Hierarchical Self-Assembly of Poly(d-glucose carbonate) Amphiphilic Block Copolymers in Mixed Solvents

Computational Reverse Engineering Analysis of the Scattering Experiment Method for Interpretation of 2D Small-Angle Scattering Profiles (CREASE-2D)

Small-angle scattering (SAS) is a widely used characterization technique that provides structural information in soft materials at varying length scales (nanometers to microns). The output of an SAS measurement is the scattered intensity I(q) as a function of q, the scattered wavevector with respect to the incident wave; the latter is represented by its magnitude |q| ≡ q (in inverse distance units) and azimuthal angle θ. While isotropic structural arrangement can be interpreted by analysis of the azimuthally averaged one-dimensional (1D) scattering profile, to understand anisotropic arrangements, one has to interpret the two-dimensional (2D) scattering profile, I(q, θ). Manual interpretation of such 2D profiles usually involves fitting of approximate analytical models to azimuthally averaged sections of the 2D profile. In this paper, we present a new method called CREASE-2D that interprets, without any azimuthal averaging, the entire 2D scattering profile, I(q, θ), and outputs the relevant structural features. CREASE-2D is an extension of the "computational reverse engineering analysis for scattering experiments" (CREASE) method that has been used successfully to analyze 1D SAS profiles for a variety of soft materials. CREASE-2D goes beyond CREASE by enabling analysis of 2D scattering profiles, which is far more challenging to interpret than the azimuthally averaged 1D profiles. The CREASE-2D workflow identifies the structural features whose computed I(q, θ) profiles, calculated using a surrogate XGBoost machine learning model, match the input experimental I(q, θ). We expect that this CREASE-2D method will be a valuable tool for materials' researchers who need direct interpretation of the 2D scattering profiles in contrast to analyzing azimuthally averaged 1D I(q) vs q profiles that can lose important information related to structural anisotropy.

In Situ Monitoring of Block Copolymer Self-Assembly via Solvent Exchange through Controlled Dialysis with Light and Neutron Scattering Detection

Solution self-assembly of amphiphilic block copolymers (BCs) is typically performed by a solvent-to-water exchange. However, BC assemblies are often trapped in metastable states depending on the mixing conditions such as the magnitude and rate of water addition. BC self-assembly can be performed under near thermodynamic control by dialysis, which accounts for a slow and gradual water addition. In this Letter we report the use of a specifically designed dialysis cell to continuously monitor by dynamic light scattering and small-angle neutron scattering the morphological changes of PDMS-b-PEG BCs self-assemblies during THF-to-water exchange. The complete phase diagrams of near-equilibrium structures can then be established. Spherical micelles first form before evolving to rod-like micelles and vesicles, decreasing the total developed interfacial area of self-assembled structures in response to increasing interfacial energy as the water content increases. The dialysis kinetics can be tailored to the time scale of BC self-assembly by modifying the membrane pore size, which is of interest to study the interplay between thermodynamics and kinetics in self-assembly pathways.

Connecting Non-Gaussian Water Density Fluctuations to the Lengthscale Dependent Crossover in Hydrophobic Hydration

We connect density fluctuations in liquid water to lengthscale dependent crossover in hydrophobic hydration. Specifically, we employ indirect umbrella sampling (INDUS) simulations to characterize density fluctuations in observation volumes of various sizes and shapes in water and as a function of temperature and salt concentration. Consistent with previous observations, density fluctuations are Gaussian in small molecular scale volumes, but they display non-Gaussian "low-density fat tails" in larger volumes. These non-Gaussian tails are indicative of the proximity of water to its liquid to vapor phase transition and have implications on biomolecular interactions and function. We show that the onset of non-Gaussian fluctuations in large volumes is accompanied by the formation of a cavity in the observation volume. We develop a model that uses the physics of cavity-water interface formation as a key ingredient and show that it captures the nature of non-Gaussian density fluctuations over a broad region in water and in salt solutions. We discuss the limitations of this model in the very low density region of the distribution. Our calculations provide new insights into the origins of non-Gaussian density fluctuations in water and their connections to lengthscale dependent crossover in hydrophobic hydration.

Computational Reverse-Engineering Analysis for Scattering Experiments (CREASE) with Machine Learning Enhancement to Determine Structure of Nanoparticle Mixtures and Solutions

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 Au2021, 1 (2 (2), ), 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.

