Investigation of Multicomponent Fluoridated Borate Glasses through a Design of Mixtures Approach
Bibliographic record
Abstract
Due to their enhanced dissolution, solubility and reaction speed, borate glasses offer potential advantages for the design and development of therapeutic ion-release systems. However, the field remains poorly understood relative to traditional phosphosilicate and silicate bioglasses. The increased structural complexity and relative lack of published data relating to borates, particularly borofluorates, also decreases the accuracy of artificial intelligence models, which are used to predict glass properties. To develop predictive models for borofluorate networks, this paper uses a design of mixtures approach for rapid screening of composition–property relationships, including the development of polynomial equations that comprehensively establish the predictive capabilities for glass transition, density, mass loss and fluoride release. A broad range of glass compositions, extending through the boron anomaly range, were investigated, with the inclusion of 45 to 95 mol% B2O3 along with 1–50 mol% MgO, CaO and Na2O as well as 1–30% KF and NaF. This design space allows for the investigation of the impact of fluorine as well as mixed alkali–alkaline earth effects. Glass formation was found to extend past 30 mol% KF or NaF without a negative impact on glass degradation in contrast to the trends observed in phosphosilicates. The data demonstrates that fluoroborate materials offer an exceptional base for the development of fluoride-releasing materials.
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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 | 0.000 | 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) | 0.000 | 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".