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Semi-quantitative computational analysis of plastic additives in a FLOPP-E and SLOPP-E database subset
Summary
Researchers developed a semi-quantitative computational 'Additive Analysis' algorithm to characterize the heterogeneous chemical composition of microplastics using spectroscopic and chromatographic data from the FLOPP-E and SLOPP-E databases, offering a faster alternative to conventional particle-by-particle characterization methods.
Abstract Microplastic (MPL) abundance in the environment and the biosphere is a grave problem that is confounded by many aspects with one vital aspect being the characterization of their heterogeneous matrix. Currently, spectroscopy, chromatography, and soxhelation aid in this matter. However, many of these techniques are time-consuming for MPL characterization, which can include a large number of particles. Therefore, we propose a facile “Additive Analysis” algorithm that can provide information and ranking for MPL constituents. For our first trial, we used 2 MPL entries, from FLOPP-E (C2. Blue Fiber) and SLOPP-E (Polyester 12. Red Fiber), as a continuation of our previous work. For our second trial, we extended the use of the algorithm to a semi-randomly selected subset of MPL samples from FLOPP-E and SLOPP-E based on choosing 1 sample of each color for each polymer. Both trials’ reference used an in-lab digitization of the Hummel database for Fourier-transform Infrared (FTIR) spectroscopy and an open-source Raman spectroscopy database from Nava. We determined that the “C2. Blue Fiber” contains metal-free phthalocyanine, potentially indicating the presence of degradation in context to the controls (t 10,.05 : .4879, p: .6387). For “Polyester 12. Red Fiber,” we determined a high likelihood of significant amounts of quinone and azo-family colorants in the sample, negating a previous hypothesis of pyrrole presence (W: 0, p: .036364). For the second trial, 49/56 and 27/40 hits were generated out of the randomly selected samples, with a vast majority possessing hits (matching the color of the sample) within our most scrutinizing tolerance of 5 1/cm (77.6%/74.07%), respectively. For the FTIR portion, the top 3 IDs from tolerances of 5, 10, and 15 1/cm were benzenesulfonohydrazide (1st and 2nd Hit), titanium dioxide (4th Hit), and barium permanganate/barium sulfate (6th Hit). For the Raman portion, the top 3 IDs from tolerances of 5, 10, and 15 1/cm were PR210 (azo derivative – 2nd Hit), PB25 (azo derivative – 2nd Hit), and muscovite (mineral – 1st Hit). Lastly, the distribution for these hits appears to identify organic colorants (FTIR) and azo-derivative colorants (Raman) most dominantly. Our discussion concludes with the potential toxicological impacts of these top 6 IDs.