Green synthesis of catalytic gold/bismuth oxyiodide nanocomposites with oxygen vacancies for treatment of bacterial infections
Bibliographic record
Abstract
We have developed a simple and green solution for the synthesis of catalytic gold-doped bismuth oxyiodide (Au/BiOI) nanocomposites at room temperature from an aqueous mixture of gold ions, bismuth ions, and iodide ions. Au nanoparticles (NPs) were formed in situ and doped into BiOI nanosheets. The oxygen vacancies generated in BiOI give rise to its oxidase-like activity, and Au doping facilitated the reaction leading to a 4-fold higher oxidase-like activity of the Au/BiOI nanocomposite. The Au/BiOI nanocomposites showed wide spectrum antimicrobial activity not only against non-multidrug-resistant E. coli, K. pneumoniae, S. enteritidis, S. aureus, and B. subtilis bacteria, but also against multidrug-resistant bacteria, methicillin-resistant S. aureus (MRSA). The gold doping reduced the minimal inhibitory concentration value by ∼2000-fold for the Au/BiOI nanocomposite, in comparison with only BiOI nanoparticles. The bactericidal property of the Au/BiOI nanocomposite arose from the combined effect of the disruption of the bacterial membrane through a strong interaction of the nanocomposite with the bacteria and the generation of reactive oxygen species. Also, the Au/BiOI nanocomposite is highly biocompatible, which has been demonstrated in vitro by analysis of cytotoxicity and hemolysis, and in vivo by evaluating ocular tissue responses. Furthermore, intrastromal administration of Au/BiOI nanocomposites can effectively alleviate S. aureus-induced bacterial keratitis in rabbits, suggesting a significant disinfectant benefit in preclinical studies. The Au/BiOI nanocomposites show great potential for the inactivation of bacterial pathogens in an aqueous environment and treatment of bacterial infection-induced diseases.
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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 | 0.001 | 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 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".