Salt-Induced Adsorption and Rupture of Liposomes on Microplastics
Bibliographic record
Abstract
Microplastics have attracted considerable attention because of concerns regarding their environmental risks to living systems. The interaction between the lipid bilayer and microplastics is important for examining the potential harm to biological membranes in the presence of microplastics. In addition, membrane coatings may change the surface and colloidal properties of microplastics. Herein, phosphatidylcholine (PC) lipids, whose headgroup is most common in cell membranes, were used as model lipids. The adsorption and rupture of PC liposomes on microplastics were systematically studied. We found that divalent metal ions, such as Mg 2+ and Ca 2+, facilitate liposome adsorption onto microplastics and induce 40–55% liposome leakage at 2.5 mM. In contrast, to achieve a similar effect, 300 mM Na + was required. Adsorption and rupture followed the same metal concentration requirements, suggesting that liposome adsorption was the rate-limiting step. After adsorption with liposomes, microplastics became more hydrophilic and were better dispersed in water. A similar behavior was observed for all five types of tested microplastics, including PP, PE, PVC, PET, and PS. Leakage also occurred in ocean water. This study provides fundamental insights into the interactions between liposomes and microplastics and has implications for the colloidal and transport properties 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) | 0.001 | 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".