Impact of Cyclic Block Copolymer Chain Architecture and Degree of Polymerization on Nanoscale Domain Spacing: A Simulation and Scaling Theory Analysis

Snapshot of cyclooctyne ring-opening to a tethered alkylidene cyclic polymer catalyst

Cyclooctyne reacts with the trianionic pincer ligand supported alkylidyne [ t BuOCO]WCC(CH3)3(THF)2 (1) to yield tungstacyclopropene (3) and tungstacyclopentadiene (4) complexes. The ratio of 3 and 4 in the reaction mixture depends on the stoichiometry of the reaction. The maximum concentration of 3 occurs with one equiv. of cyclooctyne and 4 is the exclusive product of the reaction above three equivalents. Both complexes 3 and 4 convert to the cyclooctyne ring-opened product 5 upon heating. While the conversion of 4 to 5 is accompanied by formation of polycyclooctyne as a white precipitate during the reaction, conversion of 3 to 5 is homogeneous. Exhibiting Ring Expansion Polymerization (REP), complexes 4 and 5 initiate the polymerization of phenylacetylene to generate cyclic poly(phenylacetylene) (c-PPA).

Preparation, Properties, and Bioapplications of Block Copolymer Nanopatterns

Block copolymer (BCP) self-assembly has emerged as a feasible method for large-scale fabrication with remarkable precision - features that are not common for most of the nanofabrication techniques. In this review, recent advancements in the molecular design of BCP along with state-of-the-art processing methodologies based on microphase separation alone or its combination with different lithography methods are presented. Furthermore, the bioapplications of the generated nanopatterns in the development of protein arrays, cell-selective surfaces, and antibacterial coatings are explored. Finally, the current challenges in the field are outlined and the potential breakthroughs that can be achieved by adopting BCP approaches already applied in the fabrication of electronic devices are discussed.

Ring Expansion Alkyne Metathesis Polymerization

The synthesis, characterization, and preliminary activity of an unprecedented tethered alkylidyne tungsten complex for ring expansion alkyne metathesis polymerization (REAMP) are reported. The tethered alkylidyne 7 is generated rapidly by combining alkylidyne W(CtBu)(CH2tBu)(O-2,6-i-Pr2C6H3)2 (6) with 1 equiv of an yne-ol proligand (5). Characterized by NMR studies and nuclear Overhauser effect spectroscopy, complex 7 is a dimer. Each metal center contains a tungsten-carbon triple bond tethered to the metal center via an alkoxide ligand. The polymerization of the strained cycloalkyne 3,8-didodecyloxy-5,6-dihydro-11,12-didehydrodibenzo[a,e]-[8]annulene, 8, to generate cyclic polymers was demonstrated. Size exclusion chromatography (SEC) and intrinsic viscosity (η) measurements confirm the polymer's cyclic topology.

Blending Linear and Cyclic Block Copolymers to Manipulate Nanolithographic Feature Dimensions

Block copolymers (BCPs) consist of two or more covalently bound chemically distinct homopolymer blocks. These macromolecules have emerging applications in photonics, membrane separations, and nanolithography stemming from their self-assembly into regular nanoscale structures. Theory suggests that cyclic BCPs should form features up to 40% smaller than their linear analogs while also exhibiting superior thin-film stability and assembly dynamics. However, the complex syntheses required to produce cyclic polymers mean that a need for pure cyclic BCPs would present a challenge to large-scale manufacturing. Here, we employ dissipative particle dynamics simulations to probe the self-assembly behavior of cyclic/linear BCP blends, focusing on nanofeature size and interfacial width as these qualities are critical to nanopatterning applications. We find that for mixtures of symmetric cyclic and linear polymers with equivalent lengths, up to 10% synthetic impurity has a minimal impact on cyclic BCP feature dimensions and interfacial roughness. On the other hand, blending with cyclic BCPs provides a route to "fine-tune" linear BCP feature sizes. We analyze simulated blend domain spacings within the context of strong segregation theory and find significant deviations between simulation and theory that arise from molecular-level packing motifs not included in theory. These insights into blend self-assembly will assist experimentalists in rationally designing BCP materials for advanced nanolithography applications.

Fibril Assembly and Gelation of Macromolecules with Amphiphilic Repeating Units

This paper reports the self-assembly of the fibrillar network in a concentrated solution of macromolecules with an amphiphilic structure of repeating units. The investigation of amphiphilic homopolymers and alternating copolymers with the linear and cyclic topologies, the solution with different polymer concentrations and solvent qualities, allows us to conclude that the ability to form a fibrillar gel with branched fibrils and regular subchain thickness is inherent for macromolecules with the solvophobic backbone and solvophilic pendants. The elements of the gel structure, such as the mesh size and fibrillar thickness, the number of cross-links, and their functionality, can be tuned and customized according to the requirements of their application. The results could be helpful for the directed design of the synthetic analogue of the relevant extracellular matrix, in tissue engineering, for fibrotic disease treatment and cell encapsulation.

Deep learning-based estimation of Flory-Huggins parameter of A-B block copolymers from cross-sectional images of phase-separated structures

In this study, deep learning (DL)-based estimation of the Flory-Huggins χ parameter of A-B diblock copolymers from two-dimensional cross-sectional images of three-dimensional (3D) phase-separated structures were investigated. 3D structures with random networks of phase-separated domains were generated from real-space self-consistent field simulations in the 25-40 χN range for chain lengths (N) of 20 and 40. To confirm that the prepared data can be discriminated using DL, image classification was performed using the VGG-16 network. We comprehensively investigated the performances of the learned networks in the regression problem. The generalization ability was evaluated from independent images with the unlearned χN. We found that, except for large χN values, the standard deviation values were approximately 0.1 and 0.5 for A-component fractions of 0.2 and 0.35, respectively. The images for larger χN values were more difficult to distinguish. In addition, the learning performances for the 4-class problem were comparable to those for the 8-class problem, except when the χN values were large. This information is useful for the analysis of real experimental image data, where the variation of samples is limited.

Lattice self-consistent field calculations of confined symmetric block copolymers of various chain architectures

The effects of chain architecture and confinement on the structure and orientation of lamellae formed by incompressible and symmetric AB-type block copolymer melts confined between two parallel and identical surfaces are investigated using self-consistent field calculations on a simple cubic lattice. Five systems of various chain architectures (linear, ring, and star) and lengths are studied, with their bulk lamellar period L₀ chosen such that they have comparable L₀/R_g, where R_g denotes the ideal-chain radius of gyration. For thin films of thickness D = L₀ confined between two neutral surfaces, we define the rescaled volume fraction profiles of A, B, chain end, and joint segments in the parallel and perpendicular lamellae such that these profiles can be directly compared among the five systems to quantitatively reveal the interplay between the chain-end enrichment near confining surfaces and the surface-induced A-B compatibilization, and how such interplay is affected by the chain architectures (for example, the chain-crowding effects in the star block copolymers). The effects of D and surface preference for one of the blocks are also investigated.