Advancements in Raman imaging for nanoplastic analysis: Challenges, algorithms and future Perspectives

Cheng Fang; Yunlong Luo; Ravi Naidu

doi:10.1016/j.aca.2023.342069

Advancements in Raman imaging for nanoplastic analysis: Challenges, algorithms and future Perspectives

Anal Chim Acta. 2024 Feb 15:1290:342069. doi: 10.1016/j.aca.2023.342069. Epub 2023 Dec 4.

Authors

Cheng Fang¹, Yunlong Luo², Ravi Naidu³

Affiliations

¹ Global Centre for Environmental Remediation (GCER), University of Newcastle, Callaghan, NSW, 2308, Australia; Cooperative Research Centre for Contamination Assessment and Remediation of the Environment (CRC CARE), University of Newcastle, Callaghan, NSW, 2308, Australia. Electronic address: cheng.fang@newcastle.edu.au.
² Global Centre for Environmental Remediation (GCER), University of Newcastle, Callaghan, NSW, 2308, Australia.
³ Global Centre for Environmental Remediation (GCER), University of Newcastle, Callaghan, NSW, 2308, Australia; Cooperative Research Centre for Contamination Assessment and Remediation of the Environment (CRC CARE), University of Newcastle, Callaghan, NSW, 2308, Australia.

PMID: 38246736
DOI: 10.1016/j.aca.2023.342069

Abstract

Background: While the concept of microplastic (<5 mm) is well-established, emergence of nanoplastics (<1000 nm) as a new contaminant presents a recent and evolving challenge. The field of nanoplastic research remains in its early stages, and its progress is contingent upon the development of reliable and practical analytical methods, which are currently lacking. This review aims to address the intricacies of nanoplastic analysis by providing a comprehensive overview on the application of advanced imaging techniques, with a particular focus on Raman imaging, for nanoplastic identification and simultaneous visualisation towards quantification.

Results: Although Raman imaging via hyper spectrum is a potentially powerful tool to analyse nanoplastics, several challenges should be overcome. The first challenge lies in the weak Raman signal of nanoplastics. To address this, effective sample preparation and signal enhancement techniques can be implemented, such as by analysing the hyper spectrum that contains hundred-to-thousand spectra, rather than a single spectrum. Second challenge is the complexity of Raman hyperspectral matrix with dataset size at megabyte (MB) or even bigger, which can be adopted using different algorithms ranging from image merging to multivariate analysis of chemometrics. Third challenge is the laser size that hinders the visualisation of small nanoplastics due to the laser diffraction (λ/2NA, ∼300 nm), which can be solved with involving the use of super-resolution. Signal processing, such as colour off-setting, Gaussian fitting (via deconvolution), and re-focus or image re-construction, are reviewed herein, which show a great promise for breaking through the diffraction limit.

Significance: Overall, current studies along with further validation are imperative to refine these approaches and enhance the reliability, not only for nanoplastics research but also for broader investigations in the realm of nanomaterials.

Keywords: Algorithms; Laser diffraction; Microplastic; Nanoplastic; Raman imaging; Super-resolution imaging.

Publication types

Review