Impact of Surfactant in Enhancing Electrostatic Stability and Dielectric Response of Mixed Oil-Based AlN Nanofluids Under Impulse Conditions
Bibliographic record
Abstract
This study investigates the synergistic effects of Aluminium Nitride (AlN) nanoparticles and cetyltrimethylammonium bromide (CTAB) surfactant on the performance of mixed insulating oil composed of 20% rapeseed oil and 80% mineral oil. A Taguchi-based grey relational analysis is utilised to identify the optimal nanoparticle-surfactant concentration based on various parameters. The identified optimal nanofluid is further validated using the Derjaguin-Landau-Verwey-Overbeek (DLVO) theory to confirm the stable suspension phenomenon. The DLVO-based total interaction potential energy indicated a higher potential barrier at the optimized concentration of 50 mg/L AlN and 1 mg/L CTAB, supporting enhanced nanofluid stability. The degradation behaviour of nano fluid studied under standard impulse current (standard 8/20μs waveform) was studied. Simultaneously, optical emission spectroscopy (OES) was employed during impulse application to capture real-time spectral changes. The results demonstrate that the nanofluid exhibits enhanced suspension stability, reduced acidity and improved resistance to electrical degradation. The negative polarity impulses induce a more pronounced destabilizing effect compared to positive impulses, emphasizing the asymmetric impact of surge conditions on nanofluid performance. This work highlights the critical role of nanoparticle-surfactant interactions in advancing the reliability of transformer insulating systems.
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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 | 0.001 |
| 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) | 0.000 | 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".