Automated multimodal spectroscopy system for rapid microscope-free microplastic characterization

Characterizing microplastics for both polymer classification and surface contaminant identification is still lagging behind for general use, due to the need for multiple resource combinations with currently available methodologies. The major limitation of current methodologies for detecting and characterizing microplastics is the lack of methods for combining multiple analytical techniques within a single system for comprehensive assessment. In the current study, we present an automated, microscope-free, multimodal spectroscopic system that combines optical microscopy, Laser-Induced Breakdown Spectroscopy (LIBS), and Raman spectroscopy for comprehensive microplastic characterization, providing morphological, elemental, and molecular information. The system is equipped with a custom-built motorized path selector module that eliminates the need for a bulky microscope for targeted analysis. The system was standardized using virgin microplastics (PE, PP, PET, PS, and PVC), followed by the evaluation of environmentally relevant microplastics from 10 plastic classes to enable automated polymer classification using the Random Forest algorithm. LIBS analysis of microplastics and standard heavy metals is performed to automatically detect heavy metals and other contaminants on the microplastics' surfaces. The automated system enables comprehensive analysis of microplastics in under 30 s by providing morphological, molecular, and elemental information. The performance of the system for environmental microplastic analysis was evaluated using microplastic samples from the Seetha River, where fiber-, fragment-, and film-type microplastics were observed. The Raman-LIBS analysis identified in these studies PP, LDPE, nylon, and PET microplastics, along with surface contaminants Cr, Mn, Zn, Ni, Co, and Ti.