Recent Progress on Self‐Healable Conducting Polymers
Bibliographic record
Abstract
Materials able to regenerate after damage have been the object of investigation since the ancient times. For instance, self-healing concretes, able to resist earthquakes, aging, weather, and seawater have been known since the times of ancient Rome and are still the object of research. During the last decade, there has been an increasing interest in self-healing electronic materials, for applications in electronic skin (E-skin) for health monitoring, wearable and stretchable sensors, actuators, transistors, energy harvesting, and storage devices. Self-healing materials based on conducting polymers are particularly attractive due to their tunable high conductivity, good stability, intrinsic flexibility, excellent processability and biocompatibility. Here recent developments are reviewed in the field of self-healing electronic materials based on conducting polymers, such as poly 3,4-ethylenedioxythiophene (PEDOT), polypyrrole (PPy), and polyaniline (PANI). The different types of healing, the strategies adopted to optimize electrical and mechanical properties, and the various possible healing mechanisms are introduced. Finally, the main challenges and perspectives in the field are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".