Detection and Characterisation of Micro- and Nano-plastics in Water using Optical Spectroscopy

Abstract

Micro- and nanoplastic (MNP) contamination in various environmental matrices has become an increasing global concern due to its potential ecological and health impacts. However, detecting and characterising these minuscule plastic particles remains challenging due to the limitations of conventional spectroscopic techniques. This thesis explores the potential of photoluminescence (PL) spectroscopy as an alternative technique for detecting and characterising MNPs in aqueous media. The research encompasses the optimisation of fluorescence excitation-emission (FLE) features for MNP detection, the development of a protocol for generating model MNPs, and the proof-of-concept demonstration of a semi-portable PL spectrometer for microplastic detection. In the first set of experiments, Fluorescence (FL) spectroscopy was utilised to investigate the intrinsic excitation-emission features of common microplastics, including polystyrene (PS), polyethylene terephthalate (PET), and polypropylene (PP). Through systematic analysis, distinct fluorescence signatures were identified for each polymer type, enabling precise and label-free identification. These spectral characteristics, linked to polymer-specific molecular transitions, form the basis for a non-destructive fluorescence-based microplastic detection method. The second set of experiments established a protocol for generating model MNPs through a high-power direct ultra-sonication technique. This method significantly improved the efficiency of MNP production compared to conventional methods, eliminating the need for chemical additives and achieving high suspension stability in an aqueous medium. The generated MNPs, ranging from 100 nm to 150 μm, closely resembled environmental plastic pollutants in terms of size distribution and morphology. Their characterisation using Raman spectroscopy, infrared spectroscopy combined with machine learning, UV-Vis spectroscopy, and SEM imaging confirmed their suitability as reference materials for microplastic research. Furthermore, the third phase of the study examined the effectiveness of FLE mapping for detecting and characterising MNPs in aqueous media. The study demonstrated that ultrasonicated MNPs, including PS, PET, and PP exhibited strong and distinct FL spectra, including those smaller than 100nm. This study confirmed that optimised excitation wavelengths provide distinct FL fingerprints for MNPs and enhance their FL signal strength, improving the technique's sensitivity for real-time detection. Finally, the development of a semi-portable PL spectrometer further highlighted the feasibility of real-time, on-site MNP detection. Although challenges such as background noise require further optimisation, this proof-of-concept system represents a significant step toward field-deployable MNP pollution monitoring.

Publication DOI: https://doi.org/10.48780/publications.aston.ac.uk.00048416
Divisions: College of Engineering & Physical Sciences > Aston Institute of Photonics Technology (AIPT)
Additional Information: Copyright © Syed Atif Iqrar, 2025. Syed Atif Iqrar asserts his moral right to be identified as the author of this thesis. This copy of the thesis has been supplied on condition that anyone who consults it is understood to recognise that its copyright rests with its author and that no quotation from the thesis and no information derived from it may be published without appropriate permission or acknowledgement. If you have discovered material in Aston Publications Explorer which is unlawful e.g. breaches copyright, (either yours or that of a third party) or any other law, including but not limited to those relating to patent, trademark, confidentiality, data protection, obscenity, defamation, libel, then please read our Takedown Policy and contact the service immediately.
Institution: Aston University
Uncontrolled Keywords: micro- and nanoplastics,photoluminescence spectrography,ultrasonification,semi-portable spectrometer,microplastic detection,intrinsic fluourescence,machine learning
Last Modified: 28 Nov 2025 08:09
Date Deposited: 27 Nov 2025 12:54
Completed Date: 2025-03
Authors: Iqrar, Syed Atif

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