Mechanistic description of lead sorption onto nanoplastics
Bibliographic record
Abstract
Although it is currently recognized that nanoplastics (NPs) are present in the environment, their ability to carry metals or other contaminants and the processes involved are still poorly investigated. Yet, one of the significant threats of plastic debris is found in these associated species. The lack of relevant data is directly explained by our ability to collect enough nanoplastics from the environment for experimental investigation. The environmental NPs models that we have recently proposed offer new opportunities to characterize their interactions with metals. We have developed an adsorption model based on lead (Pb2+) adsorption experiments (adsorption isotherm at pH 5.5 for Pb(II)/NPs ratios between 0.5 and 100 mg gC-1 and a pH adsorption edge between pH 2 and 8 for Pb(II)/NPs at 2.5 and 25 mg gC-1 relative to the studied NPs, Ionic strength = 5 mmol L-1 of NaNO3) using various NPs models to describe surface complexation processes and to predict the sorption capacity of NPs. Model hypotheses have suggested that Pb is adsorbed onto NPs via carboxylic sites mainly as mono-ligand and bi-ligand complexes. The empirical model and linear free energy relationship validated the extrapolated stability constants and binding hypothesis. The adsorption capacities of nanoplastics are highly dependent on their surface oxidation state, shape heterogeneity and aggregation which control their carboxylic site density, distribution and then availability. This metal-NPs affinity suggests that NPs could act as vectors of metals relative to their behavior regarding other natural adsorbent phases.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".