Efficient Removal of Tetracyclines and Quinolones Enabled by Polyphenol-Mediated Supramolecular Coagulation
Bibliographic record
Abstract
Ubiquitous antibiotics threaten human health and ecosystem sustainability, and existing removal strategies, especially conventional multistep water treatments, are primarily limited by the antibiotic-specific removal capability. Here, we explore the natural biomass, plant polyphenols, in the capture of various antibiotics with a facile treatment─polyphenol-mediated antibiotic-independent supramolecular coagulation (PMAC). The PMAC shows a superior performance in removing five tetracyclines and quinolones (up to 98.54%), even under complex environmental parameters, including different pH, the presence of inorganic particles and ionic strength, and the presence of conventional colloid-associated contaminants. Our mechanistic studies suggested that PMAC is capable of exerting multiple molecular interactions with various antibiotics, and the coordination-driven self-assembly further destabilizes the phenolic-antibiotic nanocomplexes, enabling an antibiotic-independent coagulation. Collectively, the combination of efficient remediation with inexpensive biomass suggests a simple and scalable method for the sustainable removal of antibiotics. Our strategy shows great promise as a cost-effective, facile approach to eliminate antibiotics capable of being integrated into the currently existing water treatment systems.
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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".