Properties and Applications of Self-Healing Polymeric Materials: A Review
Bibliographic record
Abstract
Self-healing polymeric materials, engineered to autonomously self-restore damages from external stimuli, are at the forefront of sustainable materials research. Their ability to maintain product quality and functionality and prolong product life plays a crucial role in mitigating the environmental burden of plastic waste. Historically, initial research on the development of self-healing materials has focused on extrinsic self-healing systems characterized by the integration of embedded healing agents. These studies have primarily focused on optimizing the release of healing agents and ensuring rapid self-healing capabilities. In contrast, recent advancements have shifted the focus towards intrinsic self-healing systems that utilize their inherent reactivity and interactions within the matrix. These systems offer the advantage of repeated self-healing over the same damaged zone, which is attributed to reversible chemical reactions and supramolecular interactions. This review offers a comprehensive perspective on extrinsic and intrinsic self-healing approaches and elucidates their unique properties and characteristics. Furthermore, various self-healing mechanisms are surveyed, and insights from cutting-edge studies are integrated.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".