Contamination control in micro- and nanoplastics research: a diagnostic framework for reproducible analysis

Abstract

Micro- and nanoplastics (MNPs) not only contaminate natural environments but also the analytical workflows used to study them. At nanoplastic-relevant size scales, background contamination from airborne particles, consumables, reagents, and laboratory infrastructure increasingly compromises data interpretation, reproducibility, and cross-study comparability. Although contamination-aware practices are widely acknowledged, they remain inconsistently implemented and variably reported across the literature. Here, we introduce the Contamination Control Scorecard (CCS), a structured, risk-weighted diagnostic (non-prescriptive) framework that organises major contamination pathways alongside reporting transparency in MNP research workflows. By systematically mapping laboratory practices across key procedural domains and separating process control from disclosure practices, the CCS supports contamination-aware interpretation of analytical results and highlights recurring sources of uncertainty. This Perspective positions the CCS as an evolving tool to promote transparent reporting, methodological reflection, and reproducible micro- and nanoscale environmental analysis, with broader relevance to trace-level measurements.