Effect of electromagnetic permeability on transition temperature of superconductivity
Bibliographic record
Abstract
The description of superconductivity at high-temperature is a problem that has recently been addressed. Transition temperature of superconductivity, [Formula: see text], depends on the lattice structure type, size and room pressure. In super-lattices and low-dimensional layered nanostructures, [Formula: see text] is increased by increasing the complexity of the structure and internal pressures in solid lattice. In this paper, we investigate the relation between physical parameters ([Formula: see text]) of matter and superconductivity properties as well as transition temperature ([Formula: see text]), and explain the superconductivity at high-temperature. In this study, a semi-classical electromagnetic description along with vortex topologic theory and quantum dynamic models with experimental data is considered to justify the relation between superconductivity phenomena and magnetic monopole properties. We find that the electromagnetic energy of magnetic monopole is in agreement with vortex energy in topological theory and it can get close to thermal energy at high-temperature. These models suggest that the superconductivity is related to the mobile monopole or vortices. We show that the electrical permittivity ([Formula: see text]) and magnetic permeability ([Formula: see text]) of matter have a key role in the superconductive properties.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".