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Block copolymers have attracted recent interest as candidate materials for ultrafiltration membranes, due to their ability to form isoporous integral-asymmetric membranes by the combined processes of self-assembly and nonsolvent-induced phase separation (SNIPS). However, the dependence of surface layer and substructure morphologies on the processing variables associated with SNIPS is not well understood nor is the interplay between microphase and macrophase separation in block copolymers undergoing such coagulation. Here, we use dynamical self-consistent field theory to simulate the microstructure evolution of block copolymer films during SNIPS and find that such films form the desired sponge-like asymmetric porous substructure only if the solvent and nonsolvent have opposite block selectivities and that otherwise they form a dense nonporous microphase-separated film. Our results could have important implications for the choices of solvent and nonsolvent in the processing of block copolymer membranes.

We develop a multiscale simulation model for diffusion of solutes through porous triblock copolymer membranes. The approach combines two techniques: self-consistent field theory (SCFT) to predict the structure of the self-assembled, solvated membrane and on-lattice kinetic Monte Carlo (kMC) simulations to model diffusion of solutes. Solvation is simulated in SCFT by constraining the glassy membrane matrix while relaxing the brush-like membrane pore coating against the solvent. The kMC simulations capture the resulting solute spatial distribution and concentration-dependent local diffusivity in the polymer-coated pores; we parameterize the latter using particle-based simulations. We apply our approach to simulate solute diffusion through nonequilibrium morphologies of a model triblock copolymer, and we correlate diffusivity with structural descriptors of the morphologies. We also compare the model's predictions to alternative approaches based on simple lattice random walks and find our multiscale model to be more robust and systematic to parameterize. Our multiscale modeling approach is general and can be readily extended in the future to other chemistries, morphologies, and models for the local solute diffusivity and interactions with the membrane.

Liquid-liquid phase separation (LLPS) is a process that results in the formation of a polymer-rich liquid phase coexisting with a polymer-depleted liquid phase. LLPS plays a critical role in the cell through the formation of membrane-less organelles, but it also has a number of biotechnical and biomedical applications such as drug confinement and its targeted delivery. In this chapter, we present a computational efficient methodology that uses field-theoretic simulations (FTS) with complex Langevin (CL) sampling to characterize polymer phase behavior and delineate the LLPS phase boundaries. This approach is a powerful complement to analytical and explicit-particle simulations, and it can serve to inform experimental LLPS studies. The strength of the method lies in its ability to properly sample a large ensemble of polymers in a saturated solution while including the effect of composition fluctuations on LLPS. We describe the approaches that can be used to accurately construct phase diagrams of a variety of molecularly designed polymers and illustrate the method by generating an approximation-free phase diagram for a classical symmetric diblock polyampholyte., (© 2023. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

A versatile and scalable strategy is reported for the rapid generation of block copolymer libraries spanning a wide range of compositions starting from a single parent copolymer. This strategy employs automated and operationally simple chromatographic separation that is demonstrated to be applicable to a variety of block copolymer chemistries on multigram scales with excellent mass recovery. The corresponding phase diagrams exhibit increased compositional resolution compared to those traditionally constructed via multiple, individual block copolymer syntheses. Increased uniformity and lower dispersity of the chromatographic libraries lead to differences in the location of order-order transitions and observable morphologies, highlighting the influence of dispersity on the self-assembly of block copolymers. Significantly, this separation technique greatly simplifies the exploration of block copolymer phase space across a range of compositions, monomer pairs, and molecular weights (up to 50000 amu), producing materials with increased control and homogeneity when compared to conventional strategies.

Coacervation is a common phenomenon in natural polymers and has been applied to synthetic materials systems for coatings, adhesives, and encapsulants. Single-component coacervates are formed when block polyampholytes exhibit self-coacervation, phase separating into a dense liquid coacervate phase rich in the polyampholyte coexisting with a dilute supernatant phase, a process implicated in the liquid-liquid phase separation of intrinsically disordered proteins. Using fully fluctuating field-theoretic simulations using complex Langevin sampling and complementary molecular-dynamics simulations, we develop molecular design principles to connect the sequenced charge pattern of a polyampholyte with its self-coacervation behavior in solution. In particular, the lengthscale of charged blocks and number of connections between oppositely charged blocks are shown to have a dramatic effect on the tendency to phase separate and on the accessible chain conformations. The field and particle-based simulation results are compared with analytical predictions from the random phase approximation (RPA) and postulated scaling relationships. The qualitative trends are mostly captured by the RPA, but the approximation fails catastrophically at low concentration., Competing Interests: The authors declare no conflict of interest.

