Rapid and high-throughput analysis of PAHs and pesticides adsorbed on microplastics using SPME-MS through a microfluidic open interface coupled to liquid electron ionization mass spectrometry
Bibliographic record
Abstract
• Microplastic and transport of organic pollutants across the environment. • SPME-MS analysis for miniaturization and greenness of the analytical protocol. • Liquid Electron Ionization for the analysis of liquid flows with EI ion source. • Adsorption of PAHs on MPs depends on non-polar interactions. • Compounds adsorption on MPs is conditioned by structure-dependent interactions. Microplastics (MPs) are pervasive contaminants in aquatic environments, capable of adsorbing and transporting hazardous chemicals such as polycyclic aromatic hydrocarbons (PAHs) and pesticides. Understanding these adsorption processes is crucial for evaluating their ecological and health risks. In this study, a green analytical approach with a high-throughput, solid-phase microextraction coupled with a microfluidic open interface and liquid electron ionization mass spectrometry (SPME-MOI-LEI-MS), was applied to investigate the kinetics and thermodynamics of PAHs and pesticides adsorption on low-density polyethylene (LDPE) and polypropylene (PP) microplastics. Method optimization and validation demonstrated intraday RSD values below 15% and limits of quantification below 10 μg/L. Results revealed that PAHs adsorb predominantly through non-polar interactions, with adsorption efficiency correlating with analyte hydrophobicity. For pesticides, adsorption patterns were more diverse, reflecting differences in molecular structure and physicochemical properties. Notably, chlorpyrifos exhibited high affinity for LDPE (95% recovery), raising concern due to its toxicity. Competition experiments further highlighted how strongly adsorbing molecules can inhibit the uptake of weaker ones as adsorption capacity at the equilibrium of atrazine, metalaxyl, dichlorvos and alachlor increases in absence of chlorpyrifos.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".