Investigation of CeTi<sub>2</sub>O<sub>6</sub>- and CaZrTi<sub>2</sub>O<sub>7</sub>-containing glass–ceramic composite materials
Bibliographic record
Abstract
Glass–ceramic composite materials are being investigated for numerous applications (i.e., textile, energy storage, nuclear waste immobilization applications, etc.) due to the chemical durability and flexibility of these materials. Borosilicate and Fe–Al–borosilicate glass–ceramic composites containing brannerite (CeTi2O6) or zirconolite (CaZrTi2O7) crystallites were synthesized at different annealing temperatures. The objective of this study was to understand the interaction of brannerite or zirconolite-type crystallites within the glass matrix and to investigate how the local structure of these composite materials changed with changing synthesis conditions. Powder X-ray diffraction (XRD) and Backscattered electron (BSE) microprobe images have been used to study how the ceramic crystallites dispersed in the glass matrix. X-ray absorption near edge spectroscopy (XANES) spectra were also collected from all glass–ceramic composite materials. Examination of Ti K-, Ce L3-, Zr K-, Si L2,3-, Fe K-, and Al L2,3-edge XANES spectra from the glass–ceramic composites have shown that the annealing temperature, glass composition, and the loading of the ceramic crystallites in the glass matrix can affect the local environment of the glass–ceramic composite materials. A comparison of the glass–ceramic composites containing brannerite or zirconolite crystallites has shown that similar changes in the long range and local structure of these composite materials occur when the synthesis conditions to form these materials or the composition are changed.
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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) | 0.001 | 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".