Computational Reverse Engineering Analysis for Scattering Experiments (CREASE) on Vesicles Assembled from Amphiphilic Macromolecular Solutions

In this paper we present the development and validation of the "Computational Reverse-Engineering Analysis for Scattering Experiments" (CREASE) method for analyzing scattering results from vesicle structures that are commonly found upon assembly of synthetic, biomimetic, or bioderived amphiphilic copolymers in solution. The two-step CREASE method takes the amphiphilic polymer chemistry and small-angle scattering intensity profile, I exp(q), as input and determines the vesicles' structural features on multiple length scales ranging from assembled vesicle wall's individual layer thicknesses to the monomer-level packing and distribution of polymer conformations. In the first step of CREASE, a genetic algorithm (GA) is used to determine the relevant vesicle dimensions from the input macromolecular solution information and I exp(q) by identifying the structure whose computed scattering profile best matches the input I exp(q). Then in the second step, the GA-determined dimensions are used for molecular reconstruction of the vesicle structure. To validate CREASE for vesicles, we test CREASE on input scattering intensity profiles generated mathematically (termed as in silico I exp(q) vs q) from a variety of vesicle sizes with known dimensions. We also test CREASE on in silico I exp(q) vs q generated from vesicles with dispersity in all relevant dimensions, resembling real experiments. After successful validation of CREASE, we compare the CREASE-determined dimensions against those obtained from the traditional approach of fitting the scattering intensity profile to relevant analytical model in SASVIEW package. We show that CREASE performs better than or as well as the core-multishell analytical model's fitting in SASVIEW in determining vesicle dimensions with dispersity. We also show that CREASE provides structural information beyond those possible from traditional scattering analysis using the core-multishell model, such as the distribution of solvophilic monomers between the vesicle wall's inner and outer layers in the vesicle wall and the chain-level packing within each vesicle layer.

Alginate-Based Amphiphilic Block Copolymers as a Drug Codelivery Platform

Structured nanoassemblies are biomimetic structures that are enabling applications from nanomedicine to catalysis. One approach to achieve these spatially organized architectures is utilizing amphiphilic diblock copolymers with one or two macromolecular backbones that self-assemble in solution. To date, the impact of alternating backbone architectures on self-assembly and drug delivery is still an area of active research limited by the strategies used to synthesize these multiblock polymers. Here, we report self-assembling ABC-type alginate-based triblock copolymers with the backbones of three distinct biomaterials utilizing a facile conjugation approach. This "polymer mosaic" was synthesized by the covalent attachment of alginate with a PLA/PEG diblock copolymer. The combination of alginate, PEG, and PLA domains resulted in an amphiphilic copolymer that self-assembles into nanoparticles with a unique morphology of alginate domain compartmentalization. These particles serve as a versatile platform for co-encapsulation of hydrophilic and hydrophobic small molecules, their spatiotemporal release, and show potential as a drug delivery system for combination therapy.

Computational Reverse-Engineering Analysis for Scattering Experiments of Assembled Binary Mixture of Nanoparticles

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.

Supramolecular Interactions and Morphology of Self-Assembling Peptide Amphiphile Nanostructures

The morphology of supramolecular peptide nanostructures is difficult to predict given their complex energy landscapes. We investigated peptide amphiphiles containing β-sheet forming domains that form twisted nanoribbons in water. We explained the morphology based on a balance between the energetically favorable packing of molecules in the center of the nanostructures, the unfavorable packing at the edges, and the deformations due to packing of twisted β-sheets. We find that morphological polydispersity of PA nanostructures is determined by peptide sequences, and the twisting of their internal β-sheets. We also observed a change in the supramolecular chirality of the nanostructures as the peptide sequence was modified, although only amino acids with l-configuration were used. Upon increasing charge repulsion between molecules, we observed a change in morphology to long cylinders and then rodlike fragments and spherical micelles. Understanding the self-assembly mechanisms of peptide amphiphiles into nanostructures should be useful to optimize their well-known functions.

Machine Learning Enhanced Computational Reverse Engineering Analysis for Scattering Experiments (CREASE) to Determine Structures in Amphiphilic Polymer Solutions

In this article, we present a machine learning enhancement for our recently developed "Computational Reverse Engineering Analysis for Scattering Experiments" (CREASE) method to accelerate analysis of results from small angle scattering (SAS) experiments on polymer materials. We demonstrate this novel artificial neural network (NN) enhanced CREASE approach for analyzing scattering results from amphiphilic polymer solutions that can be easily extended and applied for scattering experiments on other polymer and soft matter systems. We had originally developed CREASE to analyze SAS results [i.e., intensity profiles, I(q) vs q] of amphiphilic polymer solutions exhibiting unconventional assembled structures and/or novel polymer chemistries for which traditional fits using off-the-shelf analytical models would be too approximate/inapplicable. In this paper, we demonstrate that the NN-enhancement to the genetic algorithm (GA) step in the CREASE approach improves the speed and, in some cases, the accuracy of the GA step in determining the dimensions of the complex assembled structures for a given experimental scattering profile.

A cascade strategy towards the direct synthesis of green polyesters with versatile functional groups

The cascade coupling of ROP and CP enables the facile synthesis of high functional group content biodegradable polyesters.