Bibliographic record
Abstract
The performance of Niobium (Nb) superconducting radio frequency (SRF) cavities is extremely sensitive to defects near the surface. These imperfections include dislocations, interstitial solutes, or grain boundaries which have been shown to limit the material’s ability to expel magnetic flux during the transitions to its superconducting state. This gives rise to detrimental thermal effects that limit the quality factor, and hence performance, of an SRF cavity. However, beside its importance in the performance of SRF cavities, little is known about the effect of annealing on the distribution of dislocations, specially near the surface. In this work, a mixed multiscale computational and experimental approach is developed to study the evolution dislocation density in pure Nb samples during annealing. The model is based on a Discrete Dislocation Dynamics (D.D.D.) technique that couples glide and climb motion of edge dislocations to simulate the evolution of dislocation density within the bulk of a Nb substrate as well as near the surface. The D.D.D. model integrates information from multiple scales. Accelerated molecular dynamics simulations were used to calculate the required activation energies for vacancy migration and hence fed our dislocation climb constitutive laws. Finite element modelling and experimental techniques were used as inputs to the D.D.D. implementation which accounted for the presence of residual stresses for both kink-pair nucleation dominated glide motion and vacancy migration driven climb. The model showed good agreement with other empirical methods. Furthermore, experimental results showed that the resistance of Nb samples at temperatures slightly above the critical temperature (T = 10K) can be coupled to the dislocation density. Hence, together the D.D.D. framework provided herein, can serve as a useful tool to determine an optimal annealing recipe for a given initial state of material condition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".