The effect of TiO2 on nucleation and crystallization of a Li2O-Al2O3-SiO2 glass investigated by XANES and STEM
Bibliographic record
Abstract
Abstract Glass ceramics based on Li2O/Al2O3/SiO2 are of high economic importance, as they often show very low coefficients of thermal expansion. This enables a number of challenging applications, such as cooktop panels, furnace windows or telescope mirror blanks. Usually, the crystallization of the desired LAS crystal phases within the glasses must be tailored by a careful choice of crystallization schedule and type of nucleation agents to be used. The present work describes the formation of nanocrystalline TiO2 within an LAS base composition that contains solely TiO2 as nucleating agent. Using a combination of scanning transmission electron microscopy as well as X-ray absorption spectroscopy, it is found that a mixture of four- and six-fold coordinated Ti4+ ions exists already within the glass. Heating of the glass to 740 °C immediately changes this ratio towards a high content of six-fold coordinated Ti, which accumulates in liquid-liquid phase-separation droplets. During the course of thermal treatment, these droplets eventually evolve into nanocrystalline TiO2 precipitations, in which Ti4+ is six-fold coordinated. Thus, it is shown that the nucleation of nanocrystalline TiO2 is initiated by a gradual re-arrangement of the Ti ions in the amorphous, glassy matrix, from a four-fold towards a six-fold coordination.
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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) | 0.002 | 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".