Surface Properties and Reactivity of Phosphate-based Glasses by Inverse Gas Chromatography and Dynamic Vapour Sorption
Bibliographic record
Abstract
Abstract The chemical durability of phosphate-based glasses (PGs) in an aqueous environment is crucial in determining their dissolution properties and their ultimate performance in vivo. In this study, inverse gas chromatography (IGC) and dynamic vapour sorption (DVS)were used to investigate the short-term aqueous interactions of PG particles doped with SiO2 and TiO2 (50P2O5-40CaO-xSiO2- (10-x)TiO2, where x=7, 5, 3, and 0 mol%). IGC was used to evaluate the solubility parameter and surface energy of PGs. A good correlation between the polar parts of the solubility parameter and surface energy with glass transition temperature (Tg) and dissolution rates was demonstrated. DVS was applied to monitor the sorption characteristics of the PG particles. An increase in silica content resulted in greater vapour sorption and mass change. Nuclear magnetic resonance spectroscopy data of the PGs post exposure to vapour demonstrated that increased SiO2 content disrupted the glass network and formed protonated phosphate species. Fourier transform infrared spectroscopy verified the presence of non-reacted water molecules in the PGs depending on SiO2 content. Moreover, there was a good correlation between the values measured through IGC and DVS, demonstrating the ability of both techniques in predicting the dissolution properties of PGs as consequence of alterations in their chemistry.
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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) | 0.000 | 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 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".