000			03963nas a2200421 a 4500
005			20260818113150.0
008			260616s20269999th u ms t 000 eng d
035			_a.b12508184
099		9	_aAIT Thesis no.EV-26-10
100	1		_aGhazal, Naina
245	1	0	_aExploring the electrical sensing zone method for advanced microplastic analysis : _bpolymer differentiation and dynamic monitoring
260			_aPathum Thani, Thailand : _bAsian Institute of Technology, _c2026
300			_a85 leaves : _bill.+ _e1 online resource
490	1		_aThesis ; _vno. EV-26-10
500			_aA thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Environmental Engineering and Management
502			_aThesis (M. Sc.) - Asian Institute of Technology, 2026
520			_aThe Electrical Sensing Zone (ESZ) method is an emerging approach for microplastic detection, capable of providing particle count and size information. This study extends its application beyond conventional counting and sizing by integrating ESZ with machine learning to enable polymer differentiation and real-time monitoring of particle interactions.For polymer differentiation, ESZ signal features were used to classify three polymer types, polyethylene (PE), polyamide (PA), and polyvinyl chloride (PVC), based on particle transit dynamics. Pulse morphology study showed a broader pulse for PE compared to PA and PVC inferring that buoyancy induced pulse broadening provides an excellent discrimination between PE and PA/PVC, however, density induced difference in the pulse width between PA and PVC was indiscernible based on morphology analysis alone. A Random Forest classifier achieved an overall accuracy of 91.24% in three-class discrimination, with high precision for PE and moderate misclassification between PA and PVC due to similar density characteristics.ESZ was applied for real-time monitoring of aggregation dynamics between PVC microplastics and powdered activated carbon (PAC) over a four-hour experiment comprising six phases and 24,557 particle events. While the median particle diameter remained approximately constant at 111.7 æm, the distribution width (Span) increased from 0.265 to 0.415 during early flocculation, indicating aggregate formation. A three-component Gaussian Mixture Model (GMM) successfully differentiated PAC homoaggregates, PVC particles, and PAC-PVC heteroaggregates. The heteroaggregate fraction peaked at 23% during early flocculation before declining due to settling, indicating effective removal.The results demonstrate that ESZ, when coupled with machine learning, can move beyond conventional particle sizing to provide simultaneous insight into microplastic identity and dynamic behavior, offering strong potential for scalable, in-situ environmental monitoring and water treatment applications.
650		0	_aCoulter principle
650		0	_aMicroplastics _xAnalysis
650		0	_aMicroplastics _xEnvironmental aspects
650		0	_aEnvironmental monitoring
700	1		_aXue, Wenchao, _eChairperson
700	1		_aCruz, Simon Guerrero, _eExamination Committee
700	0		_aChantri Polprasert, _eExamination Committee
710	2		_aPMU-KPCIP-AIT Scholarship, _eScholarship Donor
810	2		_aAsian Institute of Technology. _tThesis ; _vno. EV-26-10
856	4	0	_3Full-Text _uhttp://203.159.5.9/ait-thesis/detail.php?q=B24448
907			_a.b12508184 _bmnarc _ca
902			_a260622
998			_b0 _c260618 _dm _eh _fa _g0
945			_lmnarc
945			_lmnarc
942			_c67
942			_c40
909			_aBarcode : - _bCREATED : 2026-06-16 _cRECORD # : i13605434 _dLPATRON : 0 _eLCHKIN : - _f# RENEWALS : 0 _g# OVERDUE : 0 _hIUSE3 : 0 _iTOT CHKOUT : 0 _jTOT RENEW : 0
909			_aBarcode : 30050120421002 _bCREATED : 2026-06-17 _cRECORD # : i13605914 _dLPATRON : 0 _eLCHKIN : - _f# RENEWALS : 0 _g# OVERDUE : 0 _hIUSE3 : 0 _iTOT CHKOUT : 0 _jTOT RENEW : 0
999			_c42470 _d42470