Integrating Metal–Phenolic Networks-Mediated Separation and Machine Learning-Aided Surface-Enhanced Raman Spectroscopy for Accurate Nanoplastics Quantification and Classification
Bibliographic record
Abstract
Increasing accumulation of nanoplastics across ecosystems poses a significant threat to both terrestrial and aquatic life. Surface-enhanced Raman scattering (SERS) is an emerging technique used for nanoplastics detection. However, the identification and classification of nanoplastics using SERS faces challenges regarding sensitivity and accuracy as nanoplastics are sparsely dispersed in the environment. Metal-phenolic networks (MPNs) have the potential to rapidly concentrate and separate various types and sizes of nanoplastics. SERS combined with machine learning may improve prediction accuracy. Herein, we report the integration of MPNs-mediated separation with machine learning-aided SERS methods for the accurate classification and high-precision quantification of nanoplastics, which is tailored to include the complete region of characteristic peaks across diverse nanoplastics in contrast to the traditional manual analysis of SERS spectra on a singular characteristic peak. Our customized machine learning system (e.g., outlier detection, classification, quantification) allows for the identification of detectable nanoplastics (accuracy 81.84%), accurate classification (accuracy > 97%), and sensitive quantification of various types of nanoplastics (polystyrene (PS), poly(methyl methacrylate) (PMMA), polyethylene (PE), and poly(lactic acid) (PLA)) down to ultralow concentrations (0.1 ppm) as well as accurate classification (accuracy > 92%) of nanoplastic mixtures at a subppm level. The effectiveness of this approach is substantiated by its ability to discern between different nanoplastic mixtures and detect nanoplastic samples in natural water systems.
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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 | 0.002 |
| 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 | 0.000 |
| 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".