AC Dielectric Strength of Mineral Oil-Based Fe<sub>3</sub>O<sub>4</sub> and Al<sub>2</sub>O<sub>3</sub> Nanofluids
Bibliographic record
Abstract
This paper deals with experimental study of the influence of conductive (Fe3O4) and insulating (Al2O3) nanostructured particles at various concentrations on the dielectric strength of transformer mineral oil. The method of preparation and characterization of these nanofluids (NFs) through the measurements of zeta potential, the real and imaginary parts of dielectric constant as well as the concentration and size of nanoparticles using Scanning Electron Microscope (SEM) images of nanoparticles powders and Dispersive x-ray Spectroscopy (EDS) analysis are presented. Experimental findings reveal that these two types of nanoparticles materials significantly improve AC breakdown voltage and the magnitude of this improvement depends on the concentration, size and nature (material) of nanoparticles. For a given type of nanoparticles, the effect is more marked with the smallest nanoparticles. The conductive nanoparticles offer higher enhancement of dielectric strength compared with insulating nanoparticles based nanofluids. With Fe3O4, the breakdown voltage (BDV) can exceed twice that of mineral oil and it increases by more than 76% with Al2O3. The physicochemical mechanisms implicated in this improvement are discussed.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.010 | 0.012 |
| Meta-epidemiology (broad) | 0.010 | 0.005 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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; both teacher heads agree on what is shown here.
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".