Theoretical Study on Gaussian Polymer Chains for Spin-Echo Small-Angle Neutron Scattering

This study develops a generalized method for applying spin-echo small-angle neutron scattering (SESANS) to the structural analysis of polymers. Starting from the theoretical framework of SESANS, we developed real space correlation functions for the Gaussian chain model systems consisting of chains with many beads. Further molecular dynamics (MD) simulations affirm that the functions derived by our proposed theoretical work can accurately predict the radii of gyration of polymer chains, which bring straightforward insight of SESANS measurements. This work will enable a broader application of SESANS in soft matter analysis.

Deep-Learning-Assisted Understanding of the Self-Assembly of Miktoarm Star Block Copolymers

The self-assemblies of topological complex block copolymers, especially the ABn type miktoarm star ones, are fascinating topics in the soft matter field, which represent typical self-assembly behaviors analogous to those of biological membranes. However, their diverse topological asymmetries and versatile spontaneous curvatures result in rather complex phase separations that deviate significantly from the common mechanisms. Thus, numerous trial-and-error experiments with tremendous parameter space and intricate relationships are needed to study their assemblies. Herein, we applied deep learning technology to decipher the phase behaviors of the miktoarm star block copolymer PEO-s-PS2 in an evaporation-induced self-assembly system. A neural network model was trained from practical experimental data encompassing two polymer properties and three synthesis condition parameters as input variables, which successfully predicted a three-dimensional (3D) synthesis-field diagram and mined the relationship between input parameters and obtained structures. This model demonstrated the highly flexible structure modulation directions of the miktoarm star block copolymer, revealing the correlation between the polymer parameters, synthesis conditions, and the output structures due to the significant influence of the variables on spontaneous curvatures. This work demonstrated the efficiency of a deep learning technique in uncovering the underlying rules of complex self-assembly systems, providing valuable insights into the exploration of soft matter science.

Supraparticles from Cubic Iron Oxide Nanoparticles: Synthesis, Polymer Encapsulation, Functionalization, and Magnetic Properties

Supraparticles (SPs) consisting of superparamagnetic iron oxide nanoparticles (SPIONs) are of great interest for biomedical applications and magnetic separation. To enable their functionalization with biomolecules and to improve their stability in aqueous dispersion, polymer shells are grown on the SPs' surface. Robust polymer encapsulation and functionalization is achieved via atom transfer radical polymerization (ATRP), improving the reaction control compared to free radical polymerizations. This study presents the emulsion-based assembly of differently sized cubic SPIONs (12-30 nm) into SPs with diameters ranging from ∼200 to ∼400 nm using dodecyltrimethylammonium bromide (DTAB) as the surfactant. The successful formation of well-defined spherical SPs depends upon the method used for mixing the SPION dispersion with the surfactant solution and requires the precise adjustment of the surfactant concentration. After purification, the SPs are encapsulated by growing surface-grafted polystyrene shells via activators generated by electron transfer (AGET) ATRP. The polymer shell can be decorated with functional groups (azide and carboxylate) using monomer blends for the polymerization reaction. When the amount of the monomer is varied, the shell thickness as well as the interparticle distances between the encapsulated SPIONs can be tuned with nanometer-scale precision. Small-angle X-ray scattering (SAXS) reveals that cubic SPIONs form less ordered assemblies within the SPs than spherical SPIONs. As shown by vibrating sample magnetometer measurements, the encapsulated SPs feature the same superparamagnetic behavior as their SPION building blocks. The saturation magnetization ranges between 10 and 30 emu/g and depends upon the nanocubes' size and phase composition.

Formation of various structures caused by particle size difference in colloidal heteroepitaxy

By performing isothermal-isochoric Monte Carlo simulations with depletion force, the author investigated the dependence of the epitxial layer structure on the differences in the particle size between the substrate in colloidal heteroepitaxy. By changing the size of epitaxial particles and performing simulations comprehensively, various structures including the structures observed in a experiment, such as a honeycomb, one created by hexagonal heptamers, and one consisting of both pentagonal tiles and triangular tiles, were created. When the ratio of particle sizes between the epitxial layer and substrate takes a specific value, two types of hexagonal structures were created. One is the hexagonal layer parallel to the substrate layer and the other layer is rotated by 60[Formula: see text] from the substrate layer. The former structure was created over a wide range of particle-size ratios, whereas the latter structure was created when the particle-size ratio was only around the specific ratio, and it seemed a metastable structure.

