Thermal Conductivity of the Iron-Based Superconductor FeSe: Nodeless Gap with a Strong Two-Band Character
Bibliographic record
Abstract
The thermal conductivity $\ensuremath{\kappa}$ of the iron-based superconductor FeSe was measured at temperatures down to 75 mK in magnetic fields up to 17 T. In a zero magnetic field, the electronic residual linear term in the $T=0\text{ }\text{ }\mathrm{K}$ limit, ${\ensuremath{\kappa}}_{0}/T$, is vanishingly small. The application of a magnetic field $B$ causes an exponential increase in ${\ensuremath{\kappa}}_{0}/T$ initially. Those two observations show that there are no zero-energy quasiparticles that carry heat and therefore no nodes in the superconducting gap of FeSe. The full field dependence of ${\ensuremath{\kappa}}_{0}/T$ has the classic two-step shape of a two-band superconductor: a first rise at very low field, with a characteristic field ${B}^{\ensuremath{\star}}\ensuremath{\ll}{B}_{c2}$, and then a second rise up to the upper critical field ${B}_{c2}$. This shows that the superconducting gap is very small (but finite) on one of the pockets in the Fermi surface of FeSe. We estimate that the minimum value of the gap, ${\mathrm{\ensuremath{\Delta}}}_{\mathrm{min}}$, is an order of magnitude smaller than the maximum value, ${\mathrm{\ensuremath{\Delta}}}_{\mathrm{max}}$.
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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".