How phase (α and γ) and porosity affect specific heat capacity and thermal conductivity of thermal storage alumina
Bibliographic record
Abstract
Abstract Ceramic materials are a potential medium to store thermal energy with a reasonable cost. Respective thermodynamic properties of ceramics generally depend on temperature, and the energy storage capacity significantly varies with microstructure and porosity of ceramics. In order to improve understanding on the correlation between microstructure change and energy storage capacity, two commercial grades of alumina specimens are characterized. Their thermo‐mechanical properties are measured and correlated with temperature‐dependent material phases (ie, α and γ phases) and porosity. Higher values of the γ phase fraction and the porosity result in a lower mass‐based specific heat capacity when the temperature changes from room temperature to 1200°C. On the other hand, lower values of the γ phase fraction and the porosity lead to higher values of thermal conductivity and diffusivity between room temperature and 900°C. While both alumina specimens exhibit a decrease in specific heat capacity with increasing temperature for temperatures above 590°C, largely due to the phase transformation from γ to α, they both exhibit a decrease in thermal conductivity with increasing temperature in the same range. Generally a sample with a higher fraction of α phase and a lower porosity possesses a higher thermal conductivity. Quantitative relations are derived from experimental data.
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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 | 0.001 |
| 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".