A Stable Quaternized Chitosan-Black Phosphorus Nanocomposite for Synergetic Disinfection of Antibiotic-Resistant Pathogens
Bibliographic record
Abstract
Antibiotics are widely used for treatment of bacterial infections, and their overuse has contributed to microbial resistance. Currently, an alternative antibiotic-free therapy for inactivating bacteria is of great interest. Black phosphorus (BP), a biocompatible and nontoxic rising-star two-dimensional layered material, has gained remarkable interest in many bioapplications including biosensing, cancer therapy, drug delivery, and also antibacterial treatment. However, BP nanosheets suffer from instability in ambient environments due to rapid oxidation and degradation. To address this issue, BP nanosheets were modified with quaternized chitosan (QCS) by electrostatic adsorption to prepare a BP-QCS composite for photothermal/pharmaco treatment of bacterial infection. The BP-QCS has obviously enhanced solubility and chemical stability in aqueous suspensions. We have demonstrated that under near-infrared (NIR) irradiation, the BP-QCS can synergistically inactivate more than 95% methicillin-resistant Staphylococcus aureus ( S . aureus ) (MRSA) and Escherichia coli within 10 min with a dose of only 75 μg/mL in vitro. Meanwhile, the BP-QCS composite under NIR can synergistically inactivate 98% S. aureus in vivo . Furthermore, the BP-QCS suspensions at effective antibacterial concentrations have negligible cytotoxicity and in vivo toxicity.
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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".