Accelerated Weathering of Microplastics: A Systematic Approach to Model Microplastic Production
Bibliographic record
Abstract
As microplastics (MP) are omnipresent in the environment, there is an increasing need to understand these emerging contaminants and their potential risks to health and the environment. However, no reliable experimental protocol exists for generating environmentally representative model MPs in sufficient quantities for toxicity and environmental fate studies across various polymer types, as uniform environmental samples are difficult to obtain due to technical and practical challenges. Additionally, there is a lack of focus on mimicking the surface characteristics of environmental microplastics in laboratory weathered samples. In this work, an accelerated method of MP generation from macroplastics was investigated to synthesize MPs that mimic key surface properties of environmental MP samples. A three-step methodology consisting of cryo-milling, UV-O exposure, and mechanochemical persulfate-based surface modification was used to create artificially weathered MPs matching the properties of environmentally found ones. The production of relevant microplastic models has the potential to allow for more quantitative experimental studies on the toxicity, fate, and behavior of these anthropogenic particles. The accelerated weathering method generated MPs in the hundreds of milligrams to grams scales, with controllable and tunable degree of oxidation (measured as carbonyl indices from 0.06 to 1.84), particle size (between 15.8 and 365.4 μm), and surface features. We found these properties to be comparable with MPs found in the ocean, making this report a unique example of scalable and tunable model MP synthesis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".