First-principles-computational quantum insights on enhanced thermophysical performance of ThC:Mg for clean thermoelectric and nuclear energy
Bibliographic record
Abstract
Besides being a matured energy technology, nuclear energy is the second cleanest energy source after hydropower regarding the emission of greenhouse gases. As such, the role of nuclear energy as a key player to achieve sustainable clean energy to solve the future energy crisis cannot be underestimated. To harness the nuclear energy via the fission process, the routine fuel materials in the nuclear power plants are uranium and uranium-based compounds. However, thorium-based materials have some advantages for advanced breeder power plants. This owes to the abundance, peculiar mechanical, and thermodynamic properties of thorium (Th), such as high melting temperature (1750 °C), density, and thermal conductivity, and less radioactive byproducts. Th makes many refractory materials with melting points above 1800°C, which include carbides, nitrides, phosphides, and silicides, holding promising potential for diverse applications such as clean thermoelectric and nuclear energy. This study is the first attempt to explore comparative analysis on the phonon dynamics, thermodynamic, and thermoelectric performance and potential of ThC and Mg-doped ThC carbides using density-functional theoretical formalism. For the first-principle quantun insights and computation of thermodynamic characteristics of the materials, the Debye Model based on the Quasi Harmonic approximations is utilized. The computed results are interpreted considering novel prospects and implications, which hold great potential for fundamental and practical applications regarding thermal management and sustainable thermoelectric and clean nuclear energy via advanced breeder power plants.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".