State-Controlled Exclusive Colloidal Assembly for Composition-Invariant Structural Color Mixing
Source abstract
Abstract
Colloidal self-assembly enables pigment-free structural coloration, yet conventional systems typically produce a single photonic stopband defined by the average periodicity, limiting attainable hues. We present a simple yet versatile strategy to expand the structural color gamut by controlling the states of exclusively assembled grains in colloidal mixtures at a fixed composition. Monodisperse particles exhibit a tunable sequence of states─milky fluid, vividly colored crystal, and weakly colored glass, by strengthening attractive interparticle potentials through depletion forces and reduced electrostatic repulsion in the presence of a depletant and salt. Because larger particles experience stronger depletion attraction, binary mixtures exhibit three regimes: selective crystallization of large particles with dispersed small particles under mild conditions; crystallization of both particle sizes into separate lattices under intermediate conditions; and glass formation of large particles with crystallization of small particles under strong attraction. Within each regime, relative occupancy is modulated by fine-tuning pair potentials, enabling precise control of color-mixing ratios without changing the composition. Extending this approach to ternary mixtures of red-, green-, and blue-producing particles yields smooth, composition-invariant color transitions across the visible spectrum. This robust, reproducible method generates multiple stopbands with tunable contributions, providing a general platform for high-fidelity structural color mixing.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Photonic Crystals and Applications · Pickering emulsions and particle stabilization · Micro and Nano Robotics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Chemical and Biomolecular Engineering
Korea Advanced Institute of Science and Technology (KAIST)
GIST InnoCORE AI-Nano Convergence Institute for Early Detection of Neurodegenerative Diseases
Gwangju Institute of Science and Technology (GIST)
Funding
Ministry of Science and ICT, South Korea · National Research Foundation of Korea
References (43)
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