Aggregation behavior of nanoparticles: Revisiting the phase diagram of colloids

Surface functionalization of metal nanoparticles (NPs), e.g., using peptides and proteins, has recently attracted a considerable attention in the field of design of therapeutics and diagnostics. The possibility of diverse functionalization allows them to selectively interact with proteins, while the metal core ensures solubility, making them tunable therapeutic agents against diseases due to mis-folding or aggregation. On the other hand, their action is limited by possible self-aggregation, which could be, however, prevented based on the full understanding of their phase diagram as a function of the environmental variables (temperature, ionic strength of the solution, concentration) and intrinsic characteristics (size, charge, amount, and type of functional groups). A common modeling strategy to study the phase behavior is to represent the NPs as spheres interacting via effective potentials implicitly accounting for the solvation effects. Their size put the NPs into the class of colloids, albeit with particularly complex interactions including both attractive and repulsive features, and a consequently complex phase diagram. In this work, we review the studies exploring the phases of these systems starting from those with only attractive or repulsive interactions, displaying a simpler disperse-clustered-aggregated transitions. The phase diagram is here interpreted focusing on the universal aspects, i.e., those dependent on the general feature of the potentials, and available data are organized in a parametric phase diagram. We then consider the potentials with competing attractive short range well and average-long-range repulsive tail, better representing the NPs. Through the proper combination of the attractive only and repulsive only potentials, we are able to interpret the appearance of novel phases, characterized by aggregates with different structural characteristics. We identify the essential parameters that stabilize the disperse phase potentially useful to optimize NP therapeutic activity and indicate how to tune the phase behavior by changing environmental conditions or the NP chemical–physical properties.

Predicting aggregate morphology of sequence-defined macromolecules with recurrent neural networks

Self-assembly of dilute sequence-defined macromolecules is a complex phenomenon in which the local arrangement of chemical moieties can lead to the formation of long-range structure. The dependence of this structure on the sequence necessarily implies that a mapping between the two exists, yet it has been difficult to model so far. Predicting the aggregation behavior of these macromolecules is challenging due to the lack of effective order parameters, a vast design space, inherent variability, and high computational costs associated with currently available simulation techniques. Here, we accurately predict the morphology of aggregates self-assembled from sequence-defined macromolecules using supervised machine learning. We find that regression models with implicit representation learning perform significantly better than those based on engineered features such as k-mer counting, and a recurrent-neural-network-based regressor performs the best out of nine model architectures we tested. Furthermore, we demonstrate the high-throughput screening of monomer sequences using the regression model to identify candidates for self-assembly into selected morphologies. Our strategy is shown to successfully identify multiple suitable sequences in every test we performed, so we hope the insights gained here can be extended to other increasingly complex design scenarios in the future, such as the design of sequences under polydispersity and at varying environmental conditions.

Shape multistability in flexible tubular crystals through interactions of mobile dislocations

Crystalline sheets rolled up into cylinders occur in diverse biological and synthetic systems, including carbon nanotubes, biofilaments of the cellular cytoskeleton, and packings of colloidal particles. In this work, we show, computationally, that such tubular crystals can be programmed with reconfigurable shapes, due to motions of defects that interrupt the periodicity of the crystalline lattice. By identifying and exploiting stable patterns of these defects, we cause tubular crystals to relax into desired target geometries, a design principle that could guide the creation of versatile colloidal analogues to nanotubes. Our results suggest routes to tunable and switchable material properties in ordered, soft materials on deformable surfaces.

We study avenues to shape multistability and shape morphing in flexible crystalline membranes of cylindrical topology, enabled by glide mobility of dislocations. Using computational modeling, we obtain states of mechanical equilibrium presenting a wide variety of tubular crystal deformation geometries, due to an interplay of effective defect interactions with out-of-tangent-plane deformations that reorient the tube axis. Importantly, this interplay often stabilizes defect configurations quite distinct from those predicted for a two-dimensional crystal confined to the surface of a rigid cylinder. We find that relative and absolute stability of competing states depend strongly on control parameters such as bending rigidity, applied stress, and spontaneous curvature. Using stable dislocation pair arrangements as building blocks, we demonstrate that targeted macroscopic three-dimensional conformations of thin crystalline tubes can be programmed by imposing certain sparse patterns of defects. Our findings reveal a broad design space for controllable and reconfigurable colloidal tube geometries, with potential relevance also to architected carbon nanotubes and microtubules.

Structural and Dynamical Behaviour of Colloids with Competing Interactions Confined in Slit Pores

Systems with short-range attractive and long-range repulsive interactions can form periodic modulated phases at low temperatures, such as cluster-crystal, hexagonal, lamellar and bicontinuous gyroid phases. These periodic microphases should be stable regardless of the physical origin of the interactions. However, they have not yet been experimentally observed in colloidal systems, where, in principle, the interactions can be tuned by modifying the colloidal solution. Our goal is to investigate whether the formation of some of these periodic microphases can be promoted by confinement in narrow slit pores. By performing simulations of a simple model with competing interactions, we find that both the cluster-crystal and lamellar phases can be stable up to higher temperatures than in the bulk system, whereas the hexagonal phase is destabilised at temperatures somewhat lower than in bulk. Besides, we observed that the internal ordering of the lamellar phase can be modified by changing the pore width. Interestingly, for sufficiently wide pores to host three lamellae, there is a range of temperatures for which the two lamellae close to the walls are internally ordered, whereas the one at the centre of the pore remains internally disordered. We also find that particle diffusion under confinement exhibits a complex dependence with the pore width and with the density, obtaining larger and smaller values of the diffusion coefficient than in the corresponding bulk system.

