A magneto-optic investigation of flux penetration in high temperature superconductors
Bibliographic record
Abstract
An efficient non-destructive magneto-optic imaging system was developed that employs special iron-garnet indicator films to image magnetic fields in high temperature superconductors. The flexibility of the system allows the investigation of many different types of samples having different sizes and characteristics. Two very high quality, low pinning, thin, flat YBa₂Cu₃O₆.₉₅ single crystals are imaged using this system to observe field penetration dynamics. Slightly different pinning characteristics of both samples enable a comparison to be made between the different flux penetration dynamics observed. In one ultra low pinning crystal, penetrating flux is found to accumulate in the sample's central regions while avoiding the inner edges and in the other higher pinning crystal flux tends to initially avoid both the central regions and sample's inner edges. These results compare favourably with a theoretical model of Zeldov et al.[21] that characterizes penetration phenomenon in low pinning superconductors. Field penetration of a Tl₂Ba₂CaCu₂ thin film is also observed for different sample temperatures up to the sample's superconducting transition temperature. A new computational technique is employed to calculate the critical current of the film using imaged field strengths. A comparison of the imaged fields and calculated currents is made with a theoretical model devised to characterize field penetration in superconductors having a thin circular geometry. From these results the critical current temperature dependence is established and is fitted to a simple phenomenon logical model. A small discontinuity in the data 10 K below the sample's transition temperature is also noted and is examined.
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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".