Elastic softening of metamict titanite CaTiSiO5: Radiation damage and annealing
Bibliographic record
Abstract
We have measured the elastic response of radiation-damaged titanite, CaTiSiO 5 , as a function of thermal annealing. We estimate the bulk modulus of the damaged samples (-24% amorphous) to be 85 GPa, which is much softer than for undamaged crystalline titanite [131.4 GPa; Angel et al. (1999)]. Conversely, the lowest shear modulus of the radiation-damaged material is 52-58 GPa, which is harder that of the undamaged titanite, 46-52 GPa. The bulk and shear moduli of the radiation-damaged materials are close to those of thermal titanite glass, B glass ≈ 75 GPa and G glass ≈ 47 GPa, and are much smaller than expected based on other radiation-damaged materials such as zircon (ZrSiO 4 ). Surprisingly, annealing of the damaged titanite in the range 600 < T < 1000 K leads to additional massive softening of the shear moduli. During annealing the shear modulus of titanite sample 1 softened from 58 to 29 GPa, and sample 2 softened from 52 to 19 GPa. The temperature range for the softening coincides with that found for crystallization of the amorphous regions, as measured previously by diffraction and spectroscopic methods. In contrast to the huge softening of the ultrasonically measured shear modulus, the calorimetrically measured Debye temperature θ D increases by -5%, suggesting a small intrinsic hardening of the acoustic shear modes. Additional heating to 1473 K leads, in one titanite sample, to a steep increase of the shear modulus to values much larger than that of the initial, radiation-damaged material. Theoretical models are discussed to rationalize the massive softening due to both radiation damage and subsequent anneal.
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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".