Engineering of self-assembled silver-peptide colloidal nanohybrids with enhanced biocompatibility and antibacterial activity
Bibliographic record
Abstract
Several bacterial strains have developed resistance against commercial antibiotics, and interestingly, supramolecular nanomaterials have shown considerable advantages for antibacterial applications. However, the main challenges in adopting nanotechnology for antibacterial studies are random aggregation, compromised toxicity, multi-step preparation approaches, and unclear structure-function properties. Herein, we designed the amphiphilic tripeptide that acts as a reducing and capping agent for silver metal to form silver-peptide colloidal nanohybrids with the mild assistance of UV light (254 nm) through the photochemical reduction method. The nanohybrids are characterized by different spectroscopic and microscopic techniques, and non-covalent molecular interactions between metal and peptide building blocks confirm their central role in the formation of nanohybrids. The tripeptide is biocompatible and can reduce the toxicity of silver ions (Ag + ) by reducing to Ag 0 . These colloidal nanohybrids showed antibacterial activity against gram-negative and gram-positive bacterial strains, and the possible mechanism of killing bacterial cells could be membrane disruption. This synthetic strategy is facile and green, which helps avoid using toxic chemicals or reagents and complicated methods for colloidal nanohybrid preparation for biomedical applications.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".