Ferroelastic ionic organic crystals that self-heal to 95%
Bibliographic record
Abstract
The realm of self-healing materials integrates chemical and physical mechanisms that prevent wear and fracturing and extend the operational lifetime. Unlike the favorable rheology of amorphous soft materials that facilitates efficient contact between fragments, the efficiency of recovery of atomistically ordered materials is restricted by slower interfacial mass transport and the need for ideal physical alignment, which limits their real-world application. We report drastic enhancements in efficiency and recovery time in the self-healing of anilinium bromide, challenging these limitations. Crystals of this material recovered up to 49% within seconds and up to 95% after 100 min via ferroelastic detwinning. The spatial evolution of strain during cracking and healing was measured in real time using digital image correlation. Favorable alignment and strong ionic bonding across the interface of partially fractured crystals facilitate self-healing. This study elevates organic crystals close to the best-in-class self-healing polymers and sets an approach for durable crystal-based optoelectronics. Self-healing in structurally ordered materials is restricted by slow interfacial mass transport and the need for ideal physical alignment. Here the authors show self-healing in an anilinium bromide crystals achieving up to 95% recovery through ferroelastic detwinning.
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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 | 0.001 |
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 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".