Natural Ester Blended with Dielectric Nanoparticles: A Promising Solution to Sustainable Development Threat
Bibliographic record
Abstract
Poor biodegradability and sustainability of mineral oil-based products are threats to “the world we want”. Spillage of mineral oil from electricity networks is a contribution to the threats to sustainable development. This led to the quest for a sustainable alternative insulating oil. This work uses dielectric nano-additives to enhance the cooling and insulating properties of methyl ester from neem oil. SiO2and Al2O3nanoparticle of the same particle size was used as the additives. The particles were characterized by SEM-EDX to know the surface morphology and the elemental composition of the nanoparticles. The oil was transesterified using NaOH as the catalyst. The viscosity of the base fluid and nanofluids was found to be lower relative to the viscosity of mineral oil. The addition of nanoparticles to the base oil reduces the leakage current and consequently, increases the dielectric breakdown of the liquid. The dielectric breakdown voltage of SiO2Nanofluid was observed to be better than the dielectric breakdown voltage of Al2O3nanofluids. The optimum DC breakdown voltage of 31.4 kV was observed for SiO2nanofluid at 0.5 wt% and 29.5 kV for Al2O3nanofluid at 0.6 wt%. It can be deduced from the results that both nanofluids are promising alternative insulating oil to mineral oil with an outstanding performance for SiO2nanofluid
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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 | 0.001 |
| 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".