Enhanced thermal conductivity of spark plasma-sintered thorium dioxide-silicon carbide composite fuel pellets
Bibliographic record
Abstract
Thorium dioxide (ThO2)-silicon carbide (SiC) composite fuel pellets were fabricated via the spark plasma-sintering (SPS) method to investigate the role of the addition of SiC in enhancing the thermal conductivity of ThO2 fuel. SiC particles with an average size of 1μm in 10 and 15 vol% were used to manufacture the composite pellets. The changes in the composites' densification, microstructure and thermal conductivity were explored by comparing them with pure ThO2 pellets. The structural and microstructural characterization of the composite pellets has revealed that SPS could manufacture high-quality composite pellets without having any reaction products or intermetallic. The density measurement by the Archimedes principles and the grain size from the electron back-scattered diffraction (EBSD) analysis has indicated that the composites have higher densities and smaller grain sizes than the pellets without SiC addition. Finally, thermal conductivity as a function of temperature has revealed that sintered ThO2SiC composites showed an increase of up to 56% in thermal conductivity compared to pristine ThO2 pellets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".