JOURNAL ARTICLE

Investigations of axioms of twist grain boundary phases (TGBPs) in binary mixture of liquid crystals.

  • Published In: International Journal of Modern Physics B: Condensed Matter Physics; Statistical Physics; Applied Physics, 2024, v. 38, n. 1. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Chaudhary, Manisha; Ghildyal, Divya 3 of 3

Abstract

In this paper, the investigated mixture of liquid crystals shows a spacious range of twist grain boundary phases (TGBPs). Thermo dynamical, optical and dielectric measurements confirmed the presence of TGBPs in the mixtures of nematic and cholesteric liquid crystals. In the heating cycle, differential scanning calorimetry (DSC) thermogram shows the mixed different TGBPs at the high scanning rate. Cylindrical and cone-like textures with brushes of TGBA* is observed under the optical investigation. The grain boundaries and blocks of smectic phases are twisting two times around an axis. Wide-ranging dielectric investigation of nematic and cholesteric liquid crystal mixture have been performed in the 50 Hz–10 MHz range of frequency for the homogeneous conditions. In the absence of direct bias, TGBA* phase exhibits two dielectric relaxation processes, soft mode (SM) and process in the grain boundaries (PGBs). In the presence of electric field, grain boundaries steadily demolished the phases. Whereas the weak dielectric relaxation processes have been distinguished in the presence of the electric field in the TGBPs. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Modern Physics B: Condensed Matter Physics; Statistical Physics; Applied Physics. 2024/01, Vol. 38, Issue 1, p1
  • Document Type:Article
  • Subject Area:Geology
  • Publication Date:2024
  • ISSN:0217-9792
  • DOI:10.1142/S0217979224500061
  • Accession Number:174823457
  • Copyright Statement:Copyright of International Journal of Modern Physics B: Condensed Matter Physics; Statistical Physics; Applied Physics is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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