Electrochemical approaches for detecting micro and nano-plastics in different environmental matrices
Bibliographic record
Abstract
Microplastics and nanoplastics are synthetic polymer particles < 5 mm and < 1 μm respectively, have emerged as ubiquitous environmental contaminants with significant implications for ecosystem and human health. Conventional detection methods including FTIR spectroscopy, Raman microspectroscopy, and pyrolysis-GC/MS face substantial limitations including poor spatial resolution, matrix interference, time-intensive analysis, and high costs that hinder widespread monitoring efforts. Electrochemical sensing strategies offer promising alternatives, leveraging the inherent properties of plastic particles through direct detection via particle collision, indirect detection of electroactive additives, and recognition-based approaches using surface modifications. These methods provide exceptional sensitivity with detection limits reaching nanomolar concentrations, rapid response times of seconds to minutes, and significant cost advantages over traditional techniques. Advanced electrode modifications incorporating nanomaterials, molecularly imprinted polymers, and biological recognition elements enhance selectivity and sensitivity for diverse environmental matrices including water bodies, food products, and biological samples. Portable electrochemical sensors enable real-time, on-site monitoring capabilities previously unattainable with laboratory-based methods. Despite challenges in selectivity, reproducibility, and matrix interference, emerging hybrid platforms integrating electrochemical detection with complementary techniques, machine learning algorithms, and automated systems demonstrate significant potential for revolutionizing microplastic monitoring. This review critically evaluates current electrochemical approaches, identifies key limitations, and outlines future research directions toward practical field-deployable sensors for comprehensive environmental surveillance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".