Gold Nanoparticles Effect on (Bi,Pb)-2223 Superconducting Thin Films
Bibliographic record
Abstract
<p class="1Body">Superconductor samples were prepared by a conventional solid-state reaction method and systematically studied for their superconducting properties. The Au nanoparticles concentration x varied from 0.0 to 1.0 wt% of the sample's total mass. Those samples were used as targets to prepare Au<sub>x</sub>–Bi<sub>1.7</sub>Pb<sub>0.3</sub>Sr<sub>2</sub>Ca<sub>2</sub>Cu<sub>3</sub>O<sub>10+δ</sub> thin films by pulsed laser deposition (PLD), deposited on Si (111) substrates and post-deposition oxygen annealing have been achieved. The effect of Au nanoparticles (20 nm) on the physical properties of superconducting phase was studied. The phase identification/gross structural characteristics of synthesized bulk and thin films samples explored through powder X-ray diffractometer and reveals that all the samples crystallize in orthorhombic structure In addition, phase examination by XRD indicated that Au nanoparticles enhanced the (Bi,Pb)-2223 phase formation. The critical transition temperature (<em>T<sub>c</sub></em>) measured by the standard DC four-probe method and was found to have optimal value at x=1.0 wt%, which had a maximum enhancement in <em>T<sub>c</sub></em> for both bulk and thin films samples. The surface morphology investigated through scanning electron microscope (SEM) and atomic force microscopy (AFM), the granular investigation showed that both number and size of voids decreased while grains size increased as x increases to 1.0 wt%.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 teacher head, 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".