Experimental investigation of dielectric properties on electrorheological fluids
Bibliographic record
Abstract
A home-made electrorheological (ER) fluid, known as ETSERF, has been created with suspension-based powders dispersed in silicon oil. Because of the special structure of their particles, ETSERF suspensions present a complex behavior. In the absence of an electric field, an ETSERF fluid will manifest a near-Newtonian behavior, but when an electric field is applied, it exhibits a pseudoplastic behavior with yield stress. The ER effect under DC electric fields has been experimentally investigated using both hydrous and anhydrous ER fluids. ER properties are strongly dependent on the dielectric properties of ETSERF suspensions and hydrous ER fluids have a high dielectric constant and a high relaxation frequency which show a strong electrorheological effect. The relationship between the electrorheological effect and the permittivity of ER fluids has also been extensively studied. Experimental results show that the interfacial polarization plays an important role in the electrorheological phenomenon. The ageing of ETSERF fluids was also studied and it was found that the dielectric properties (mainly the dielectric loss tangent) and ER properties are strongly related to the duration of ageing. A fresh ETSERF suspension exhibits a high relaxation frequency and a high dielectric constant. These results are mainly explained by the effect of interfacial polarizations.
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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) | 0.001 | 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".