Materials Map

Discover the materials research landscape. Find experts, partners, networks.

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The Materials Map is an open tool for improving networking and interdisciplinary exchange within materials research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

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Materials Map under construction

The Materials Map is still under development. In its current state, it is only based on one single data source and, thus, incomplete and contains duplicates. We are working on incorporating new open data sources like ORCID to improve the quality and the timeliness of our data. We will update Materials Map as soon as possible and kindly ask for your patience.

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693.932 PEOPLE
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Pablo, Juan J. De

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in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (4/4 displayed)

  • 2024Numerical Study of Cleavable Bond-Modified Polyethylene for Circular Polymer Designcitations
  • 2023Scattering evidence of positional charge correlations in polyelectrolyte complexes10citations
  • 2023LCPOM: Precise Reconstruction of Polarized Optical Microscopy Images of Liquid Crystalscitations
  • 2018Light-activated helical inversion in cholesteric liquid crystal microdroplets34citations

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Marsden, Sam
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Ley-Flores, Maria
1 / 1 shared
Chabbi, Archit
1 / 1 shared
Rowan, Stuart J.
2 / 5 shared
Vettese, Isabella
1 / 1 shared
Fang, Yan
1 / 1 shared
Liang, Heyi
1 / 2 shared
Nealey, Paul
1 / 2 shared
Tirrell, Matt
1 / 1 shared
Rumyantsev, Artem
1 / 1 shared
Norouzi, Sepideh
1 / 1 shared
Rico, Pablo F. Zubieta
1 / 1 shared
Sadati, Monirosadat
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Palacio-Betancur, Viviana
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Katsonis, Nathalie
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Zhou, Ye
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Lacaze, Emmanuelle
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Sleczkowski, Piotr
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Iamsaard, Supitchaya
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Co-Authors (by relevance)

  • Marsden, Sam
  • Ley-Flores, Maria
  • Chabbi, Archit
  • Rowan, Stuart J.
  • Vettese, Isabella
  • Fang, Yan
  • Liang, Heyi
  • Nealey, Paul
  • Tirrell, Matt
  • Rumyantsev, Artem
  • Norouzi, Sepideh
  • Rico, Pablo F. Zubieta
  • Sadati, Monirosadat
  • Palacio-Betancur, Viviana
  • Katsonis, Nathalie
  • Zhou, Ye
  • Lacaze, Emmanuelle
  • Sleczkowski, Piotr
  • Iamsaard, Supitchaya
OrganizationsLocationPeople

document

LCPOM: Precise Reconstruction of Polarized Optical Microscopy Images of Liquid Crystals

  • Norouzi, Sepideh
  • Rico, Pablo F. Zubieta
  • Sadati, Monirosadat
  • Pablo, Juan J. De
  • Rowan, Stuart J.
  • Palacio-Betancur, Viviana
Abstract

When viewed with a cross-polarized optical microscope (POM), liquid crystals display interference colors and complex patterns that depend on the material's microscopic orientation. That orientation can be manipulated by application of external fields, which provides the basis for applications in optical display and sensing technologies. The color patterns themselves have a high information content. Traditionally, however, calculations of the optical appearance of liquid crystals have been performed by assuming that a single-wavelength light source is employed, and reported in a monochromatic scale. In this work, the original Jones matrix method is extended to calculate the colored images that arise when a liquid crystal is exposed to a multi-wavelength source. By accounting for the material properties, the visible light spectrum and the CIE color matching functions, we demonstrate that the proposed approach produces colored POM images that are in quantitative agreement with experimental data. Results are presented for a variety of systems, including radial, bipolar, and cholesteric droplets, where results of simulations are compared to experimental microscopy images. The effects of droplet size, topological defect structure, and droplet orientation are examined systematically. The technique introduced here generates images that can be directly compared to experiments, thereby facilitating machine learning efforts aimed at interpreting LC microscopy images, and paving the way for the inverse design of materials capable of producing specific internal microstructures in response to external stimuli.

Topics
  • impedance spectroscopy
  • experiment
  • simulation
  • defect
  • optical microscopy
  • machine learning
  • defect structure
  • liquid crystal
  • liquid chromatography