperature PEMFC applications.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Sean, Nor Arbani
author_facet	Sean, Nor Arbani
author_sort	Sean, Nor Arbani
title	Machine vision based class attendance monitoring system
title_short	Machine vision based class attendance monitoring system
title_full	Machine vision based class attendance monitoring system
title_fullStr	Machine vision based class attendance monitoring system
title_full_unstemmed	Machine vision based class attendance monitoring system
title_sort	machine vision based class attendance monitoring system
granting_institution	Universiti Teknologi Malaysia
granting_department	Faculty of Science
publishDate	2021
url	http://eprints.utm.my/id/eprint/102259/1/NorArbaniSeanPFS2021.pdf.pdf
_version_	1776100881205297152

Machine vision based class attendance monitoring system

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