Block polymer (BP) electrolytes offer significant advantages relative to existing liquid or polymer electrolytes due to their independently tunable ion transport and mechanical stability properties as a result of nanoscale self-assembly. Many of these nanostructured electrolytes are composed of a BP that is doped with a lithium salt to impart conductivity but which also alters the self-assembly (structure and thermodynamics) in comparison to the neat BP. By elucidating the effects of lithium salt concentration and counterion chemistry on the relevant salt and polymer density distributions, BP electrolytes with more efficient conductivity pathways can be developed. In this work, neutron and X-ray reflectometry (NR and XRR, respectively) were harnessed to determine the spatial distribution of salt and polymer in lamellae-forming polystyrene-block-poly­(oligo-oxyethylene methacrylate) [PS-b-POEM] films doped with various lithium salts. From the NR results, the distribution of lithium salts across domains appeared to match that of the POEM in the BP electrolyte for all salts tested. This finding of a salt distribution that was directly proportional to the POEM density profile facilitated quantitative analysis of polymer and salt XRR profiles using a strong-segregation theory framework. Through this approach, effective Flory–Huggins interaction parameters (χeff)­s were deconvoluted from POEM statistical segment lengths (b POEM)­s. For all salts tested, χeff increased at low salt concentrations and then plateaued at higher salt concentrations, while b POEM increased linearly across all salt concentrations. These findings can be leveraged to advance the next generation of salt-doped BP electrolyte materials that enhance the performance and mechanical stability of lithium-ion batteries.

Nanostructure-forming polymers have tremendous potential to enhance the performance and safety of lithium-ion batteries (LiBs) as a result of their ability to simultaneously optimize often contradictory properties, such as ionic conductivity and mechanical stability, in a single material. These macromolecules can be harnessed in both LiB electrolyte and electrode components. With respect to electrolytes, advances in salt-doped and single-ion systems are highlighted herein with a focus on strategies that improve conductivities to rival that found in gel and liquid electrolytes, while also permitting further enhancements in electrochemical and mechanical stability. In the arena of electrodes, three major functions are considered: binders to maximize active material efficiency, polymer electrodes to enable fully organic LiBs, and sacrificial constructs that template high surface area, well-ordered metal oxide or metallic electrodes to improve electrode capacity. Additionally, the application of theory and simulation to streamline the development of key structure–property−processing relationships in ion-conducting nanomaterials is discussed. Finally, several next steps and future directions are suggested to accelerate the fabrication of next-generation LiBs.

The optimization of ionic conductivity and lithium-ion battery stability can be achieved by independently tuning the ion transport and mechanical robustness of block polymer (BP) electrolytes. However, the ionic conductivity of BP electrolytes is inherently limited by the covalent attachment of the ionically conductive block to the mechanically robust block, among other factors. Herein, the BP electrolyte polystyrene-block-poly(oligo-oxyethylene methacrylate) [PS-b-POEM] was blended with POEM homopolymers of varying molecular weights. The incorporation of a higher molecular weight homopolymer additive (α > 1 state) promoted a "dry brush-like" homopolymer distribution within the BP self-assembly and led to higher lithium salt concentrations in the more mobile homopolymer-rich region, increasing overall ionic conductivity relative to the "wet brush-like" (α < 1 state) and unblended composites, where α is the molecular weight ratio between the POEM homopolymer and the POEM block in the copolymer. Neutron and X-ray reflectometry (NR and XRR, respectively) provided additional details on the lithium salt and polymer distributions. From XRR, the α > 1 blends showed increased interfacial widths in comparison to their BP (unblended) or α < 1 counterparts because of the more central distribution of the homopolymer. This result, paired with NR data that suggested even salt concentrations across the POEM domains, implied that there was a higher salt concentration in the homopolymer POEM-rich regions in the dry brush blend than in the wet brush blend. Furthermore, using 7 Li solid-state nuclear magnetic resonance spectroscopy, we found a temperature corresponding to a transition in lithium mobility (T Li mobility ) that was a function of blend type. T Li mobility was found to be 39 °C above T g in all cases. Interestingly, the ionic conductivity of the blended BPs was highest in the α > 1 composites, even though these composites had higher T g s than the α < 1 composites, demonstrating that homopolymer-rich conducting pathways formed in the α > 1 assemblies had a larger influence on conductivity than the greater lithium ion mobility in the α < 1 blends.

