Durable Microstructured Surfaces: Combining Electrical Conductivity with Superoleophobicity
Bibliographic record
Abstract
In this study, electrically conductive and superoleophobic polydimethylsiloxane (PDMS) has been fabricated through embedding Ag flakes (SFs) and Ag nanowires (SNWs) into microstructures of the trichloroperfluorooctylsilane (FDTS)-blended PDMS elastomer. Microstructured PDMS surfaces became conductive at the percolation surface coverage of 3.0 × 10(-2) mg/mm(2) for SFs; the highest conductivity was 1.12 × 10(5) S/m at the SFs surface coverage of 6.0 × 10(-2) mg/mm(2). A significant improvement of the conductivity (increased 3 times at the SNWs fraction of 11%) was achieved by using SNWs to replace some SFs because of the conductive pathways from the formed SNWs networks and its connections with SFs. These conductive fillers bonded strongly with microstructured FDTS-blended PDMS and retained surface properties under the sliding preload of 8.0 N. Stretching tests indicated that the resistance increased with the increasing strains and returned to its original state when the strain was released, showing highly stretchable and reversible electrical properties. Compared with SFs embedded surfaces, the resistances of SFs/SNWs embedded surfaces were less dependent on the strain because of bridging effect of SNWs. The superoleophobicity was achieved by the synergetic effect of surface modification through blending FDTS and the microstructures transferred from sand papers. The research findings demonstrate a simple approach to make the insulating elastomer to have the desired surface oleophobicity and electrical conductivity and help meet the needs for the development of conductive devices with microstructures and multifunctional properties.
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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".