Bibliographic record
Abstract
Attributable to their low chemical durability, borate-based glasses have been demonstrated to convert rapidly to hydroxy-carbonated apatite (HCA), the inorganic component of bone. However, the effect of sol-gel processing on the bioactivity of borate-based glasses has not been investigated. In this study, the gel and glass forming abilities of six different borate-based glass formulations with borate content ranging from 36 to 61 mol % and based on a previously studied four component melt-derived glass system [(46.1)B 2 O 3 –(26.9)CaO–(24.4)Na 2 O–(2.6)P 2 O 5; mol %] were investigated. Compared to melt-quench, sol-gel processing fabricated nanoporous glass particles with at least 2 orders of magnitude greater values for specific surface areas and total pore volumes, which translated to dramatically higher aqueous interaction and ion release rates. Surprisingly, when immersed in simulated body fluid, HCA conversion was achieved in as little as 3 h for sol-gel derived borate based glasses, demonstrating a 25-fold increase in mineralization rate when compared to melt derived equivalents. The ability of the sol-gel derived borate-based glasses to rapidly convert to bone-like HCA holds promise in numerous potential tissue engineering applications, including the repair and augmentation of mineralized tissues.
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".