Dissipative particle dynamics for directed self-assembly of block copolymers

Directed Self-Assembly by Sparsely Prepatterned Substrates with Self-Responsive Polymer Brushes

The guiding pattern in the chemoepitaxially directed self-assembly (DSA) of block copolymers is often fabricated by periodically functionalizing homogeneously random copolymer brushes tethered on a substrate. The prepatterned copolymer brushes constitute a soft penetrable surface, and their two components can in principle locally segregate in response to the overlying self-assembly process of block copolymers. To reveal how the self-responsive behavior of the copolymer brushes affects the directing effect, we develop a dissipative particle dynamics model to explicitly include the prepatterned polymer brushes and implement it to simulate the DSA of a cylinder-forming diblock copolymer melt on the sparse pattern of polymer brushes. Through large-scale dynamic simulations, we identify the windows of the content of the random copolymer, the film thickness, and the diameter of the patterned spot, for the formation of perfectly ordered hexagonal patterns composed of perpendicular cylinders. Our dynamic simulations reveal that the random copolymer brushes grafted on the unpatterned area exhibit a remarkable self-responsive ability with respect to the self-assembly of the diblock copolymers overlying them, which may widen the effective window of the content of the random copolymer. Within the processing windows of these key parameters, defect-free patterns are successfully achieved both in simulations and in experiments with sizes as large as a few micrometers for 4-fold density multiplications. This work demonstrates that highly efficient computer simulations based on an effective model can provide helpful guidance for experiments to optimize the critical parameters and even may promote the application of DSA.

A Novel Demulsifier with Strong Hydrogen Bonding for Effective Breaking of Water-in-Heavy Oil Emulsions

In the heavy petroleum industry, the development of efficient demulsifiers for the effective breaking of interfacially active asphaltenes (IAA)-stabilized water-in-heavy oil (W/HO) emulsions is a highly attractive but challenging goal. Herein, a novel nitrogen and oxygen containing demulsifier (JXGZ) with strong hydrogen bonding has been successfully synthesized through combining esterification, polymerization and amidation. Bottle tests indicated that JXGZ is effectual in quickly demulsifying the IAA-stabilized W/HO emulsions; complete dehydration (100%) to the emulsions could be achieved in 4 min at 55 °C using 400 ppm of JXGZ. In addition, the effects of demulsifier concentration, temperature and time on the demulsification performance of JXGZ are systematically analyzed. Demulsification mechanisms reveal that the excellent demulsification performance of JXGZ is attributed to the strong hydrogen bonding between JXGZ and water molecules (dual swords synergistic effect under hydrogen bond reconstruction). The interaction of the "dual swords synergistic effect" generated by two types of hydrogen bonds can quickly break the non-covalent interaction force (π-π stacking, Van der Waals force, hydrogen bonds) of IAA at the heavy oil-water interface, quickly promote the aggregation and coalescence of water molecules and finally achieve the demulsification of W/HO emulsions. These findings indicate that the JXGZ demulsifier shows engineering application prospects in the demulsification of heavy oil-water emulsions, and this work provides the key information for developing more efficient chemical demulsifiers suitable for large-scale industrial applications.

Emergence of layered nanoscale mesh networks through intrinsic molecular confinement self-assembly

Block copolymer self-assembly is a powerful tool for two-dimensional nanofabrication; however, the extension of this self-assembly concept to complex three-dimensional network structures is limited. Here we report a simple method to experimentally generate three-dimensional layered mesh morphologies through intrinsic molecular confinement self-assembly. We designed triblock bottlebrush polymers with two Janus domains: one perpendicular and one parallel to the polymer backbone. The former enforces a lamellar superstructure that intrinsically confines the intralayer self-assembly of the latter, giving rise to a mesh-like monoclinic (54°) M¹⁵ network substructure with excellent long-range order, as well as a tetragonal (90°) T¹³¹ mesh. Numerical simulations show that the spatial constraints exerted on the polymer backbone drive the assembly of M¹⁵ and yield T¹³¹ in the strong segregation regime. This work demonstrates that intrinsic molecular confinement is a viable path to bottom-up assembly of new geometrical phases of soft matter, extending the capabilities of block copolymer nanofabrication.

