Fungal Isolate Optimized for Biogenesis of Silver Nanoparticles with Enhanced Colloidal Stability
Bibliographic record
Abstract
Understanding the synthesis and properties of nanomaterials is critical for reliable applications. Biological systems, such as fungi, have been described as a "green" alternative to synthesis, yet knowledge gaps exist in terms of production variability, comparison with commercial products, and identifying a clear biological advantage over other synthesis methods. In this study, we evaluated 12 fungal isolates of Fusarium oxysporum for Ag nanoparticle production and characterized the resultant biologically produced (biogenic) nanoparticles. We show evidence that isolate selection, temperature, and pH can influence the quantity, size, and shape of nanoparticles. All F. oxysporum isolates produced Ag nanoparticles, but in varied quantities. Increasing incubation temperature increased the quantity, yet nanoparticle diameter was inversely related to temperature. Variations in pH predominately influenced nanoparticle morphology. A direct comparison with commercial, chemically produced Ag nanoparticles yielded physical similarities; however, important differences in surface chemistry are observed. Biogenic nanoparticles show a greater degree of colloidal stability in high-ionic-strength solutions, pointing to a biological advantage associated with the fungal produced layer (corona) surrounding the nanoparticles. It is clear that understanding the organic layer and interfacial interactions will be beneficial in developing innovative applications, particularly in the field of biosensing.
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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 | 0.000 | 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".