Unravelling protein corona formation on pristine and leached microplastics
Bibliographic record
Abstract
Abstract Upon entering the environment, the surface properties of pristine plastics are rapidly altered due to interactions with exogenous biomolecules, contaminants, and even microbiota, which ultimately alter their ecological impacts. When present in biological fluids or high protein environments, micro(nano)plastics bind with proteins, which form a protein corona around the particle. Although a significant body of literature exists on protein corona formation on nanomaterials, less is known about how the physiochemical properties of microplastics may influence protein corona formation. This study utilises quantitative proteomics to quantify protein binding to pristine and leached microplastics. Pristine polyethylene (PE) beads (50 and 500 μm), polyamide (PA) fibres (100 μm), polyethylene terephthalate fibres (500 μm), and fragments (< 300 μm), as well as pristine and leached textile microfibres comprised of PET, recycled PET, PA or cotton were incubated for 24 h in bovine serum albumin solution (2 mg mL −1 ) to form a protein corona. Protein adsorption to microplastics was dependant on particle surface area to volume ratio but only when additives were absent. For environmentally relevant textile microfibres, cotton microfibres adsorbed significantly more protein than synthetic microfibres. Fourteen-day aqueous leaching increased the zeta potential of all microfibres. However, only PA fibres adsorbed significantly higher protein on the leached fibres compared to their pristine counterparts. Overall, the presence of chemical additives in microplastics strongly influenced protein corona formation, and this phenomenon should be incorporated into routine microplastic toxicity assessment. Graphical Abstract
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".