Effect of Polymer Composition and Morphology on Mechanochemical Activation in Nanostructured Triblock Copolymers

Abstract

The effect of composition and morphology on mechanochemical activation in nanostructured block copolymers was investigated in a series of poly(methyl methacrylate)-block-poly(n-butyl acrylate)-block-poly(methyl methacrylate) (PMMA-b-PnBA-b-PMMA) triblock copolymers containing a force-responsive spiropyran unit in the center of the rubbery PnBA midblock. Triblock copolymers with identical PnBA midblocks and varying lengths of PMMA end-blocks were synthesized from a spiropyran-containing macroinitiatior via atom transfer radical polymerization, yielding polymers with volume fractions of PMMA ranging from 0.21 to 0.50. Characterization by transmission electron microscopy revealed that the polymers self-assembled into spherical and cylindrical nanostructures. Simultaneous tensile tests and optical measurements revealed that mechanochemical activation is strongly correlated to the chemical composition and morphologies of the triblock copolymers. As the glassy (PMMA) block content is increased, the overall activation increases, and the onset of activation occurs at lower strain but higher stress, which agrees with predictions from our previous computational work. These results suggest that the self-assembly of nanostructured morphologies can play an important role in controlling mechanochemical activation in polymeric materials and provide insights into how polymer composition and morphology impact molecular-scale force distributions.

Fabrication of poly(4-vinylpyridine)-b-polystyrene-b-poly(4-vinylpyridine) triblock copolymer particles via three-dimensional soft confined self-assembly

“…9a. 42,[52][53][54] The extended length (L) of the middle PS block with a zig-zag conformation is estimated to be about 58.4 nm by using L PS = 2Nl C-C sin(θ/2) (N is the degree of polymerization of PS, l C-C is the bond length of C-C, and θ is the bond angle of C-C), as shown in Fig. 9b.…”

Quantifying Pairwise Similarity for Complex Polymers

“…These similarity scoring methods have been used for a variety of tasks, such as calculating the similarity of entries in a drug molecule library, designing new drug molecules, ranking search results, and calculating the magnitude of a chemical change from one small molecule to another . Specialized machine learning methods also exist for similarity calculations of sequence-defined biomacromolecules, such as proteins, peptides, and polysaccharides. , Both small molecules and sequence-defined biomacromolecules have well-defined deterministic structures that are easily represented by graphs with atoms (or molecular fragments) as nodes and bonds as edges. ,– In contrast, the vast majority of synthetic polymers are characterized by stochastic graphs that represent molecular ensembles or distributions. , Previous studies have used monomers and compositions as representations and utilized methods similar to those developed for small molecules to measure pairwise polymer similarity, but those methods can only be applied to polymers with simple topologies, such as homopolymers and copolymers. – These methods do not take into consideration the variety of topologies and stochastic configurations available to polymers; therefore, it is not possible for these methods to obtain an accurate and meaningful similarity score for polymers with complex topologies and stochastic properties, such as star polymers, graft polymers, and segmented polymers.…”

Preferential Mechanochemical Activation of Short Chains in Bidisperse Triblock Elastomers

“…As illustrated in Figure , we exploit the self-assembly of bidisperse triblock copolymer blends to generate network-like structures with mixtures of short and long chains. In these materials, self-assembly of the glassy end-blocks creates physical cross-links between the chains, which transmit force to the rubbery midblock and drive activation of mechanophores in the centers of the polymer chains. − By changing which component of the blend contains mechanophores (short chains, long chains, or both), we are effectively able to “label” and probe the activation of chains with different elastic strand lengths within otherwise equivalent networks. We find that activation occurs primarily in the short chains, which molecular dynamics (MD) simulations reveal is driven in large part by pinning of the elastically effective midblock ends to the glassy/rubbery interface.…”

“…The mechanical properties and mechanochemical activation profiles of the materials were then characterized by measuring changes in their optical absorption between 550 and 630 nm during uniaxial tensile deformation . The stress–strain and activation–strain profiles of all three blends are shown in Figure .…”