Effect of Adding Ag Nanoparticles onto Magnetic and Structural Properties of BSCCO Superconducting Compound
Bibliographic record
Abstract
Abstract Effect of adding silver nanoparticles on Bi1.7Pb0.3Sr2Ca2Cu3O10+δ superconductor phase by solid state reaction technique. It was studied the significant variations in the superconducting, magnetic and structural properties of Bi-2223 phase. Ag NPs concentration of tiny particles varied, (0 - 1,25) % from the total weight mass of the BPSCCO compound were characterized by X-ray diffraction (XRD) and EDX spectrum measurements. It was found that adding Ag nanoparticles to BSCCO was enhanced the (Bi, Pb)-2223 phase formation up to x=1.25 wt%. On the other hand, the low concentrations of Ag nanoparticles of 0.25 wt% retarded the high-Tc phase formation and (Tc) measured that had the maximum improvement in Tc for most samples. The surface morphology investigated were examined by SEM, which the grain size increased with an increase Ag NPs, while the grain size examination showed that both the size and number of voids were reduced. Magnetization variance was measured of the samples by M - T curve, where the highest value for x = 1.25 wt%, and it become clear when the magnetic and resistance transition points are close to each other. Moreover, Critical current density (Jc ) characteristic curves were measured at temperature range (2-150) K.
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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".