Enhancement of Critical Current Density of Yttrium Barium Copper Oxide Thin Films by Introducing Nano dimensional Cerium Oxide Defects
Bibliographic record
Abstract
The critical current density, Jc has been the most important parameter used in the design and engineering of effective devices which is one of the implementation of high temperature superconductors (HTSC). In this work, an effort has been made to further improve the critical current density of YBa2Cu3O7-x (YBCO) thin films by preventing the magnetic flux line lattice against the Lorentz force by pinning it in place with the aid of nano-dimensional defects. These defects were generated by distributing nano sized CeO2 islands after YBCO layer was created on LaAlO3 substrates perpendicular to the film using pulsed laser deposition (PLD) technique. Three samples with buffer layers of CeO2 were prepared. CeO2 with 50 pulses, 100 pulses and 150 pulses, after each 1000 pulses of YBCO were prepared five layers for each of the samples. The structural characterization of YBCO/CeO2 and YBCO pristine films were carried out using x-ray diffraction (XRD) and scanning electron microscopy (SEM). Superconducting proprieties were measured using a vibrating sample magnetometer (VSM). Jc for the pure YBCO and the YBCO/CeO2 films were calculated from magnetization (M) versus Field (H) loops using Bean’s model. Jc for the 50 pulses of YBCO/CeO2 films was found to be increased slightly by an order of magnitude of about 40% with respect to those of YBCO films without the nano dimensional defects.
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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) | 0.001 | 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".