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2026
Journal Article
Title
Taming Waste Heterogeneity for Plastics Circularity with Optimized Sample Preparation Protocols for Quality Assessment
Abstract
From the perspective of the circular economy and minimization of environmental pollution, recycling plastics is key for transforming polymeric waste streams (PWSs) towards reusable and, if possible, upgraded, value-added products. The low homogeneity of PWSs, even when sorted, complicates sampling, analytical characterization, processability, and quality assurance of the whole circular process. Therefore, sampling, sample preparation, and analysis methodologies that yield results accurate and representative enough to describe the contents and the safety of the bulk while being cost-effective are crucial. In this context, an experimental “model waste” approach was conceptualized to reliably assess and optimize sampling and sample preparation strategies towards specific goals, i.e., identifying and precisely quantifying different polymer types and non-polymeric contaminants (such as brominated flame retardants, BFR) along with establishing a correlation of the sample preparation steps with low deviation values between replicates. The results indicated that cryogenic grinding better preserved additive content, minimizing its degradation, i.e., 461 ± 17 ppm determined via HPLC-MS when the nominal concentration was 500 ppm. On the other hand, melt-based homogenization significantly improved homogeneity and hence reproducibility/variability of analytical results (RSD), albeit at the risk of partial additive thermal degradation (up to 70% reduction in BFR content). The current experimental approach allows a clear understanding of plastic waste characteristics in view of demonstrating analytical limits of detection (LoD), reliable verification of compliance with certain concentrations of unwanted contaminants, and eventually robust evaluation of the applied recycling scheme efficiency.
Author(s)
Open Access
File(s)
Rights
CC BY 4.0: Creative Commons Attribution
Additional link
Language
English