Melanin is ubiquitous in living organisms across different biological kingdoms of life, making it an important, natural biomaterial. Its presence in nature from microorganisms to higher animals and plants is attributed to the many functions of melanin, including pigmentation, radical scavenging, radiation protection, and thermal regulation. Generally, melanin is classified into five typeseumelanin, pheomelanin, neuromelanin, allomelanin, and pyomelaninbased on the various chemical precursors used in their biosynthesis. Despite its long history of study, the exact chemical makeup of melanin remains unclear, and it moreover has an inherent diversity and complexity of chemical structure, likely including many functions and properties that remain to be identified. Synthetic mimics have begun to play a broader role in unraveling structure and function relationships of natural melanins. In the past decade, polydopamine, which has served as the conventional form of synthetic eumelanin, has dominated the literature on melaninbased materials, while the synthetic analogues of other melanins have received far less attention. In this perspective, we will discuss the synthesis of melanin materials with a special focus beyond polydopamine. We will emphasize efforts to elucidate biosynthetic pathways and structural characterization approaches that can be harnessed to interrogate specific structure−function relationships, including electron paramagnetic resonance (EPR) and solid-state nuclear magnetic resonance (ssNMR) spectroscopy. We believe that this timely Perspective will introduce this class of biopolymer to the broader chemistry community, where we hope to stimulate new opportunities in novel, melanin-based poly-functional synthetic materials.

We present a new open-source, machine learning (ML) enhanced computational method for experimentalists to quickly analyze high-throughput small-angle scattering results from multicomponent nanoparticle mixtures and solutions at varying compositions and concentrations to obtain reconstructed 3D structures of the sample. This new method is an improvement over our original computational reverse-engineering analysis for scattering experiments (CREASE) method (ACS Materials Au 2021, 1 (22), 140−156), which takes as input the experimental scattering profiles and outputs a 3D visualization and structural characterization (e.g., real space pair-correlation functions, domain sizes, and extent of mixing in binary nanoparticle mixtures) of the nanoparticle mixtures. The new gene-based CREASE method reduces the computational running time by >95% as compared to the original CREASE and performs better in scenarios where the original CREASE method performed poorly. Furthermore, the ML model linking features of nanoparticle solutions (e.g., concentration, nanoparticles' tendency to aggregate) to a computed scattering profile is generic enough to analyze scattering profiles for nanoparticle solutions at conditions (nanoparticle chemistry and size) beyond those that were used for the ML training. Finally, we demonstrate application of this new gene-based CREASE method for analysis of small-angle X-ray scattering results from a nanoparticle solution with unknown nanoparticle aggregation and small-angle neutron scattering results from a binary nanoparticle assembly with unknown mixing/segregation among the nanoparticles.

We perform coarse-grained Langevin dynamics simulations in shrinking spherical confinement to unravel the design parameters controlling the surface composition and near-surface structure during the emulsion assembly of binary nanoparticle mixtures.

Melanin is a ubiquitous natural pigment that exhibits broadband absorption and high refractive index. Despite its widespread use in structural color production, how the absorbing material, melanin, affects the generated color is unknown. Using a combined molecular dynamics and finite‐difference time‐domain computational approach, this paper investigates structural color generation in one‐component melanin nanoparticle‐based supraparticles (called supraballs) as well as binary mixtures of melanin and silica (nonabsorbing) nanoparticle‐based supraballs. Experimentally produced one‐component melanin and one‐component silica supraballs, with thoroughly characterized primary particle characteristics using neutron scattering, produce reflectance profiles similar to the computational analogs, confirming that the computational approach correctly simulates both absorption and multiple scattering from the self‐assembled nanoparticles. These combined approaches demonstrate that melanin's broadband absorption increases the primary reflectance peak wavelength, increases saturation, and decreases lightness factor. In addition, the dispersity of nanoparticle size more strongly influences the optical properties of supraballs than packing fraction, as evidenced by the production of a larger range of colors when size dispersity is varied versus packing fraction. For binary melanin and silica supraballs, the chemistry‐based stratification allows for more diverse color generation and finer saturation tuning than does the degree of mixing/demixing between the two chemistries.