Control of interfacial interactions leads to a dramatic change in shape and morphology for particles based on poly(styrene-b-2-vinylpyridine) diblock copolymers. Key to these changes is the addition of Au-based surfactant nanoparticles (SNPs) which are adsorbed at the interface between block copolymer-containing emulsion droplets and the surrounding amphiphilic surfactant to afford asymmetric, ellipsoid particles. The mechanism of formation for these novel nanostructures was investigated by systematically varying the volume fraction of SNPs, with the results showing the critical nature that the segregation of SNPs to specific interfaces plays in controlling structure. A theoretical description of the system allows the size distribution and aspect ratio of the asymmetric block copolymer colloidal particles to be correlated with the experimental results.

Free energy evaluation in molecular simulations of both classical and quantum systems is computationally intensive and requires sophisticated algorithms. This is because free energy depends on the volume of accessible phase space, a quantity that is inextricably linked to the integration measure in a coordinate representation of a many-body problem. In contrast, the same problem expressed as a field theory (auxiliary field or coherent states) isolates the particle number as a simple parameter in the Hamiltonian or action functional and enables the identification of a chemical potential field operator. We show that this feature leads a “direct” method of free energy evaluation, in which a particle model is converted to a field theory and appropriate field operators are averaged using a field-theoretic simulation conducted with complex Langevin sampling. These averages provide an immediate estimate of the Helmholtz free energy in the canonical ensemble and the entropy in the microcanonical ensemble. The method is illustrated for a classical polymer solution, a block copolymer melt exhibiting liquid crystalline and solid mesophases, and a quantum fluid of interacting bosons.

Block copolymer self-assembly in conjunction with nonsolvent-induced phase separation (SNIPS) has been increasingly leveraged to fabricate integral-asymmetric membranes. The large number of formulation and processing parameters associated with SNIPS, however, has prevented the reliable construction of high performance membranes. In this study, we apply dynamical self-consistent field theory to model the SNIPS process and investigate the effect of various parameters on the membrane morphology: solvent selectivity, nonsolvent selectivity, initial film composition, and glass transition composition. We examine how solvent selectivity and concentration of polymers in the film impact the structure of micelles that connect to form the membrane matrix. In particular, we find that preserving the order in the surface layer and forming a connection between the supporting and surface layer are nontrivial and sensitive to each parameter studied. The effect of each parameter is discussed, and suggestions are made for successfully fabricating viable block copolymer membranes. [ABSTRACT FROM AUTHOR]

Field-theoretic simulations are numerical treatments of polymer field theory models that go beyond the mean-field self-consistent field theory level and have successfully captured a range of mesoscopic phenomena. Inherent in molecularly-based field theories is a "sign problem" associated with complex-valued Hamiltonian functionals. One route to field-theoretic simulations utilizes the complex Langevin (CL) method to importance sample complex-valued field configurations to bypass the sign problem. Although CL is exact in principle, it can be difficult to stabilize in strongly fluctuating systems. An alternate approach for blends or block copolymers with two segment species is to make a "partial saddle point approximation" (PSPA) in which the stiff pressure-like field is constrained to its mean-field value, eliminating the sign problem in the remaining field theory, allowing for traditional (real) sampling methods. The consequences of the PSPA are relatively unknown, and direct comparisons between the two methods are limited. Here, we quantitatively compare thermodynamic observables, order-disorder transitions, and periodic domain sizes predicted by the two approaches for a weakly compressible model of AB diblock copolymers. Using Gaussian fluctuation analysis, we validate our simulation observations, finding that the PSPA incorrectly captures trends in fluctuation corrections to certain thermodynamic observables, microdomain spacing, and location of order-disorder transitions. For incompressible models with contact interactions, we find similar discrepancies between the predictions of CL and PSPA, but these can be minimized by regularization procedures such as Morse calibration. These findings mandate caution in applying the PSPA to broader classes of soft-matter models and systems. [ABSTRACT FROM AUTHOR]

Reactive blending is an efficient method for synthesizing polymer blends. Industrially, this process is carried out in extruders, where the reacting polymers and the generated copolymer are subjected to high shear stresses. The dynamics of the process, and the resulting morphology is dictated by a coupling of the hydrodynamic forces in the extruder, the thermodynamic interactions between species, and the reaction kinetics on a complex interfacial manifold. We use phase-field simulations to quantify the evolution of the reactive blending process under an external shear flow. Specifically, we consider a model system of two homopolymers of equal length, which react via an end-coupling reaction to form a diblock copolymer of double the length. We compare the morphology development in two different initial geometries of the homopolymers—a cylindrical thread and a drop of one homopolymer in a matrix of the second. We investigate the effect of flow strength, measured by the shear rate, and reaction kinetics, quantified by a Damkohler number, on the progress of the reaction and morphology development. Cylindrical threads are susceptible to breakup via the Rayleigh capillary instability. We demonstrate that this instability can be suppressed by imposing shear along the direction of the thread and increasing the extent of the reaction. The reaction rate in this geometry is unaffected by shear imposed along the cylinder axis. Drops deform significantly under an imposed flow, eventually stretching to long cylindrical threads for sufficient shear rates. In the case of drops, shear stresses enhance the reaction rate by deforming the drop, enabling more homopolymers to come in contact at the expanded interface. We show that shear stresses significantly impact the morphology development and reaction dynamics in reactive polymer blending. [ABSTRACT FROM AUTHOR]

