Illuminating the Surface Corona of Biogenic Silver Nanoparticles
Bibliographic record
Abstract
Biogenic nanoparticles are attractive due to their unique surface chemistry compared to chemical counterparts. Underpinning the importance of the surface layer or corona is the interaction between the nanoparticles and the environment. The surface corona provides biological identity, physical structure, colloidal stability and a chemical scaffold for modification. Understanding the structure and composition of the corona that surrounds nanoparticles enables rational design for applications. Previous investigations examining biogenic nanoparticles have purported a coating comprised of biomolecules, however a defined structure is extremely limited. We address this limitation through a detailed examination involving both in situ analyses of silver nanoparticle dispersions (indirect) produced by Fusarium oxysporum and desorbing the surface corona (direct). Using a series of orthogonal characterization techniques we show evidence the surface corona of biogenic silver nanoparticles is comprised of a thin mixed layer of peptides and carbohydrates. We propose the origin of these peptides is from adaptive or protective proteins triggered by environmental stress. The differences and limitations in our two approaches are highlighted. Our findings make it clear that methods used to characterize the inherent surface corona of biogenic silver nanoparticles should be carefully documented for consistent and judicious interpretations.
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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".