Bacterial synthesis of metal nanoparticles as antimicrobials
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
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.006 |
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