journal article·2025-04Open access

Predictive Design to Determine Optimal Absorber Placement in Colloidal Photonic Crystals

Gudrun Bleyer, Nico Nees, Florian Prohaska, Lukas Pflug, Michael Stingl, Nicolas Vogel

Advanced Optical Materials· Volume 13· Issue 12

Abstract

Abstract Structural coloration abounds in nature and its remarkable optical effects are mimicked in synthetic photonic crystals and glasses. However, the color saturation of these synthetic structures is often diminished by incoherent scattering caused by defects and irregularities. The inclusion of absorbing materials increases color saturation, but where this absorber is most efficiently incorporated within a photonic structure remains unknown. Here, this question is addressed using predictive design. A mathematical structure optimization algorithm is developed that iteratively places absorbing particles within a colloidal photonic crystal until an optimal distribution that produces maximum chroma values is identified. This rigorous optimization shows that placement of the absorbing material in the bottom layers of the photonic structure is most efficient in producing highly chromatic structural colors compared to other common absorber placements. Experiments based on a layer‐by‐layer assembly of polystyrene@polydopamine core–shell particles with controlled absorber distribution confirm these findings. These results demonstrate the ability of predictive design to guide the experimental realization of structurally‐colored materials with optimal properties without the need for time‐ and resource‐consuming experimental parameter studies.
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Research context

OpenAlex topics: Photonic Crystals and Applications · Photonic and Optical Devices · Nonlinear Optical Materials Studies

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Institute of Particle Technology Friedrich‐Alexander‐Universität Erlangen‐Nürnberg Cauerstraße 4 91058 Erlangen Germany

Department of Mathematics Chair of Applied Mathematics (Continuous Optimisation) Friedrich‐Alexander‐Universität Erlangen‐Nürnberg Cauerstraße 11 91058 Erlangen Germany

Competence Center Scientific Computing (FAU CSC) Friedrich‐Alexander‐Universität Erlangen‐Nürnberg Cauerstraße 11 91058 Erlangen Germany

Funding

Deutsche Forschungsgemeinschaft · European Research Council

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