Search for inhomogeneous Meissner screening in Nb induced by low-temperature surface treatments
Bibliographic record
Abstract
Empirical surface treatments, such as low-temperature baking (LTB) in a gaseous atmosphere or in vacuum, are important for the surface preparation of Nb superconducting radio frequency cavities. These treatments inhomogeneously dope approximately the first 50 nm of Nb’s subsurface and are expected to impart depth-dependent characteristics to its Meissner response; however, direct evidence supporting this remains elusive, suggesting the effect is subtle. In this work, we revisit the Meissner profile data for several LTB treatments obtained from low-energy muon spin rotation (LE-μSR) experiments [Romanenko et al., Appl. Phys. Lett. 104, 072601 (2014) and McFadden et al., Phys. Rev. Appl. 19, 044018 (2023)] and search for signatures of inhomogeneous field screening. Using a generalized London expression with a recently proposed empirical model for depth-dependent magnetic penetration lengths λ(z), we obtain improved fits to the Meissner data, revealing that the presence of a non-superconducting surface “dead layer” d ≳ 25 nm is a strong indicator of a reduced supercurrent density at shallow subsurface depths. Our analysis supports the notion that vacuum annealing at 120 °C for 48 h induces a depth-dependent Meissner response, which has consequences for Nb’s ability to maintain a magnetic-flux-free state. Evidence of similar behavior from a “nitrogen infusion” treatment is less compelling. Suggestions for further investigation into the matter are provided.
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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 | 0.001 |
| 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".