Electrophoretic Deposition and Physicochemical Characterization of Al2O3-NiO Composite Materials
Bibliographic record
Abstract
Al2O3-NiO composites were fabricated by electrophoretic deposition (EPD). Suspensions were prepared from the mixture of Al2O3 and NiO powder in a mixed ethanol-water electrolyte and were stabilized with acetic acid. The effects of the NiO content, sintering temperature and the deposition parameters such as the EPD applied voltage and deposition time on the coating weight, microstructure and crystallinity of final composites were studied. Due to the positive charge of Al2O3 and NiO in suspension in the mixed ethanol-water solvent, samples were deposited on the cathode and they were not affected by the possible solvent electrolysis. Coatings with defect-free microstructure were then obtained. These investigations have revealed that the deposits weight gain increases linearly with NiO content and theirs thickness was controlled by adjusting the NiO content and/or the time of the deposition. By increasing NiO content, the distribution of NiO nano-particles in Al2O3 matrix became more homogenous forming thereby an interconnected network like-microstructure which inhibits the grain growth of Al2O3. This illustrates clearly the advantage of EPD for the synthesis of Al2O3-NiO particles. XRD studies have revealed that apart from Al2O3 and NiO, a cubic nickel spinel (NiAl2O4) like phase is formed in the Al2O3-NiO composite. The increase of the applied voltage has resulted in a serious reduction of the deposit weight gain and also to the degradation of their microstructures particularly when the NiO content is lower than 40%.
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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 | 0.001 | 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".