Gold nanoparticles on titanium and interaction with prototype protein
Bibliographic record
Abstract
Modifying titanium (Ti) implant surfaces with functional proteins can strengthen the interface between prosthesis and bone. A prototype system was developed using gold nanoparticles (AuNPs) to immobilize proteins onto Ti. An electroless (galvanic displacement) deposition method was first used to form AuNPs of controlled size and coverage on commercial Ti foil (giving Ti-AuNPs). Parameters were then modified to create two groups of discs (n = 26) with different average AuNP diameters. Scanning electron microscopy and X-ray photoelectron spectroscopy were used to characterize the morphology and surface structure of Ti-AuNPs. To study the interaction of Ti-AuNPs with proteins, Ti discs (n = 8) modified with plain AuNPs and discs (n = 8) modified with thiol (HS--R--COOH)-functionalized AuNPs were treated with lysozyme solution. The amount and activity of the lysozyme on the discs were examined with Micro-BCA and enzymatic assays. Lysozyme was immobilized onto the discs, and the assays showed that the discs with thiol-functionalized AuNPs, discs with bare AuNPs, and Ti controls had average lysozyme adsorptions of 23 x 10(4), 2.3 x 10(4), and 5.7 x 10(4) microg/m2, respectively. The activity assays showed that 21.5, 18.4, and 12.5% of the adsorbed lysozyme was active on the discs with thiol-functionalized AuNPs, discs with bare AuNPs, and Ti controls, respectively. This technique holds promise for binding functional biomolecules to surgical implants, hence possibly creating implant surfaces that react to their local environment.
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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 | 0.001 | 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".