An extensible density-biasing approach for molecular simulations of multicomponent block copolymers

A node-density biased Monte Carlo methodology is proposed for the molecular structure generation of complex block copolymers. Within this methodology, the block copolymer is represented as bead-spring model. Using self-consistent field theory, a density field for all monomer species within the system is calculated. Block copolymers are generated by random walk configuration biased by the density fields. The proposed algorithm then modifies the generation process by taking the global structure of the polymer into account. It is then demonstrated that these global considerations can be built into the sampling procedure, specifically through functions that assign a permissible difference in density field value between relevant monomer species to each step of the random walk. In this way, the random walk may be naturally controlled to provide the most appropriate conformations. The overall viability of this approach has been demonstrated by using the resulting configurations in molecular dynamics simulations. This new methodology is demonstrated to be powerful enough to generate molecular configurations for a much wider variety of materials than the original approach. Two key examples of the new capabilities of the method are viable configurations for ABABA pentablock copolymers and ABC triblock terpolymers.

Competitive Registration Fields for The Development of Complex Block Copolymer Structures by A Layer-by-Layer Approach

Block copolymer (BCP) self-assembly in thin films is an elegant method to generate nanometric features with tunable geometrical configurations. By combining directed assembly and hybridization methods, advances in nano-manufacturing have been attested over the past decades with flagship applications in lithography and optics. Nevertheless, the range of geometrical configurations is limited by the accessible morphologies inherent to the energy minimization process involved in BCP self-assembly. Layering of nanostructured BCP thin films has been recently proposed in order to enrich the span of nanostructures derived from BCP self-assembly with the formation of non-native heterostructures such as double-layered arrays of nanowires or dots-on-line and dots-in-hole hierarchical structures. In this work, the layer-by-layer method is further exploited for the generation of nano-mesh arrays using nanostructured BCP thin films. In particular, a subtle combination of chemical and topographical fields is leveraged in order to demonstrate design rules for the controlled registration of a BCP layer on top of an underneath immobilized one by the precise tuning of the interfacial chemical field between the two BCP layers.

Experimental and Computational Evaluation of Self-Assembled Morphologies in Diblock Janus Bottlebrush Copolymers

Diblock Janus-type "A-branch-B" bottlebrush copolymers (di-JBBCPs) consist of a backbone with alternating A and B side chains, in contrast to the side chain arrangement of conventional bottlebrush copolymers. As a result, A and B blocks of di-JBBCPs can microphase-separate perpendicular to the backbone, which is located at the interface between the two blocks. A reparametrized dissipative particle dynamics (DPD) model is used to theoretically investigate the self-assembly of di-JBBCPs and to compare with the experimental results of a range of polystyrene-branch-polydimethylsiloxane di-JBBCPs. The experimentally formed cylinder, gyroid, and lamellar morphologies showed good correspondence with the model phase diagram, and the effect of changing volume fraction and backbone length is revealed. The DPD model predicts a bulk-stable perforated lamella morphology together with two unconventional spherical phases, the Frank-Kasper A15 spheres and the hexagonally close-packed spheres, indicating the diversity of morphologies available from complex BCP molecular architectures.

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.

Symmetry-Breaking and Self-Sorting in Block Copolymer-Based Multicomponent Nanocomposites

Co-assembly of inorganic nanoparticles (NPs) and nanostructured polymer matrix represents an intricate interplay of enthalpic or entropic forces. Particle size largely affects the phase behavior of the nanocomposite. Theoretical studies indicate that new morphologies would emerge when the particles become comparable to the soft matrix's size, but this has rarely been supported experimentally. By designing a multicomponent blend composed of NPs, block copolymer-based supramolecules, and small molecules, a 3-D ordered lattice beyond the native BCP's morphology was recently reported when the particle is larger than the microdomain of BCP. The blend can accommodate various formulation variables. In this paper, when the particle size equals the microdomain size, a symmetry-broken phase appears in a narrow range of particle sizes and compositions, which we named the "train track" structure. In this phase, the NPs aligned into a 3-D hexagonal lattice and packed asymmetrically along the c axis, making the projection of the ac and the bc plane resemble train tracks. Computational studies show that the broken symmetry reduces the polymer chain deformation and stabilizes the metastable hexagonally perforated lamellar morphology. Given the mobility of the multicomponent blend, the system shows a self-sorting behavior: segregating into two macroscopic phases with different nanostructures based on only a few nanometers NP size differences. Smaller NPs form "train track" morphology, while larger NPs form a "simple hexagon" structure, where the NPs take a symmetric hexagonal arrangement. Detailed structural evolution and simulation studies confirm the systematic-wide cooperativity across different components, indicating the strong self-regulation of the multicomponent system.

