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Updated: May 13, 2026

Utilization of Plasmonic and Photonic Crystal Nanostructures for Enhanced Micro- and Nanoparticle Manipulation

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Published on: September 27, 2011

Illuminating Disorder: Optical Properties of Complex Plasmonic Assemblies.

Zachary M Sherman^1,2, Jiho Kang², Delia J Milliron^2,3

¹Department of Chemical Engineering, University of Washington, 3781 Okanogan Lane, Seattle, Washington 98195, United States.

The Journal of Physical Chemistry Letters

|June 12, 2024

Summary

Researchers are improving the design of disordered plasmonic nanoparticle materials by combining experiments with large-scale electromagnetic computations. This approach links structural organization to optical properties, guiding future material design.

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Published on: November 21, 2019

Area of Science:

Materials Science
Nanotechnology
Computational Physics

Background:

Disordered plasmonic nanoparticle assemblies offer tunable optical properties.
Controlling, characterizing, and predicting optical behavior in these materials remains challenging.

Purpose of the Study:

To discuss integrated experimental and computational approaches for designing disordered optical materials.
To bridge the gap between experimental parameters, structural features, and optical properties.

Main Methods:

Experimental assembly of disordered optical materials (e.g., nanocrystal gels, metasurfaces).
Electromagnetic computations on large-scale simulated structures.
Analysis of structure-property relationships.

Main Results:

Simulations are crucial for linking experimental parameters to structural motifs and optical properties.
Established structure-property relations inform the design of disordered optical materials.
Identified opportunities for optimizing optical designs using computational inverse methods and machine learning.

Conclusions:

Integrated experimental and computational studies are vital for advancing disordered optical materials.
Computational tools enable precise control and prediction of optical properties.
Machine learning and inverse design hold promise for future material optimization.