Peptide-Directed Preparation and X-ray Structural Study of Au Nanoparticles on Titanium Surfaces
Bibliographic record
Abstract
We report the peptide-directed preparation and X-ray structural study of biofunctionalized Au nanoparticles (NPs) deposited on Ti surfaces. Au NPs were prepared by reduction of Au(3+) compound onto HCl-refreshed Ti in the presence of thiol-functionalized small peptides. A modified extended X-ray absorption fine-structure (EXAFS) technique, equipped with a rotating-stage and glancing-angle setup, was able to more sensitively detect the structure and bonding of Au NPs on Ti with low surface coverage. It was found that the use of the tripeptide glutathione (GSH) results in smaller NP size when compared to N-(2-mercapto-propionyl) glycine (MPG), a pseudodipeptide, over a wide range of Au/peptide molar ratios (20:1, 10:1, 5:1, and 2:1). By varying the ligand concentration, the Au NP structure in both systems can be controlled, generating nanocrystals, nanoclusters, and Au-thiolate polymer, which is unique for substrate-supported NP synthesis. This work presents a facile preparation of Au-peptide nanoparticles on biocompatible surfaces, and illustrates the high sensitivity of this modified EXAFS technique for structural studies of substrate-supported nanoparticles with low coverage.
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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".