DPD Modelling of the Self- and Co-Assembly of Polymers and Polyelectrolytes in Aqueous Media: Impact on Polymer Science

This review article is addressed to a broad community of polymer scientists. We outline and analyse the fundamentals of the dissipative particle dynamics (DPD) simulation method from the point of view of polymer physics and review the articles on polymer systems published in approximately the last two decades, focusing on their impact on macromolecular science. Special attention is devoted to polymer and polyelectrolyte self- and co-assembly and self-organisation and to the problems connected with the implementation of explicit electrostatics in DPD numerical machinery. Critical analysis of the results of a number of successful DPD studies of complex polymer systems published recently documents the importance and suitability of this coarse-grained method for studying polymer systems.

Metallic Nanomeshes Fabricated by Multimechanism Directed Self-Assembly

The directed self-assembly of block copolymers (BCPs) is a powerful motif for the continued scaling of feature sizes for nanoscale devices. A multimechanism directed self-assembly (MMDSA) method is described that generates orthogonal meshes from a polystyrene-b-poly-2-vinylpyridine BCP that is subsequently metallized with Pt. The MMDSA process takes advantage of three different mechanisms, trench wall guidance, edge nucleation, and underlayer guidance, to align the mesh with respect to substrate features. The mechanisms and their interactions are investigated via both experiments and dissipative particle dynamics simulations. MMDSA is applied to produce well-aligned conductive nanomeshes and then is extended to fabricate multicomponent metallic structures with 2D/3D hybrid morphologies.

Diversifying Composition Leads to Hierarchical Composites with Design Flexibility and Structural Fidelity

Although significant progress has been made in the self-assembly of nanostructures, present successes heavily rely on precision in building block design, composition, and pair interactions. These requirements fundamentally limit our ability to synthesize macroscopic materials where the likelihood of impurity inclusion escalates and, more importantly, to access molecular-to-nanoscopic-to-microscopic-to-macroscopic hierarchies, since the types and compositions of building blocks vary at each stage. Inspired by biological blends and high-entropy alloys, we hypothesize that diversifying the blend's composition can overcome these limitations. Increasing the number of components increases mixing entropy, leading to the dispersion of different components and, as a result, enhances interphase miscibility, weakens the dependence on specific pair interactions, and enables long-range cooperativity. This hypothesis is validated in complex blends containing small molecules, block copolymer-based supramolecules, and nanoparticles/colloidal particles. Hierarchically structured composites can be obtained with formulation flexibility in the filler selection and blend composition. It is worth noting that, by adding small molecules, we can solve the size constraint that plagues traditional block copolymer/nanoparticle blends. Detailed characterization and simulation further confirm that each component is distributed to locally mediate unfavorable interactions, cooperatively mitigate composition fluctuations, and retain structural fidelity. Furthermore, the blends have sufficient mobility to access tunable microstructures without compromising the order of the nanostructure. Besides establishing a kinetically viable pathway to release current constraints in the composite design and to navigate uncertainties during structure formation over multiple length scales, the present study demonstrates that entropy-driven behaviors can be realized in systems beyond high-entropy alloys despite inherent differences between metal alloys and organic/inorganic hybrids.

Self-Directed Self-Assembly of 3D Tailored Block Copolymer Nanostructures

Directed self-assembly (DSA) of block copolymers (BCPs) provides a powerful tool to fabricate various 2D nanostructures. However, it still remains a challenge to extend DSA to make uniform and complex 3D nanostructures through BCP self-assembly. In this paper, we introduce a method to fabricate various nanostructures in 3D and test it using simulations. In particular, we employ dissipative particle dynamics (DPD) simulation to demonstrate that uniform multilayer nanostructures can be achieved by alternating the stacking of two "orthogonal" BCPs films, AB copolymer film and AC copolymer film, without the need to cross-link or etch any of the components. The assembly of a new layer occurs on top of the previous bottom layer, and thus the structural information from the substrate is propagated upward in the film, a process we refer to as self-directed self-assembly (SDSA). If this process is repeated many times, one can have tailored multilayer nanostructures. Furthermore, the natural (bulk) phases of the block copolymers in each layer do not need to be the same, so one can achieve complex 3D assemblies that are not possible with a single-phase 3D system. This method in conjunction with grapho (or chemo) epitaxy is able to evolve a surface pattern into a 3D nanostructure. Here we show several examples of nanostructures fabricated by this process, which include aligned cylinders, spheres on top of cylinders, and orthogonal nanomeshes. Our work should be useful for creating complex 3D nanostructures using self-assembly.