Investigation of The Dielectric Properties Of (Cu, Tl) 1234 Added with Graphene
Bibliographic record
Abstract
Superconducting samples of type (Cu0.5Tl0.5)-1234 were prepared at normal pressure via one step of conventional solid state reaction technique, and added with x wt.% of graphene (0≤x≤0.1). X-ray powder diffraction (XRD) shows that the addition of graphene does not alter the tetragonal structure and the lattice parameters of the samples. Fourier Transform Infrared (FTIR) measurements indicate the non-uniform distribution of graphene inside the (Cu0.5Tl0.5)-1234 phase. While the Scanning Electron Microscope (SEM) images reveal the increase of the inter-grain connectivity. The superconducting transition temperature Tc, obtained from Ac magnetic susceptibility measurements, shows an enhancement up to x=0.04 Wt. %, followed by a deterioration for x >0.04. The frequency dependence of the dielectric properties of the samples was experimentally investigated at different temperatures (110 K ≤ T ≤ 260 K). The real and imaginary parts (ε' and ε'') of dielectric constant increase up to x=0.1. The study of ε' and ε'', the dielectric loss (tan δ) and the ac-conductivity (σac) have shown that the variation of frequency, temperature and addition of graphene allows the tuning of these parameters for various applications.
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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".