Modeling Structural Colors from Disordered One-Component Colloidal Nanoparticle-Based Supraballs Using Combined Experimental and Simulation Techniques

Abstract

Bright, saturated structural colors in birds have inspired synthesis of self-assembled, disordered arrays of assembled nanoparticles with varied particle spacings and refractive indices. However, predicting colors of assembled nanoparticles, and thereby guiding their synthesis, remains challenging due to the effects of multiple scattering and strong absorption. Here, we use a computational approach to first reconstruct the nanoparticles' assembled structures from smallangle scattering measurements and then input the reconstructed structures to a finite-difference time-domain method to predict their color and reflectance. This computational approach is successfully validated by comparing its predictions against experimentally measured reflectance and provides a pathway for reverse engineering colloidal assemblies with desired optical and photothermal properties.

Synergistic Role of Temperature and Salinity in Aggregation of Nonionic Surfactant-Coated Silica Nanoparticles

“…As a result, the conventional form factor model that works for the dispersed state may not be applicable in the aggregated state, making such a conventional analytical model fit-based analysis of the SANS profile challenging. Therefore, we use a machine learning (ML)-based computational reverse-engineering analysis for scattering experiments (CREASE) approach recently developed by the Jayaraman group to analyze the SANS data. − In particular, the recent work with “ P ( q ) and S ( q ) CREASE” is used for the analysis of the scattering profiles in this study. CREASE-based analysis produces the best fit computed scattering profile(s) while working with four different hypotheses about the adsorbed surfactant “shell” layer on the NPs with varying salinity and temperature (Figure b–g), namely, (i) shell thickness is the same across all NPs (black) I Shell‑constant , (ii) shell thickness across various NPs exhibits dispersity (red) I Shell‑disperse , (iii) shell thickness is the same across all NPs with potential overlap among the shells (blue) I Shell‑constant (overlap) , and (iv) shell thickness on various NPs exhibits dispersity with potential overlap among the shells (green) I Shell‑disperse (overlap) .…”

Computational Reverse-Engineering Analysis for Scattering Experiments for Form Factor and Structure Factor Determination (“P(q) and S(q) CREASE”)

“…There are many analytical models , ( e.g ., hard sphere , and sticky hard sphere , ) for characterizing the structure of “primary particles” in a fluid suspension; these models assume a uniform liquid-like structure, which would not perform well for dense systems at high packing fractions (above 0.4) or when there is a formation of “primary particle” aggregates. , More complex analytical models for aggregating particles require the user to possess significant a priori knowledge of their system as they choose the appropriate analytical structure factor model for the type and quality of structural information extracted ( e.g ., the aggregate radius of gyration and aggregation number) . To overcome these limitations with existing analytical S ( q ) models, we developed a computational method, CREASE, for analyzing the structure of “primary particles” ( e.g ., nanoparticle solutions, dense binary nanoparticle mixtures) without requiring a user to select a specific analytical model using substantial a priori knowledge from alternative characterization methods. , Furthermore, unlike the analytical model fits, this CREASE method provides a 3D structural reconstruction of the system being studied, which can then be used as an input for other calculations ( e.g ., resistor network model calculation for electrical conductivity and finite-difference time-domain method for optical properties , ). However, this recently published CREASE method was designed for interpreting S ( q ) in mixtures and solutions of nanoparticles where the nanoparticle’s (or nanoparticles’) P ( q ) is known a priori .…”

“… 50 To overcome these limitations with existing analytical S ( q ) models, we developed a computational method, CREASE, for analyzing the structure of “primary particles” ( e.g ., nanoparticle solutions, dense binary nanoparticle mixtures) without requiring a user to select a specific analytical model using substantial a priori knowledge from alternative characterization methods. 51 , 52 Furthermore, unlike the analytical model fits, this CREASE method provides a 3D structural reconstruction of the system being studied, which can then be used as an input for other calculations ( e.g ., resistor network model calculation for electrical conductivity 53 and finite-difference time-domain method for optical properties 54 , 55 ). However, this recently published CREASE method was designed for interpreting S ( q ) in mixtures and solutions of nanoparticles where the nanoparticle’s (or nanoparticles’) P ( q ) is known a priori .…”

Mechanism of structural colors in binary mixtures of nanoparticle-based supraballs

“…However, those models do not provide a three-dimensional (3D) structural reconstruction that would enable optical modeling using a first-principle technique, such as the FDTD method. Instead, we use the recently developed computational reverse-engineering analysis for scattering experiments (CREASE) method that has been extensively validated for nanoparticle assemblies ( 50 , 51 ). Previous work has demonstrated how combining the CREASE and FDTD methods allows a user to accurately predict the structural color of one-component nanoparticle assemblies ( 51 ).…”