Paradoxical Features Empower Biogenic Silver Nanoparticles
Bibliographic record
Abstract
Silver nanoparticles (AgNPs) have drawn great attention, owing to their unique physico-chemical and biological properties and various applications, particularly in the biomedical field. In addition to conventional chemical and physical methods, materials scientists have been exploring the capabilities endowed by several bioresources, such as plants, bacteria, fungi and algae, in the cost-effective and eco-friendly production of AgNPs. This review article provides a comprehensive overview of the current state of research on the bioapplications of biogenic AgNPs (bio-AgNPs). The various bioresources used and methodologies followed to synthesize bio-AgNPs are briefly examined, along with some aspects of the underlying mechanisms. Then, the review surveys the toxicity of AgNPs, in general, and presents the unique biological properties of bio-AgNPs. Furthermore, the review details numerous applications of bio-AgNPs with paramount importance to human health, such as the control of infectious disease vectors, cancer therapy, antibiofilm activity and environmental remediation. Importantly, the review highlights the paradoxical effect of these nano-objects since they specifically seem to exert their action solely on targeted cells and (micro)organisms. By featuring the unique advantages of biogenic methods and their challenges, this article aims at serving as a valuable resource to attract research on bio-AgNPs and elicit further developments towards the scalable and sustainable production of AgNPs for large scale industrial and clinical use.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".