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Design, Fabrication, and Experimental Characterization of Plasmonic Photoconductive Terahertz Emitters

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Predictive design of plasmonic color.

Gudrun Bleyer¹, Andrian Uihlein², Umair Sultan¹

¹Institute of Particle Technology, Friedrich-Alexander-Universität, Erlangen-Nürnberg, Cauerstraße 4, 91058 Erlangen, Germany.

Journal of Colloid and Interface Science

|April 9, 2025

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Predictive design enables precise synthesis of functional nanoparticles by computationally determining optimal structures. This approach accelerates the creation of materials with desired properties, like specific colors, through in silico blueprints.

Area of Science:

Nanomaterials Science
Computational Materials Science
Plasmonics

Background:

Traditional nanoparticle synthesis often involves extensive parameter screening, leading to inefficiencies.
The perceived color of nanoparticle dispersions depends on complex interactions between light and particle properties, not just plasmon resonance.
Predictive design offers a pathway to computationally identify optimal nanomaterial structures.

Purpose of the Study:

To establish a predictive design strategy for nanoparticle systems, focusing on plasmonic coloration.
To create color maps predicting optimal nanoparticle structures for targeted colors.
To demonstrate the feasibility of in silico design for resource-efficient nanomaterial synthesis.

Main Methods:

Utilized Mie theory to compute far-field optical spectra of gold and silver core-shell nanoparticles.

Keywords:

CIELab Nanoparticles Plasmonic color Predictive design Structure optimization

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Converted computed spectra into color values perceived by the human eye.

Developed color difference maps based on concentration, core diameter, and shell thickness.

Main Results:

Successfully generated color maps predicting optimal nanoparticle structures for targeted colors.
Demonstrated the feasibility of targeting multiple colors using the predictive design approach.
Identified optimal structural blueprints for functional nanoparticles in silico.

Conclusions:

Predictive design provides accurate, in silico blueprints for synthesizing functional nanoparticles with desired properties.
This strategy significantly reduces the need for extensive experimental parameter screening.
The approach holds promise for accelerating the development of novel nanomaterials for various applications.