Surface characteristics and protein adsorption on combinatorial binary Ti‐M (Cr, Al, Ni) and Al‐M (Ta, Zr) library films
Bibliographic record
Abstract
Systematic studies of protein adsorption onto metallic biomaterial surfaces are generally lacking. Here, combinatorial binary library films with compositional gradients of Ti(1-x)Cr(x), Ti(1-x)Al(x), Ti(1-x)Ni(x) and Al(1-x)Ta(x), (0 <x < 1) and Al(1-y)Zr(y) (0 < y <0.5) as well as corresponding pure metal films were sputtered onto clean Si surfaces. Bulk and surface chemistry, film microstructure, and surface roughness were subsequently correlated to fibrinogen or albumin adsorption measured using a high throughput wavelength dispersive spectroscopy technique. X-ray diffraction revealed these binary films to have crystalline phases present primarily at either extreme of the compositional library and an amorphous zone dominating along the gradient. These mirror-like films were generally found by atomic force microscopy to have a roughness of less than 8 nm, with any relative increases in roughness consistent with the development of crystalline phases. Surface chemistry by quantitative high-resolution X-ray photoelectron spectroscopy differed significantly from bulk film composition as measured by electron microprobe, with TiO(2) and Al(2)O(3) preferentially forming on the binary film surfaces. Correspondingly, protein adsorption onto these films closely correlated with their surface oxide fractions. Aluminum deposited as either a constant-composition film or as part of a binary library consistently adsorbed the least amount of albumin and fibrinogen, with alumina-enrichment of the surface oxide correlating with this adsorption. Overall, this combinatorial materials approach coupled with high-throughput surface analytical methods provides an efficient method of screening potential metallic biomaterials that may enable as well systematic studies of surface properties driving protein adsorption on these metal / metal oxide systems.
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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".