Metal Nanoparticle–Microbe Interactions: Synthesis and Antimicrobial Effects
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it