Manipulation of Liquid Crystalline Properties by Dynamic Covalent Chemistry─En Route to Adaptive Materials
Bibliographic record
Abstract
Dynamic covalent bonds bear great potential for the development of adaptive and self-healing materials. Herein, we introduce a versatile concept not only for the design of low-molecular-weight liquid crystals but also for their in situ postsynthetic modification by using the dynamic covalent nature of imine bonds. The methodology allows systematic investigations of structure–property relationships as well as the manipulation of the materials’ behavior (liquid crystallinity) and the introduction of additional properties (here, fluorescence) by a solvent-free method. For the first time, the transamination reaction is followed by variable-temperature 19F solid-state NMR in the mesophase, providing insights into the reaction dynamics in a liquid crystalline material. Finally, the application potential for the design of liquid crystalline materials with adaptive properties is demonstrated by a sequential combination of these reactions.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.000 |
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 teacher head, 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".