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 (SRF) cavities. These treatments inhomogeneously dope the first $\sim$50 nm of Nb's subsurface and are expected to impart depth-dependent characteristics to its Meissner response; however, direct evidence supporting this remains elusive, suggesting the effect is subtle. In this work, we revisit the Meissner profile data for several LTB treatments obtained from low-energy muon spin rotation (LE-$μ$SR) experiments [A. Romanenko et al., Appl. Phys. Lett. 104, 072601 (2014) and R. M. L. 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 a depth-dependent magnetic penetration depth $λ(z)$, we obtain improved fits to the Meissner data, revealing that the presence of a non-superconducting surface "dead layer" $d \geq 25$ nm is a strong indicator of a reduced supercurrent density at shallow subsurface depths. Our analysis supports the notion that vacuum annealing at 120 $^{\circ}$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".