Literature DB >> 28365991

Structural Color Tuning: Mixing Melanin-Like Particles with Different Diameters to Create Neutral Colors.

Ayaka Kawamura1, Michinari Kohri1, Shinya Yoshioka2, Tatsuo Taniguchi1, Keiki Kishikawa1.   

Abstract

We present the ability to tune structural colors by mixing colloidal particles. To produce high-visibility structural colors, melanin-like core-shell particles composed of a polystyrene (PSt) core and a polydopamine (PDA) shell, were used as components. The results indicated that neutral structural colors could be successfully obtained by simply mixing two differently sized melanin-like PSt@PDA core-shell particles. In addition, the arrangements of the particles, which were important factors when forming structural colors, were investigated by mathematical processing using a 2D Fourier transform technique and Voronoi diagrams. These findings provide new insights for the development of structural color-based ink applications.

Entities:  

Year:  2017        PMID: 28365991     DOI: 10.1021/acs.langmuir.7b00707

Source DB:  PubMed          Journal:  Langmuir        ISSN: 0743-7463            Impact factor:   3.882


  4 in total

1.  Designing angle-independent structural colors using Monte Carlo simulations of multiple scattering.

Authors:  Victoria Hwang; Anna B Stephenson; Solomon Barkley; Soeren Brandt; Ming Xiao; Joanna Aizenberg; Vinothan N Manoharan
Journal:  Proc Natl Acad Sci U S A       Date:  2021-01-26       Impact factor: 12.779

2.  Experimental and theoretical evidence for molecular forces driving surface segregation in photonic colloidal assemblies.

Authors:  Ming Xiao; Ziying Hu; Thomas E Gartner; Xiaozhou Yang; Weiyao Li; Arthi Jayaraman; Nathan C Gianneschi; Matthew D Shawkey; Ali Dhinojwala
Journal:  Sci Adv       Date:  2019-09-20       Impact factor: 14.136

3.  Positive and negative birefringence in packed films of binary spherical colloidal particles.

Authors:  Kai Inoue; Susumu Inasawa
Journal:  RSC Adv       Date:  2020-01-14       Impact factor: 3.361

4.  Computational Reverse-Engineering Analysis for Scattering Experiments (CREASE) with Machine Learning Enhancement to Determine Structure of Nanoparticle Mixtures and Solutions.

Authors:  Christian M Heil; Anvay Patil; Ali Dhinojwala; Arthi Jayaraman
Journal:  ACS Cent Sci       Date:  2022-07-01       Impact factor: 18.728

  4 in total

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