Microplastics Alter the Distribution and Toxic Potential of Typical Pharmaceuticals in Aqueous Solutions: Mechanisms and Theory Calculations
Bibliographic record
Abstract
The aquatic risks associated with various pharmaceuticals can be significantly influenced by the ubiquitous presence of microplastics (MPs), leading to unforeseen environmental effects. Uncovering the interactions between MPs and pharmaceuticals with diverse functional groups is of crucial importance for accurate risk assessment. Here, the sorption behaviors and underlying mechanisms by which polystyrene (PS) MPs interact with pharmaceuticals having different functional groups were explored through experimental methods, site energy distribution theory, and density functional theory (DFT) calculations. Results indicated that PS MPs exhibited a notable sorption capacity for pharmaceuticals, with the order of sorption being naproxen (NAP), bezafibrate (BZF), norfloxacin (NOR), ibuprofen (IBU), sulfamethoxazole (SMX), and carbamazepine (CAB). A deeper analysis revealed that multiple factors, including hydrophobicity, electrostatic repulsion, π–π interactions, and hydrogen bonding, regulate the sorption process. Furthermore, the Dubinin–Astakhov (DA) model was employed to calculate the energy distribution. The adsorption affinity ( E m = 2.88–8.36 kJ/mol) and energy heterogeneity (σ e * = 1.59–2.25) of PS MPs for different pharmaceuticals followed the order SMX > NOR > NAP > CAB > IBU > BZF. DFT calculations confirmed that the formation of n−π bonds between PS MPs and pharmaceuticals was also a primary sorption mechanism. The different sorption mechanisms of PS MPs for various pharmaceuticals can eventually alter their toxicity, such as increased toxicity of pharmaceuticals with carboxyl groups. Overall, this study offers a more comprehensive understanding of the interactions between MPs and pharmaceuticals, which can contribute significantly to the risk assessment of pharmaceuticals in the presence of MPs.
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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".