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...

Full description

Saved in:
Bibliographic Details
Main Author: Sean, Nor Arbani
Format: Thesis
Language:English
Published: 2021
Subjects:
Online Access:http://eprints.utm.my/id/eprint/102259/1/NorArbaniSeanPFS2021.pdf.pdf
http://eprints.utm.my/id/eprint/102259/
http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:145860
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.utm.102259
record_format eprints
spelling my.utm.1022592023-08-13T06:17:35Z http://eprints.utm.my/id/eprint/102259/ Machine vision based class attendance monitoring system 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 NonPeerReviewed application/pdf en http://eprints.utm.my/id/eprint/102259/1/NorArbaniSeanPFS2021.pdf.pdf Sean, Nor Arbani (2021) Machine vision based class attendance monitoring system. PhD thesis, Universiti Teknologi Malaysia. http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:145860
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
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
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
publishDate 2021
url http://eprints.utm.my/id/eprint/102259/1/NorArbaniSeanPFS2021.pdf.pdf
http://eprints.utm.my/id/eprint/102259/
http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:145860
_version_ 1775621961146171392
score 13.211869