Stretchable Self-Healing Polymeric Dielectrics Cross-Linked Through Metal–Ligand Coordination
Bibliographic record
Abstract
A self-healing dielectric elastomer is achieved by the incorporation of metal-ligand coordination as cross-linking sites in nonpolar polydimethylsiloxane (PDMS) polymers. The ligand is 2,2'-bipyridine-5,5'-dicarboxylic amide, while the metal salts investigated here are Fe(2+) and Zn(2+) with various counteranions. The kinetically labile coordination between Zn(2+) and bipyridine endows the polymer fast self-healing ability at ambient condition. When integrated into organic field-effect transistors (OFETs) as gate dielectrics, transistors with FeCl2 and ZnCl2 salts cross-linked PDMS exhibited increased dielectric constants compared to PDMS and demonstrated hysteresis-free transfer characteristics, owing to the low ion conductivity in PDMS and the strong columbic interaction between metal cations and the small Cl(-) anions which can prevent mobile anions drifting under gate bias. Fully stretchable transistors with FeCl2-PDMS dielectrics were fabricated and exhibited ideal transfer characteristics. The gate leakage current remained low even after 1000 cycles at 100% strain. The mechanical robustness and stable electrical performance proved its suitability for applications in stretchable electronics. On the other hand, transistors with gate dielectrics containing large-sized anions (BF4(-), ClO4(-), CF3SO3(-)) displayed prominent hysteresis due to mobile anions drifting under gate bias voltage. This work provides insights on future design of self-healing stretchable dielectric materials based on metal-ligand cross-linked polymers.
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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".