Nano sized gallium oxide surface features for enhanced antimicrobial and osteo-integrative responses
Bibliographic record
Abstract
Gallium oxide has known beneficial osteo-integrative properties. This may have importance for improving the osteointegration of orthopedic implants. At high concentrations gallium is cytotoxic. Therefore, integration of gallium into implant devices must be carefully controlled to limit its concentration and release. A strategy based on surface doping of gallium although challenging seems an appropriate approach to limit dose amounts to minimize cytotoxicity and maximize osteointegration benefits. In this work we develop a novel form of patterned surface doping via a block copolymer-based surface chemistry that enables very low gallium content but enhanced osteointegration as proven by comprehensive bioassays. Polystyrene-b-poly 4vinyl pyridine (PS-b-P4VP) BCP (block copolymer) films were produced on surfaces. Selective infiltration of the BCP pattern with a gallium salt precursor solution and subsequent UV-ozone treatment produced a surface pattern of gallium oxide nanodots as evidenced by atomic force and scanning electron microscopy. A comprehensive study of the bioactivity was carried out, including antimicrobial and sterility testing, gallium ion release kinetics and the interaction with human marrow mesenchymal stomal cells and mononuclear cells. Comparing the data from osteogenesis media assay tests with osteoclastogenesis tests demonstrated the potential for the gallium oxide nanodot doping to improve osteointegration properties of a surface.
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 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".