Photoresponsive Chiral Liquid Crystal Materials: From 1D Helical Superstructures to 3D Periodic Cubic Lattices and Beyond

  • Yannian Li
  • Quan Li
Part of the NanoScience and Technology book series (NANO)


Stimuli-responsive self-organized chiral architectures with functional properties hold great promise in design and fabrication of smart soft materials. Chiral liquid crystals (LCs) represent such a promising class of materials due to their unique chiral superstructures and properties as a result of chirality transfer from molecular level to macroscopic liquid crystalline phases. Introducing photochromic molecules into chiral LCs results in self-organized superstructures whose properties can be tuned by light and therefore open the door for their applications in several new directions. For example, the helical superstructure of chiral nematic phase can reflect light selectively according to Bragg's law. The photo-tuning of the reflection wavelength has substantial significance for applications in reflection displays, tunable lasers, photonics, etc. This chapter focus is on the recent progress in photoresponsive chiral LCs such as chiral nematic phases, chiral smectic phases, and blue phases (BPs). We especially discuss their structures, important properties and feasible applications. Photomodulation of chiral nematic phases such as phase transition, handedness inversion, and reflection color change is introduced based on different types of photochromic molecules. For the chiral smectic phase, photomodulation mainly focuses on their ferroelectric nature such as magnitude and sign of spontaneous polarization. BPs as a novel type of three dimensional (3D) photonic crystals are receiving increasing attention, and the light-induced phase transitions and reflection color tuning are also presented.


Cholesteric Phase Pitch Length Reflection Color Reflection Wavelength Blue Phase 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The preparation of this chapter benefited from the support to Quan Li by the Air Force Office of Scientific Research (AFOSR FA9550-09-1-0193 and FA9550-09-1-0254), the Department of Defense Multidisciplinary University Research Initiative (AFOSR MURI FA9550-06-1-0337 and FA9550-12-1-00370), the National Science Foundation (NSF IIP 0750379), the National Aeronautics and Space Administration (NASA), the Department of Energy (DOE DE-SC0001412), Ohio Third Frontier, and the Ohio Board of Regents under its Research Challenge program.


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© © The Author(s) 2014

Authors and Affiliations

  1. 1.Liquid Crystal InstituteKent State UniversityKentUSA

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