BigSMILES, a line notation for encapsulating the molecular structure of stochastic molecules such as polymers, was recently proposed as a compact and readable solution for writing macromolecules. While BigSMILES strings serve as useful identifiers for reconstructing the molecular connectivity for polymers, in general, BigSMILES allows the same polymer to be codified into multiple equally valid representations. Having a canonicalization scheme that eliminates the multiplicity would be very useful in reducing time-intensive tasks like structural comparison and molecular search into simple string-matching tasks. Motivated by this, in this work, two strategies for deriving canonical representations for linear polymers are proposed. In the first approach, a canonicalization scheme is proposed to standardize the expression of BigSMILES stochastic objects, thereby standardizing the expression of overall BigSMILES strings. In the second approach, an analogy between formal language theory and the molecular ensemble of polymer molecules is drawn. Linear polymers can be converted into regular languages, and the minimal deterministic finite automaton uniquely associated with each prescribed language is used as the basis for constructing the unique text identifier associated with each distinct polymer. Overall, this work presents algorithms to convert linear polymers into unique structure-based text identifiers. The derived identifiers can be readily applied in chemical information systems for polymers and other polymer informatics applications.

Block polymers (BPs) are ideal building blocks for nanoporous membranes, microelectronics templates, energy storage devices, and other technologies. For these systems, precise control over morphology, properties, and processability are essential to optimize performance. The Epps group has used a multidisciplinary approach to control BP nanostructure ordering and orientations, order-disorder and glass transition temperatures, and mechanical and transport properties through the informed manipulation of BP effective segregation strengths and interfacial energetics. For example, alterations in macromolecular interactions between BP chains were achieved through the introduction of composition gradients between copolymer blocks or by adding dopants. In thin films, high-throughput approaches were designed to modify and characterize substrate-polymer interactions, and solvent annealing techniques were developed to facilitate nanoscale alignment and ordering. By manipulating BP interfacial energetics to gain exquisite tunability of nanostructures and materials' properties, the group is accelerating the development of next-generation BPs that will be harnessed to fabricate inexpensive, efficient, and high-performance devices.

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...

Natural selective filtering systems (e.g., the extracellular matrix, nuclear pores, and mucus) separate molecules selectively and efficiently, and the detailed understanding of transport mechanisms exploited in these systems provides important bioinspired design principles for selective filters. In particular, nucleoporins consist of consensus repeat sequences that are readily utilized for engineering repeat proteins. Here, the consensus repeat sequence of Nsp1, a yeast nucleoporin, is polymerized to form a nucleoporin-like protein (NLP) and mutated to understand the effect of sequence on selective transport. The hydrophilic spacers of the NLPs were redesigned considering net charge, charge distribution, and polarity. Mutations were made near to and far from the FSFG interacting domain to explore the role of highly conserved residues as a function of spatial proximity. A nuclear transport receptor–cargo complex, nuclear transport factor 2-green fluorescent protein (NTF2-GFP), was used as a model for changes in transport. For mutations of the charged spacer, some mutations of highly conserved charged residues were possible without knocking out selective transport of the NTF2, but the formation of regions of clustered negative charge has an unfavorable effect on nuclear transporter permeation. Thus, positive net charge and alternating positive and negative charge within the hydrophilic spacer are advantageous for recognition and selective transport. In the polarity panel, mutations that increased the interaction between NTF2-GFP and the gel led to decreased permeation of the NTF2-GFP due to blocking of the interface and inability of the NTF2-GFP to transport into the gel. Therefore, these results provide a strategy for tuning selective permeability of biomolecules using the artificially designed consensus repeat-based hydrogels.

Low-cost, high-throughput methods for the determination of high-yield protein expression conditions are developed and verified, to enable the rapid development of new protein materials, such as biosensors and biomaterials.