Self-assembly Of Soft Matter Research Articles

The Promise of Soft-Matter-Enabled Quantum Materials.

The field of quantum materials has experienced rapid growth over the past decade, driven by exciting new discoveries with immense transformative potential. Traditional synthetic methods to quantum materials have, however, limited the exploration of architectural control beyond the atomic scale. By contrast, soft matter self-assembly can be used to tailor material structure over a large range of length scales, with a vast array of possible form factors, promising emerging quantum material properties at the mesoscale. This review explores opportunities for soft matter science to impact the synthesis of quantum materials with advanced properties. Existing work at the interface of these two fields is highlighted, and perspectives are provided on possible future directions by discussing the potential benefits and challenges which can arise from their bridging.

Combining Particle-Based Simulations and Machine Learning to Understand Defect Kinetics in Thin Films of Symmetric Diblock Copolymers

The self-assembly of soft matter provides a practical and scalable route toward the production of nanostructured materials, with minimal need for direct intervention at nanoscopic length scales. Symmetric diblock copolymers, which can self-assemble into a lamellar phase, are a prototype for this class of materials. In this work, we introduce a machine learning model that is trained by intermediate time-scale simulations of a soft, coarse-grained model. The aim of the model is to simulate defect kinetics in the lamellar morphology, as the material relaxes toward equilibrium. To do so, we exploit the physical characteristics of overdamped dynamics and formulate the problem of time evolution as a Markov chain. The trained artificial neural network (ANN) predicts a time-independent transition probability from one time step to the next. As a result, we arrive at a method that can be repeatedly applied to generate long-time trajectories. Predicting defect kinetics in this manner provides hitherto unavailable insights into the late-time dynamics of block copolymer relaxation. The neural network is purposely designed to be independent of input size, which enables training on small systems, and enabling predictions over large scales. As a demonstration of these capabilities, in this work, we leverage the ANN to obtain information about the statistics of defect motion and lifetimes over a long-range ordering process.

Structural studies on soft-matter self-assembly with small-angle X-ray scattering

Structural studies on soft-matter self-assembly with small-angle X-ray scattering

Composite Nanotube Ring Structures Formed by Two-Step Self-Assembly for Drug Loading/Release.

Nanotube rings are barely reported novel structures formed by the self-assembly of soft matter, as compared with nanotube structures and ring structures. The two-step self-assembly of amphiphilic copolymer AB and solvophobic copolymer CDC was studied. We found that nanotube rings can be formed from a certain mass ratio of copolymer CDC to copolymer AB and block D of certain rigidity. More interestingly, we discovered a new strategy for drug loading and release, which is different from the usual strategies reported in the literature. The present study provides a new rationale for the self-assembly of copolymers.

The concept of strongly interacting groups in self-assembly of soft matter.

Amphiphilic molecules in solution typically produce structures coming from cooperative interactions of many synergetically acting functional units. If all essential interactions are weak, the structure can be treated theoretically based on a free energy expansion for small interaction parameters. However, most self-assembling soft matter systems involve strong specific interactions of functional units leading to qualitatively new structures of highly soluble micellar or fibrillar aggregates. Here we focus on the systems with the so-called strongly interacting groups (SIGs) incorporated into unimer molecules and discuss the effects of packing frustrations and unimer chirality as well as the origins of spontaneous morphological chirality in the case of achiral unimers. We describe several theoretical approaches (overcoming the limitations of weak interaction models) including the concepts of super-strong segregation, geometrical mismatch and orientational frustration. We also review some recently developed phenomenological theories of surfactant membranes and multiscale hierarchical approaches based on all-atomic modeling of packing structures of amphiphilic molecules with SIGs. In particular, we discuss self-assembling structures in systems possessing simultaneously several distinct types of SIGs: solutions of beta-sheet oligopeptides (showing different fibrillar morphologies), aromatic diamide-ester molecules (forming membranes, helical ribbons and tubules), and triarylamine amide derivatives (producing light-controlled supramolecular nanowires).

Hydrodynamic Effects in Soft-matter Self-assembly: The Case of J-Aggregates of Amphiphilic Porphyrins.

Chiral J-aggregates of achiral amphiphilic porphyrins (4-sulfonatophenyl and aryl meso-substituted porphyrins) show several effects under the hydrodynamic forces of common stirring. These effects can be classified as pure mechanic (e. g. elasticity, plasticity and breaking of the self-assembly non-covalent bonding) and chemically selective as detected in the formation/growth of the nanoparticles. Diastereoselective, enantioselective and, depending on the sign of chiral shear forces, even enantiospecific selections have been described. Some types of these effects have been reported in other type of J-aggregates. Reversible and irreversible structural effects have been studied by atomic force imaging. The determination of the optical polarization properties (linear and circular) of their solutions is best done using Mueller matrix polarimetry methods.