Proximity effect and Josephson current in clean strong/weak/strong superconducting trilayers
Bibliographic record
Abstract
Recent measurements of the Josephson critical current through LSCO/LCO/LSCO thin films showed an unusually large proximity effect. Using the Bogoliubov--de Gennes equations for a tight-binding Hamiltonian we describe the proximity effect in weak links between a superconductor with critical temperature ${T}_{c}$ and one with critical temperature ${T}_{c}^{\ensuremath{'}}$, where ${T}_{c}>{T}_{c}^{\ensuremath{'}}$. The weak link $({N}^{\ensuremath{'}})$ is therefore a superconductor above its own critical temperature and the superconducting regions are considered to have either $s$-wave or $d$-wave symmetry. We note that the proximity effect is enhanced due to the presence of superconducting correlations in the weak link. The dc Josephson current is calculated, and we obtain a nonzero value for temperatures greater than ${T}_{c}^{\ensuremath{'}}$ for sizes of the weak links that can be almost an order of magnitude greater than the conventional coherence length. Considering pockets of superconductivity in the ${N}^{\ensuremath{'}}$ layer, we show that this can lead to an even larger effect on the Josephson critical current by effectively shortening the weak link.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".