Bacterial synthesis of metal nanoparticles as antimicrobials
Bibliographic record
Abstract
Nanoscience, a pivotal field spanning multiple industries, including healthcare, focuses on nanomaterials characterized by their dimensions. These materials are synthesized through conventional chemical and physical methods, often involving costly and energy-intensive processes. Alternatively, biogenic synthesis using bacteria, fungi, or plant extracts offers a potentially sustainable and non-toxic approach for producing metal-based nanoparticles (NP). This eco-friendly synthesis approach not only reduces environmental impact but also enhances features of NP production due to the unique biochemistry of the biological systems. Recent advancements have shown that along with chemically synthesized NPs, biogenic NPs possess significant antimicrobial properties. The inherent biochemistry of bacteria enables the efficient conversion of metal salts into NPs through reduction processes, which are further stabilized by biomolecular capping layers that improve biocompatibility and functional properties. This mini review explores the use of bacteria to produce NPs with antimicrobial activities. Microbial technologies to produce NP antimicrobials have considerable potential to help address the antimicrobial resistance crisis, thus addressing critical health issues aligned with the United Nations Sustainability Goal #3 of good health and well-being.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".