Hybrid and composite materials of organic crystals
Bibliographic record
Abstract
Organic molecular crystals have historically been viewed as delicate and fragile materials. However, recent studies have revealed that many organic crystals, especially those with high aspect ratios, can display significant flexibility, elasticity, and shape adaptability. The discovery of mechanical compliance in organic crystals has recently enabled their integration with responsive polymers and other components to create novel hybrid and composite materials. These hybrids exhibit unique structure-property relationships and synergistic effects that not only combine, but occasionally also enhance the advantages of the constituent crystals and polymers. Such organic crystal composites rapidly emerge as a promising new class of materials for diverse applications in optics, electronics, sensing, soft robotics, and beyond. While specific, mostly practical challenges remain regarding scalability and manufacturability, being endowed with both structurally ordered and disordered components, the crystal-polymer composite materials set a hitherto unexplored yet very promising platform for the next-generation adaptive devices. This Perspective provides an in-depth analysis of the state-of-the-art in design strategies, dynamic properties and applications of hybrid and composite materials centered on organic crystals. It addresses the current challenges and provides a future outlook on this emerging class of multifunctional, stimuli-responsive, and mechanically robust class of materials.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".