We introduce a stable and efficient complex Langevin (CL) scheme to enable the first direct numerical simulations of the coherent-states (CS) formulation of polymer field theory. In contrast with Edwards' well-known auxiliary-field (AF) framework, the CS formulation does not contain an embedded nonlinear, non-local, implicit functional of the auxiliary fields, and the action of the field theory has a fully explicit, semi-local, and finite-order polynomial character. In the context of a polymer solution model, we demonstrate that the new CS-CL dynamical scheme for sampling fluctuations in the space of coherent states yields results in good agreement with now-standard AF-CL simulations. The formalism is potentially applicable to a broad range of polymer architectures and may facilitate systematic generation of trial actions for use in coarse-graining and numerical renormalization-group studies. [ABSTRACT FROM AUTHOR]

• A machine learning methodology is proposed to accelerate polymer SCFT simulations. • The deep learner is trained in Sobolev space. • The neural network is designed to be invariant under spatial shifts. • The Sobolev space-trained learners are used to accelerate saddle point finding. A new framework that leverages data obtained from self-consistent field theory (SCFT) simulations with deep learning to accelerate the exploration of parameter space for block copolymers is presented. Deep neural networks are adapted and trained in Sobolev space to better capture the saddle point nature of the SCFT approximation. The proposed approach consists of two main problems: 1) the learning of an approximation to the effective Hamiltonian as a function of the average monomer density fields and the relevant physical parameters and 2) the prediction of saddle density fields given the polymer parameters. There is an additional challenge: the effective Hamiltonian has to be invariant under shifts (and rotations in 2D and 3D). A data-enhancing approach and an appropriate regularization are introduced to effectively achieve said invariance. In this first study, the focus is on one-dimensional (in physical space) systems to allow for a thorough exploration and development of the proposed methodology. [ABSTRACT FROM AUTHOR]

Multiblock polymers are a fascinating class of soft materials owing to their spontaneous self-assembly into a variety of ordered mesophases at the nanoscale. However, controlling the ordering and hence the properties is difficult due to a vast number of design parameters including the choice of monomer species, block sequence, block molecular weights and dispersity, polymer architecture, and binary interaction parameters. Navigating through this design space in order to find an optimal formulation for a target property requires an approach that efficiently searches through the countless parameter combinations in an automated fashion. We report on an inverse design strategy to target bulk morphologies utilizing particle swarm optimization (PSO) as a global optimizer and self-consistent-field theory (SCFT) as a forward prediction engine. To avoid metastable states in the forward prediction, we utilize pseudospectral variable cell SCFT initiated from a library of defect-free seeds of known block copolymer morphologies. We demonstrate that our approach allows for a robust identification of block copolymers and copolymer alloys that self-assemble into a targeted structure, optimizing parameters such as block fractions, blend fractions, and Flory χ parameters. [ABSTRACT FROM AUTHOR]

Brush block copolymers (BBCPs) enable the rapid fabrication of self-assembled one-dimensional photonic crystals with photonic band gaps that are tunable in the UV-vis-IR, where the peak wavelength of reflection scales with the molecular weight of the BBCPs. Due to the difficulty in synthesizing very large BBCPs, the fidelity of the assembled lamellar nanostructures drastically erodes as the domains become large enough to reflect IR light, severely limiting their performance as optical filters. To overcome this challenge, short linear homopolymers are used to swell the arrays to ∼180% of the initial domain spacing, allowing for photonic band gaps up to ∼1410 nm without significant opacity in the visible, demonstrating improved ordering of the arrays. Additionally, blending BBCPs with random copolymers enables functional groups to be incorporated into the BBCP array without attaching them directly to the BBCPs. The addition of short linear polymers to the BBCP arrays thus offers a facile means of improving the self-assembly and optical properties of these materials, as well as adding a route to achieving films with greater functionality and tailorability, without the need to develop or optimize the processing conditions for each new brush polymer synthesized. [ABSTRACT FROM AUTHOR]