In this paper, we describe a computational method for analyzing results from scattering experiments on dilute solutions of supraparticles, where each supraparticle is created by the assembly of nanoparticle mixtures. Taking scattering intensity profiles and nanoparticle mixture composition and size distributions in each supraparticle as input, this computational approach called computational reverse engineering analysis for scattering experiments (CREASE) uses a genetic algorithm to output information about the structure of the assembled nanoparticles (e.g., real space pair correlation function, extent of nanoparticle mixing/segregation, sizes of domains) within a supraparticle. We validate this method by taking as input in silico scattering intensity profiles from coarse-grained molecular simulations of a binary mixture of nanoparticles, forming a close-packed structure and testing if our computational method can correctly reproduce the nanoparticle structure observed in those simulations. We test the strengths and limitations of our method using a variety of in silico scattering intensity profiles obtained from simulations of a spherical or a cubic supraparticle comprising binary nanoparticle mixtures with varying chemistries, with and without dispersity in sizes, that exhibit well-mixed to strongly segregated structures. The strengths of the presented method include its capability to analyze scattering intensity profiles even when the wavevector q range is limited, to handily provide all of the pairwise radial distribution functions, and to correctly determine the extent of segregation/mixing of the nanoparticles assembled in complex geometries.

In this paper, we present an open-source machine learning (ML)-accelerated computational method to analyze small-angle scattering profiles [ I ( q ) vs q ] from concentrated macromolecular solutions to simultaneously obtain the form factor P ( q ) ( e.g ., dimensions of a micelle) and the structure factor S ( q ) ( e.g ., spatial arrangement of the micelles) without relying on analytical models. This method builds on our recent work on Computational Reverse-Engineering Analysis for Scattering Experiments (CREASE) that has either been applied to obtain P ( q ) from dilute macromolecular solutions (where S ( q ) ∼1) or to obtain S ( q ) from concentrated particle solutions when P ( q ) is known ( e.g ., sphere form factor). This paper’s newly developed CREASE that calculates P ( q ) and S ( q ), termed as “ P ( q ) and S ( q ) CREASE”, is validated by taking as input I ( q ) vs q from in silico structures of known polydisperse core(A)–shell(B) micelles in solutions at varying concentrations and micelle–micelle aggregation. We demonstrate how “ P ( q ) and S ( q ) CREASE” performs if given two or three of the relevant scattering profiles— I total ( q ), I A ( q ), and I B ( q )—as inputs; this demonstration is meant to guide experimentalists who may choose to do small-angle X-ray scattering (for total scattering from the micelles) and/or small-angle neutron scattering with appropriate contrast matching to get scattering solely from one or the other component (A or B). After validation of “ P ( q ) and S ( q ) CREASE” on in silico structures, we present our results analyzing small-angle neutron scattering profiles from a solution of core–shell type surfactant-coated nanoparticles with varying extents of aggregation.

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The adsorption of nonionic surfactants onto hydrophilic nanoparticles (NPs) is anticipated to increase their stability in aqueous medium. While nonionic surfactants show salinity- and temperature-dependent bulk phase behavior in water, the effects of these two solvent parameters on surfactant adsorption and self-assembly onto NPs are poorly understood. In this study, we combine adsorption isotherms, dispersion transmittance, and small-angle neutron scattering (SANS) to investigate the effects of salinity and temperature on the adsorption of pentaethylene glycol monododecyl ether (C12E5) surfactant on silica NPs. We find an increase in the amount of surfactant adsorbed onto the NPs with increasing temperature and salinity. Based on SANS measurements and corresponding analysis using computational reverse-engineering analysis of scattering experiments (CREASE), we show that the increase in salinity and temperature results in the aggregation of silica NPs. We further demonstrate the non-monotonic changes in viscosity for the C12E5–silica NP mixture with increasing temperature and salinity and correlate the observations to the aggregated state of NPs. The study provides a fundamental understanding of the configuration and phase transition of the surfactant-coated NPs and presents a strategy to manipulate the viscosity of such dispersion using temperature as a stimulus.