Progress in Fabrication and Applications of Cholesteric Liquid Crystal Microcapsules
Bibliographic record
Abstract
Liquid crystals (LCs) are well known for inherent responsiveness to external stimuli, such as light, thermal, magnetic, and electric fields. Cholesteric LCs are among the most fascinating, since they possess distinctive optical properties due to the helical molecular orientation. However, the good flow, easy contamination, and poor stability of small-molecule LCs limit their further applications, and microencapsulation as one of the most effective tools can evade these disadvantages. Microencapsulation can offer shell-core structure with LCs in the core can strengthen their stability, avoiding interference with the environment while maintaining the stimuli-responsiveness and optical properties. Here, we report recent progress in the fabrication and applications of cholesteric LC microcapsules (CLCMCs). We summarize general properties and basic principles, fabrication methods including interfacial polymerization, in-situ polymerization, complex coacervation, solvent evaporation, microfluidic and polymerization of reactive mesogens, and then give a comprehensive overview of their applications in various popular domains, including smart fabrics, smart sensor, smart displays, anti-counterfeiting, information encryption, biomedicine and actuators. Finally, we discuss the currently facing challenges and the potential development directions in this field.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 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".