Potency of Urea-Treated Halloysite Nanotubes for the Simultaneous Boosting of Mechanical Properties and Crystallization of Epoxidized Natural Rubber Composites

Halloysite nanotubes (HNTs) are naturally occurring tubular clay made of aluminosilicate sheets rolled several times. HNT has been used to reinforce many rubbers. However, the narrow diameter of this configuration causes HNT to have poor interfacial contact with the rubber matrix. Therefore, increasing the distance between layers could improve interfacial contact with the matrix. In this work, Epoxidized Natural Rubber (ENR)/HNT was the focus. The HNT layer distance was successfully increased by a urea-mechanochemical process. Attachment of urea onto HNT was verified by FTIR, where new peaks appeared around 3505 cm−1 and 3396 cm−1, corresponding to urea’s functionalities. The intercalation of urea to the distance gallery of HNT was revealed by XRD. It was also found that the use of urea-treated HNT improved the modulus, tensile strength, and tear strength of the composites. This was clearly responsible for interactions between ENR and urea-treated HNT. It was further verified by observing the Payne effect. The value of the Payne effect was found to be reduced at 62.38% after using urea for treatment. As for the strain-induced crystallization (SIC) of the composites, the stress–strain curves correlated well with the results from synchrotron wide-angle X-ray scattering.

Design Strategies for Alkaline Exchange Membrane–Electrode Assemblies: Optimization for Fuel Cells and Electrolyzers

Production of hydrocarbon-based, alkaline exchange, membrane–electrode assemblies (MEA’s) for fuel cells and electrolyzers is examined via catalyst-coated membrane (CCM) and gas-diffusion electrode (GDE) fabrication routes. The inability effectively to hot-press hydrocarbon-based ion-exchange polymers (ionomers) risks performance limitations due to poor interfacial contact, especially between GDE and membrane. The addition of an ionomeric interlayer is shown greatly to improve the intimacy of contact between GDE and membrane, as determined by ex situ through-plane MEA impedance measurements, indicated by a strong decrease in the frequency of the high-frequency zero phase angle of the complex impedance, and confirmed in situ with device performance tests. The best interfacial contact is achieved with CCM’s, with the contact impedance decreasing, and device performance increasing, in the order GDE >> GDE+Interlayer > CCM. The GDE+interlayer fabrication approach is further examined with respect to hydrogen crossover and alkaline membrane electrolyzer cell performance. An interlayer strongly reduces the rate of hydrogen crossover without strongly decreasing electrolyzer performance, while crosslinking the ionomeric layer further reduces the crossover rate though also limiting device performance. The approach can be applied and built upon to improve the design and production of alkaline, and more generally, hydrocarbon-based MEA’s and exchange membrane devices.

Deep graph learning of inter-protein contacts

Motivation: Inter-protein (interfacial) contact prediction is very useful for in silico structural characterization of protein-protein interactions. Although deep learning has been applied to this problem, its accuracy is not as good as intra-protein contact prediction. Results: We propose a new deep learning method GLINTER (Graph Learning of INTER-protein contacts) for interfacial contact prediction of dimers, leveraging a rotational invariant representation of protein tertiary structures and a pretrained language model of multiple sequence alignments (MSAs). Tested on the 13th and 14th CASP-CAPRI datasets, the average top L/10 precision achieved by GLINTER is 54.35% on the homodimers and 51.56% on all the dimers, much higher than 30.43% obtained by the latest deep learning method DeepHomo on the homodimers and 14.69% obtained by BIPSPI on all the dimers. Our experiments show that GLINTER-predicted contacts help improve selection of docking decoys.