Impact of Seawater Immersion of (Zn0.5Ni0.5Fe2O4)x(Bi, Pb)-2223 Composites
Bibliographic record
Abstract
This work investigated the effects of adding Zn0.5Ni0.5Fe2O4 nanoparticles in (Bi, Pb)-2223 superconductor. The conventional solid-state reaction method was used to create (Zn0.5Ni0.5Fe2O4)x(Bi, Pb)-2223 composites (0.00 ≤ x < 0.40 wt. %). X-ray diffraction (XRD) revealed the main phase of the tetragonal (Bi, Pb)-2223. The morphology and elemental contents of the produced samples were investigated using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX). When compared to the pure (Bi, Pb)-2223 sample, EDX verified that adding Zn0.5Ni0.5Fe2O4 to the superconductor improved the adsorption of saltwater components. Vickers microhardness (Hv) was measured at room temperature for 30 seconds with different applied forces (0.49 to 9.80 N) and different durations of the saltwater immersion (2, 6, 12, and 24 hours). Hv increased with increasing the immersion time in seawater from 2 to 24 hours. An optimum improvement (69.08%) was obtained for an addition of 0.04 wt. % of Zn0.5Ni0.5Fe2O4, where Hv values increased from 0.524 GPa to 0.886 GPa. With a deviation of less than 5%, the indentation-induced cracking (IIC) model provided the best theoretical analysis at the plateau limit region for measurements made before and after immersion in seawater.
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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".