Depth-Resolved Measurements of the Meissner Screening Profile in Surface-Treated <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><mml:mi>Nb</mml:mi></mml:math>
Bibliographic record
Abstract
We report depth-resolved measurements of the Meissner screening profile in several surface-treated $\mathrm{Nb}$ samples using low-energy muon spin rotation. In these experiments, implanted positive muons, whose stopping depths below $\mathrm{Nb}$'s surface are adjusted between approximately $10\phantom{\rule{0.2em}{0ex}}\mathrm{nm}$ and $150\phantom{\rule{0.2em}{0ex}}\mathrm{nm}$, reveal the field distribution inside the superconducting element via their spin precession (communicated through their radioactive decay products). We compare how the field screening is modified by different surface treatments commonly used to prepare superconducting radio-frequency cavities used in accelerator beamlines. In contrast to an earlier report [A. Romanenko et al., Appl. Phys. Lett. 104, 072601 (2014)], we find no evidence for any ``anomalous'' modifications to the Meissner profiles, with all data being well described by a London model. Differences in screening properties between surface treatments can be explained by changes to the carrier mean free paths resulting from dopant profiles near the material's surface.
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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) | 0.002 | 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".