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We review a selection of models for wormlike micelles undergoing reptation and chain sequence rearrangement (e.g. reversible scission) and show that many different assumptions and approximations all produce similar predictions for linear rheology. Therefore, the inverse problem of extracting quantitative microscopic information from linear rheology data alone may be ill-posed without additional supporting data to specify the sequence rearrangement pathway. At the same time, qualitative parameter estimates can be obtained equally well from any of the models in question. Through our study, we also show that the Poisson renewal model can be reformulated as a differential constitutive equation on the tube survival prob- ability distribution function. Using this reformulation, we identify two previously overlooked inconsistencies with Poisson renewal and discuss how these can be resolved by re-interpreting what the model calls a `breaking time'. 

The effect of branches on the linear rheology of entangled wormlike micelle solutions is modeled by tracking the diffusion of micellar material through branch points. The model is equivalent to a Kirchhoff circuit model with the sliding of an entangled branch along an entanglement tube due to the constrained diffusion of micellar material analogous to the flux of current in the Kirchhoff circuit model. When combined with our previous mesoscopic pointer algorithm for linear micelles that can both break and fuse, the model adds a branch sprouting process and therefore enables simulation of the dynamics of structural change and stress relaxation in ensembles of micelle clusters of different topologies. Applying this new model to study the relationships between fluid rheology and microstructure of micelles, our results show that branches change the scaling law exponents for viscosity versus micelle strand length. This contrasts with the long-standing hypothesis that branches affect viscosity and relaxation in the same way that micelle ends do. The model also suggests a process for inferring branching density from salt-dependent linear rheology. This is exemplified by mixed surfactant solutions over a range of salt concentrations with flow properties measured using both mechanical rheometry and diffusing wave spectroscopy (DWS). By elucidating the connection between the branching characteristics, such as strand length and branching density, with the nonmonotonic variation of solution viscosity, the above model provides a powerful new tool to help extract branching information from rheology. 

Defining the similarity between chemical entities is an essential task in polymer informatics, enabling ranking, clustering, and classification. Despite its importance, the pairwise chemical similarity of polymers remains an open problem. Here, a similarity function for polymers with well-defined backbones is designed based on polymers’ stochastic graph representations generated from canonical BigSMILES, a structurally based line notation for describing macromolecules. The stochastic graph representations are separated into three parts: repeat units, end groups, and polymer topology. The earth mover’s distance is utilized to calculate the similarity of the repeat units and end groups, while the graph edit distance is used to calculate the similarity of the topology. These three values can be linearly or nonlinearly combined to yield an overall pairwise chemical similarity score for polymers that is largely consistent with the chemical intuition of expert users and is adjustable based on the relative importance of different chemical features for a given similarity problem. This method gives a reliable solution to quantitatively calculate the pairwise chemical similarity score for polymers and represents a vital step toward building search engines and quantitative design tools for polymer data. 

Abstract Thermoset toughness and deconstructability are often opposing features; simultaneously improving both without sacrificing other mechanical properties (e.g., stiffness and tensile strength) is difficult, but, if achieved, could enhance the usage lifetime and end‐of‐life options for these materials. Here, a strategy that addresses this challenge in the context of photopolymer resins commonly used for 3D printing of glassy, acrylic thermosets is introduced. It is shown that incorporating bis‐acrylate “transferinkers,” which are cross‐linkers capable of undergoing degenerative chain transfer and new strand growth, as additives (5–25 mol%) into homemade or commercially available photopolymer resins leads to photopolymer thermosets with substantially improved tensile toughness and triggered chemical deconstructability with minimal impacts on Young's moduli, tensile strengths, and glass transition temperatures. These properties result from a transferinker‐driven topological transition in network structure from the densely cross‐linked long, heterogeneous primary strands of traditional photopolymer networks to more uniform, star‐like networks with few dangling ends; the latter structure more effectively bear stress yet is also more easily depercolated via solvolysis. Thus, transferinkers represent a simple and effective strategy for improving the mechanical properties of photopolymer thermosets and providing a mechanism for their triggered deconstructability. 

As a machine-recognizable representation of polymer connectivity, BigSMILES line notation extends SMILES from deterministic to stochastic structures. The same framework that allows BigSMILES to accommodate stochastic covalent connectivity can be extended to non-covalent bonds, enhancing its value for polymers, supramolecular materials, and colloidal chemistry. Non-covalent bonds are captured through the inclusion of annotations to pseudo atoms serving as complementary binding pairs, minimal key/value pairs to elaborate other relevant attributes, and indexes to specify the pairing among potential donors and acceptors or bond delocalization. Incorporating these annotations into BigSMILES line notation enables the representation of four common classes of non-covalent bonds in polymer science: electrostatic interactions, hydrogen bonding, metal–ligand complexation, and π–π stacking. The principal advantage of non-covalent BigSMILES is the ability to accommodate a broad variety of non-covalent chemistry with a simple user-orientated, semi-flexible annotation formalism. This goal is achieved by encoding a universal but non-exhaustive representation of non-covalent or stochastic bonding patterns through syntax for (de)protonated and delocalized state of bonding as well as nested bonds for correlated bonding and multi-component mixture. By allowing user-defined descriptors in the annotation expression, further applications in data-driven research can be envisioned to represent chemical structures in many other fields, including polymer nanocomposite and surface chemistry.