Electronic Transport of Uranium Mononitride
Bibliographic record
Abstract
We investigated the electronic heat capacity, thermal conductivity, and resistivity of UN using Quantum Espresso and EPW code. GGA, PBEsol functional was used. The calculated electronic heat coefficient was found to be significantly reduced (0.0176 J⋅mol-1⋅K-2 versus 0.0006 J⋅mol-1⋅K-2) when the non-local hybrid functional (B3LYP) was used. Furthermore, we calculated electrical resistivity using a very transparent Ziman’s formula for metals with the Eliashberg transport coupling function as implemented in EPW code for non-spin-polarized calculations. The number of mobile electrons in UN, as a function of temperature, was derived from the ratio of the calculated resistivity and available experimental data. The electronic thermal conductivity was evaluated from the calculated electronic resistivity via Wiedemann-Franz law with the number of mobility electrons (nav) incorporated (averaged over the temperature range 300 K - 1000 K). Both the electronic thermal conductivity and resistivity, as calculated using newly evaluated nav, compare well with experimental data at ~700 K, but to reproduce the observed trend as a function of temperature, the number of mobile electrons must decrease with the temperature as evaluated.
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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) | 0.001 | 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".