Binding Between Antibiotics and Polystyrene Nanoparticles Examined by NMR
Bibliographic record
Abstract
Elucidating the interactions between plastic nanoparticles and small molecules is important to understanding these interactions as they occur in polluted waterways. For example, plastic that breaks down into micro- and nanoscale particles will interact with small molecule pollutants that are also present in contaminated waters. Other components of natural water, such as dissolved organic matter, will also influence these interactions. Here we use a collection of complementary NMR techniques to examine the binding between polystyrene nanoparticles and three common antibiotics, belonging to a class of molecules that are expected to be common in polluted water. Through examination of proton NMR signal intensity, relaxation times, saturation-transfer difference (STD) NMR, and competition STD-NMR, we find that the antibiotics have binding strengths in the order amoxicillin < metronidazole ≪ levofloxacin. Levofloxacin is able to compete for binding sites, preventing the other two antibiotics from binding. The presence of tannic acid disrupts the binding between levofloxacin and the polystyrene nanoparticles, but does not influence the binding between metronidazole and these nanoparticles.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".