Flame‐Retardant Self‐Healing Polymers: A Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Developing multifunctional flame retardants (FRs) has become a strategy to reply on needs for advanced polymers. Self‐healing polymers are an emerging class of advanced polymeric materials, which have been upgraded progressively, and recently have taken the advantage of fire safety. Correspondingly, diverse industries like aerospace, automotive, construction and consumer electronics are benefited from flame‐retardant self‐healing polymeric materials, which underlines their increasing contribution to modern technologies. The self‐healing characteristics stem from intricate chemical and physical interactions, adopting self‐directed repair mechanisms leading to eliminating the need for frequent replacements, subsequently lowering maintenance costs and environmental impact. This review summarizes advantages of self‐healing polymers with emphasis on exploring highly innovative advancements among bio‐based hydrogels, aerogels, coatings, thin films, lithium‐ion batteries and advanced ionotronic skin (‐i‐skin) structures embedding sensing features for smoke detection and flame exposure warnings, further broadening their application in smart technologies and safety‐critical infrastructure. The outcomes of reports outline challenges remaining in developing such multifaceted materials in view of lack of information due to limited or exclusive investigations. However, further research may facilitate exploring dehydration, thermal shielding, and free radical quenching mechanisms contributing to flame retardancy performance of flame‐retardant self‐healing polymers. Sustainability and circular economy requirements are briefly discussed, in addition to outlining remarks on future developments.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it