Stability and aggregation of microplastics suspended in aqueous media
Bibliographic record
Abstract
The accumulation of microplastics (MPs) has become a significant problem due to their non-biodegradable nature and ingestion by marine life. This study focused on three types of microplastics of approximate size 1 μm: polystyrene microplastics (PSMPs), carboxylate-modified polystyrene (PS-COOH), and amine-modified polystyrene (PS-NH2). Their behavior was investigated in aqueous media at pH 5 and 9, with the inclusion of humic acid, different ions (Mg2+ or Na+) with ionic strength (IS) range (3mM and 15mM), and kaolinite. Results showed that a divalent cation, Mg2+, promoted aggregation by causing compression of the electric double layer. In lower IS scenarios, repulsion forces generated by electrical charges led to the stability of the PSMPs, inhibiting aggregation. Also, generally, suspensions were more unstable at higher pH values as the surface charge on microplastics may reverse, leading to reduced electrostatic repulsion. Humic acid improved the stability of suspensions through electrostatic or steric repulsion forces. Conversely, kaolinite reduced electrostatic repulsion, making suspensions less stable. Moreover, PS-NH2 suspensions generally exhibited stability with high energy barriers, affected by kaolinite and IS. PS-COOH suspensions remained stable in higher IS NaCl, suggesting that the carboxylic acid groups contributed to their stability. These findings contribute to developing strategies to mitigate the environmental impact of microplastics.
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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 | 0.000 |
| 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 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".