Biomolecule-Coated Metal Nanoparticles on Titanium
Bibliographic record
Abstract
Immobilizations of nanoparticles and biomolecules on biocompatible substrates such as titanium are two promising approaches to bringing new functionalities to Ti-based biomaterials. Herein, we used a variety of X-ray spectroscopic techniques to study and better understand metal-thiolate interactions in biofunctionalized metal nanoparticle systems supported on Ti substrates. Using a facile one-step procedure, a series of Au nanoparticle samples with varied biomolecule coatings ((2-mercatopropionyl)glycine (MPG) and bovine serum albumin (BSA)) and biomolecule concentrations are prepared. Ag and Pd systems are also studied to observe change with varying metal composition. The structure and properties of these biomolecule-coated nanoparticles are investigated with scanning electron microscopy (SEM) and element-specific X-ray techniques, including extended X-ray absorption fine structure (Au L(3)-edge), X-ray absorption near-edge structure (Au L(3), Ag L(3), Pd L(3), and S K-edge), and X-ray photoelectron spectroscopy (Au 4f, Ag 3d, Pd 3d, and S 2p core level). It was found that, by comparison of SEM and X-ray spectroscopy results, the coating of metal nanoparticles with varying model biomolecule systems can have a significant effect on both surface coverage and organization. This work offers a facile chemical method for bio- and nanofunctionalization of Ti substrates as well as provides a physical picture of the structure and bonding of biocoated metal nanoparticles, which may lead to useful applications in orthopedics and biomedicine.
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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 | 0.001 | 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".