Poly­(ethylene oxide) (PEO)-based solid polymer electrolytes (SPEs) have attracted much interest due to their high ionic conductivity resulting from inherently fast segmental dynamics and high salt solubility, yet they lack mechanical stability in their neat form. Blending PEO with another rigid, or high glass transition temperature, polymer is a versatile way to improve the mechanical stability; however, the ionic conductivity is strongly reduced due to slower segmental dynamics of highly interpenetrating linear polymer chains. In this work, we used model PEO/PMMA blend systems prepared with various well-defined PEO architectures (linear, stars, hyperbranched, and bottlebrushes) doped with lithium bis­(trifluoromethane-sulfonyl)-imide (LiTFSI) and investigated, for the first time, the role of macromolecular architecture of PEO on crystallization, segmental dynamics, and ionic conductivity in the blends and electrolytes. The results suggest that room-temperature miscibility of these polymers can be dramatically extended by using nonlinear PEO in the blends instead of linear chains, which crystallize above 35 wt %. The broadband dielectric spectroscopy results revealed enhanced decoupling of PMMA and PEO segmental dynamics in compact branched architectures, which helps to achieve faster segmental motion of star PEO in glassy PMMA. This manifests as nearly three-fold higher ionic conductivity in these nonlinear blends compared to the conventional linear PEO/PMMA system. Regardless of the PEO architectures, the temperature dependence of ionic conductivity blends with PMMA and LiTFSI is well defined using the Vogel–Fulcher–Tammann mechanism, suggesting that ion transport is mainly affected by the segmental motion. The activation energy values decrease with the increasing ionic conductivity. Overall, our results show that macromolecular architecture can be a tool to decouple segmental dynamics and ion mobility to rationally design SPEs with improved performance.

We investigate single chain dynamics of an entangled linear poly­(ethylene oxide) melt in the presence of well-dispersed attractive nanoparticles using high-resolution neutron spectroscopy at particle volume fractions as high as 0.53. The short-time dynamics shows a decrease of the Rouse rates with particle loading, yet the change remains within a factor of 2, with no evidence of segment immobilization as often hypothesized. The apparent reptation tube diameter shrinks by ≈10% from the bulk at a 0.28 particle volume fraction when the face-to-face interparticle distance approaches the single chain size. The tube diameter is remarkably concentration-independent at higher loadings where all chains are essentially bound to particle surfaces. These direct experimental observations on the microscopic chain dynamics in attractive nanocomposites are distinct from their nonattractive counterparts and account for some of the unusual dynamic behaviors of the nanoparticles as well as rheology in the composites.

Poly(ethylene oxide) (PEO)-based polymer electrolytes are a promising class of materials for use in lithium-ion batteries due to their high ionic conductivity and flexibility. In this study, the effects of polymer architecture including linear, star, and hyperbranched and salt (lithiumbis(trifluoromethanesulfonyl)imide (LiTFSI)) concentration on the glass transition (T g ), microstructure, phase diagram, free volume, and bulk viscosity, all of which play a significant role in determining the ionic conductivity of the electrolyte, have been systematically studied for PEO-based polymer electrolytes. The branching of PEO widens the liquid phase toward lower salt concentrations, suggesting decreased crystallization and improved ion coordination. At high salt loadings, ion clustering is common for all electrolytes, yet the cluster size and distribution appear to be strongly architecture-dependent. Also, the ionic conductivity is maximized at a salt concentration of [Li/EO ≈ 0.085] for all architectures, and the highly branched polymers displayed as much as three times higher ionic conductivity (with respect to the linear analogue) for the same total molar mass. The architecture-dependent ionic conductivity is attributed to the enhanced free volume measured by positron annihilation lifetime spectroscopy. Interestingly, despite the strong architecture dependence of ionic conductivity, the salt addition in the highly branched architectures results in accelerated yet similar monomeric friction coefficients for these polymers, offering significant potential toward decoupling of conductivity from segmental dynamics of polymer electrolytes, leading to outstanding battery performance.

The Major Histocompatibility Complex (MHC) binds to the derived peptides from pathogens to present them to killer T cells on the cell surface. Developing computational methods for accurate, fast, and explainable peptide-MHC binding prediction can facilitate immunotherapies and vaccine development. Various deep learning-based methods rely on separate feature extraction from the peptide and MHC sequences and ignore their pairwise binding information. This paper develops a capsule neural network-based method to efficiently capture the peptide-MHC complex features to predict the peptide-MHC class I binding. Various evaluations confirmed our method outperformance over the alternative methods, while it can provide accurate prediction over less available data. Moreover, for providing precise insights into the results, we explored the essential features that contributed to the prediction. Since the simulation results demonstrated consistency with the experimental studies, we concluded that our method can be utilized for the accurate, rapid, and interpretable peptide-MHC binding prediction to assist biological therapies.

Polymer nanocomposites exhibit remarkable physical properties that are attractive for many applications. These systems have been so far investigated using linear polymer chains; the role of polymer matrix architecture in local dynamics, bulk rheology, and nanoparticle (NP) motion remains unexplored. Here, using quasi-elastic neutron scattering, bulk rheology, and Xray photon correlation spectroscopy, we investigated nanocomposites with spherical silica nanoparticles well dispersed in poly(ethylene oxide) matrices having different architectures (specifically linear, stars, and hyperbranched). The results reveal that the mechanical reinforcement of the nanocomposites with the nonlinear polymers can be altered by orders of magnitude with respect to the conventional nanocomposite with the linear polymer. Polymer compactness and interpenetrability are found to play crucial roles in determining their bulk rheology. At the microscopic level, average segmental dynamics is remarkably slowed down by the attractive NPs in the matrices of high degree of branching, whereas no significant effect is observed in the linear polymer matrix at the same NP loading. In addition, the nanoscale dynamics of particles in the compact nonlinear matrices exhibits strong decoupling from the bulk viscoelasticity, allowing their fast relaxation even at ≈30% by volume. These results provide an experimental evidence that macromolecular architecture is a powerful new tool for tuning the bulk rheological properties as well as the nanoscale dynamics of polymer nanocomposites (PNCs) without the need for changing polymer molecular weight, nanoparticle size, shape, loading, or dispersion state.