Compatibilization of porphyrins for use as high permittivity fillers in low voltage actuating silicone dielectric elastomers
Bibliographic record
Abstract
Polysiloxanes represent, because of their unusual properties, a material with great potential for use in dielectric elastomers (DEs), a promising class of electroactive polymers. Currently, their application as actuators is limited by the need for high driving voltages, as a result of the low relative permittivity possessed by polysiloxanes (∼2-3). Reducing these voltages can be achieved to some degree by using high permittivity additives to improve the permittivity of the polysiloxane. However, modifying such additives so that they are compatible with, and can be dispersed within, polysiloxane elastomers remains challenging. For reliable actuation, full miscibility is key. In this work the porphyrin 5,10,15,20-(tetra-3-methoxyphenyl)porphyrin (TPMP) was investigated as a high permittivity additive. Its behaviour was compared to the analogue that was derivatized with bis(trimethylsiloxy)methylsilane groups using the Piers-Rubinsztajn reaction to improve compatability with silicone formulations. The derivatized porphyrin was dispersed in elastomers and their dielectric and mechanical properties were evaluated. It was discovered that only low levels of incorporation (1-10%) of the siliconized TPMP - much lower than the parent TPMP - were needed to elicit improvements in the permittivity and electromechanical actuation of the elastomers; actuation strains of up to 43% could be achieved using this method.
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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) | 0.001 | 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".