Self-assembly in mixtures with competing interactions

A binary mixture of particles interacting with spherically-symmetrical potentials leading to microsegregation is studied by theory and molecular dynamics (MD) simulations. We consider spherical particles with equal diameters and volume fractions. Motivated by the mixture of oppositely charged particles with different adsorption preferences immersed in a near-critical binary solvent, we assume short-range attraction long-range repulsion for the interaction between like particles, and short-range repulsion long-range attraction for the interaction between different ones. In order to predict structural and thermodynamic properties of such complex mixtures, we develop a theory combining the density functional and field-theoretical methods. We show that concentration fluctuations in mesoscopic regions lead to a qualitative change of the phase diagram compared to mean-field predictions. Both theory and MD simulations show coexistence of a low-density disordered phase with a high-density phase with alternating layers rich in the first and second components. In these layers, crystalline structure is present in the solid, and absent in the liquid crystals. The density and the degree of order of the ordered phase decrease with increasing temperature, up to a temperature where the theory predicts a narrow two-phase region with increasing density of both phases for increasing temperature. MD simulations show that monocrystals of the solid and liquid crystals have a prolate shape with the axis parallel to the direction of concentration oscillations, and the deviation from the spherical shape increases with increasing periodic order.

Fabrication of floating colloidal crystal monolayers by convective deposition

HYPOTHESIS

Well-defined two-dimensional colloidal crystal monolayers (CCM) have numerous applications, such as photonic crystal, sensors, and masks for colloidal lithography. Therefore, significant effort was devoted to the preparation of preparing CCM. However, the fabrication of CCM that can float in the continuous phase and readily transfer to other substrate remains an elusive challenge.

EXPERIMENTS

In this article a facile approach to prepare floating CCM from polymeric colloids as building blocks is reported. The key to obtain floating CCM is the selection of an appropriate solvent to release the formed CCM from the substrate. There are two steps involved in the preparation of floating CCM: formation and peeling off.

FINDINGS

First, colloids are dispersed in a solvent. Evaporation of this solvent results in the formation of a meniscus structure of the air-liquid interface between the colloids that are on the substrate. The deformation of the meniscus gives rise to capillary attraction, driving the colloids together in a dense monolayer. Once a crystallization nucleus is formed, a convective flow containing additional colloids sets in, resulting in the formation of CCM on the substrate. Second, the remaining bulk dispersion is replaced by an extracting solvent that wets the substrate and peels the formed CCM off. The influence of the several solvents, the substrate materials, and the types of colloids on the CCM formation are investigated systematically. The robustness of the approach facilitates the preparation of CCM. Furthermore, the floating feature of the CCM in principle makes transfer of the CCM to other substrates possible, which broadens its applications.

Confinement of Colloids with Competing Interactions in Ordered Porous Materials

In this work, we explore the possibility of promoting the formation of ordered microphases by confinement of colloids with competing interactions in ordered porous materials. For that aim, we consider three families of porous materials modeled as cubic primitive, diamond, and gyroid bicontinuous phases. The structure of the confined colloids is investigated by means of grand canonical Monte Carlo simulations in thermodynamic conditions at which either a cluster crystal or a cylindrical phase is stable in bulk. We find that by tuning the size of the unit cell of these porous materials, numerous novel ordered microphases can be produced, including cluster crystals arranged into close packed and open lattices as well as nonparallel cylindrical phases.

Out-of-Equilibrium Colloidal Assembly Driven by Chemical Reaction Networks

Transient assembled structures play an indispensable role in a wide variety of processes fundamental to living organisms including cellular transport, cell motility, and proliferation. Typically, the formation of these transient structures is driven by the consumption of molecular fuels via dissipative reaction networks. In these networks, building blocks are converted from inactive precursor states to active (assembling) states by (a set of) irreversible chemical reactions. Since the activated state is intrinsically unstable and can be maintained only in the presence of sufficient fuel, fuel depletion results in the spontaneous disintegration of the formed superstructures. Consequently, the properties and behavior of these assembled structures are governed by the kinetics of fuel consumption rather than by their thermodynamic stability. This fuel dependency endows biological systems with unprecedented spatiotemporal adaptability and inherent self-healing capabilities. Fascinated by these unique material characteristics, coupling the assembly behavior to molecular fuel or light-driven reaction networks was recently implemented in synthetic (supra)molecular systems. In this invited feature article, we discuss recent studies demonstrating that dissipative assembly is not limited to the molecular world but can also be translated to building blocks of colloidal dimensions. We highlight crucial guiding principles for the successful design of dissipative colloidal systems and illustrate these with the current state of the art. Finally, we present our vision on the future of the field and how marrying nonequilibrium self-assembly with the functional properties associated with colloidal building blocks presents a promising route for the development of next-generation materials.