Metal Nanoparticle–Microbe Interactions: Synthesis and Antimicrobial Effects
Bibliographic record
Abstract
Abstract Metal nanoparticles (NPs), chalcogenides, and carbon quantum dots can be easily synthesized from whole microorganisms (fungi and bacteria) and cell‐free sterile filtered spent medium. The particle size distribution and the biosynthesis time can be somewhat controlled through the biomass/metal solution ratio. The biosynthetic mechanism can be explained through the ion‐reduction theory and UV photoconversion theory. Formation of biosynthetic NPs is part of the detoxification strategy employed by microorganisms, either in planktonic or biofilm form, to reduce the chemical toxicity of metal ions. In fact, most reports on NP biosynthesis show extracellular metal ion reduction. This is important for environmental and industrial applications, particularly in biofilms, as it allows in principle high biosynthetic rates. The antimicrobial and antifungal effect on biosynthetic NPs can be explained in terms of reactive oxygen species and can be enhanced by the capping agents attached to the NP during the biosynthesis process. Industrial applications of NP biosynthesis are still lagging, due to the difficulty of controlling NP size and low titer. Further, the environmental assessment of biosynthetic NPs has not yet been carried out. It is expected that further advancements in biosynthetic NP research will lead to applications, particularly in environmental biotechnology.
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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) | 0.001 | 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".