A physical vapor deposition method for controlled evaluation of biological response to biomaterial chemistry and topography
Bibliographic record
Abstract
The purpose of this study was to characterize a technique to effectively mask surface chemistry without modifying surface topography. A thin layer of titanium was deposited by physical vapor deposition (PVD) onto different biomaterial surfaces. Commercially pure titanium disks were equally divided into three groups. Disks were either polished to a mirror finish, grit blasted with alumina particles, or grit blasted and subsequently plasma sprayed with a commercial grade of hydroxyapatite (HA). A subgroup of each of these treatment types was further treated by masking the entire disk surface with a thin layer of commercially pure titanium deposited by PVD. A comparison of surface topography and chemical composition was carried out between disks within each treatment group. Canine marrow cells were seeded on all disk surfaces to determine the stability of the PVD Ti mask under culture conditions. The PVD process did not significantly alter the surface topography of any samples. The thin titanium layer completely masked the underlying chemistry of the plasma sprayed HA surface and the chemistry of the plasma vapor deposited titanium layer did not differ from that of the commercially pure titanium disks. Aliquots obtained from the media during culture did not indicate any significant differences in Ti concentration amongst the Ti and Ti-masked surfaces. The PVD application of a Ti layer on HA coatings formed a stable, durable, and homogenous layer that effectively masked the underlying surface chemistry without altering the surface topography.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".