Effect of BaFe12O19 Nanoparticles Addition on (Bi,Pb)-2223 Superconducting Phase
Bibliographic record
Abstract
The aim of this work is to investigate the effect of BaFe12O19 nanoparticles on the microstructure, phase formation and mechanical properties of (Bi,Pb)-2223 superconducting phase. Co-precipitation and solid-state reaction techniques were used to synthesize BaFe12O19 nanoparticles and (BaFe12O19)x(Bi,Pb)-2223 superconducting samples with , respectively. BaFe12O19 nanoparticles and (BaFe12O19)x(Bi,Pb)-2223 structures were performed using X-ray diffraction. The morphology of BaFe12O19 nanoparticles and (BaFe12O19)x(Bi,Pb)-2223 were observed by means of transmission electron microscope (TEM) and scanning electron microscope (SEM), respectively. The experimental results reveal the composition of Bi-2223 phase and traces of Bi-2212 as a secondary phase when compared to the undadded sample. Lattice parameters are not altered with BaFe12O19 addition which indicate that nanoparticles do not enter the host crystal of (Bi,Pb)-2223. Vickers microhardness Hv is measured as function of indentation load and time. It was found that microhardness has a non-linear trend with applied load and time. The experimental results were analyzed using different models. The analysis revealed that the HK model was more suitable than the other approaches in estimating the load independent hardness of the samples.
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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) | 0.002 | 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".