Monitoring colloidal and dissolved decay particles in ester dielectric fluids
Bibliographic record
Abstract
In this article, two synthetic esters along with a natural ester are investigated to monitor the generation of colloidal and dissolved decay particles under thermal aging. The thermal aging of the oils is carried out as per a modified ASTM D1934 in the presence of cellulose and copper at elevated temperatures. Later, the colloidal and dissolved decay particles are monitored as per ASTM D1698 (centrifuge treatment) and ASTM D7150 (Fuller's earth filtration), respectively. The aged and treated dielectric liquids are characterized for the number of decay particles, oil absorbance curves, the concentration of dissolved decay content, and turbidity as per ASTM standards. The evolution of colloidal and dissolved decay contents is individually reported for ester fluids and mineral oils. The degradation in mineral oils has been witnessed by both dissolved and colloidal particles. The generation of colloidal particles following the degradation of ester fluids is almost negligible, whereas dissolved decay content is noticed with thermal degradation time. Importantly, the obtained results depict the ability of esters to dissolve more sludge. This is due to the high (oxidative) stability of ester leading to reduced radical decomposition products. Also, the temperature solvent nature of the ester group tends to dissolve sludge particles back to the liquid phase into the bulk liquid.
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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".