Oxide–Carbon Nanofibrous Composite Support for a Highly Active and Stable Polymer Electrolyte Membrane Fuel-Cell Catalyst

Well-designed electronic configurations and structural properties of electrocatalyst alter the activity, stability, and mass transport for enhanced catalytic reactions. We introduce a nanofibrous oxide–carbon composite by an in situ method of carbon nanofiber (CNF) growth by highly dispersed Ni nanoparticles that are exsoluted from a NiTiO₃ surface. The nanofibrous feature has a 3D web structure with improved mass-transfer properties at the electrode. In addition, the design of the CNF/TiO₂ support allows for complex properties for excellent stability and activity from the TiO₂ oxide support and high electric conductivity through the connected CNF, respectively. Developed CNF/TiO₂–Pt nanofibrous catalyst displays exemplary oxygen-reduction reaction (ORR) activity with significant improvement of the electrochemical surface area. Moreover, exceptional resistance to carbon corrosion and Pt dissolution is proven by durability-test protocols based on the Department of Energy. These results are well-reflected to the single-cell tests with even-better performance at the kinetic zone compared to the commercial Pt/C under different operation conditions. CNF/TiO₂–Pt displays an enhanced active state due to the strong synergetic interactions, which decrease the Pt d-band vacancy by electron transfer from the oxide–carbon support. A distinct reaction mechanism is also proposed and eventually demonstrates a promising example of an ORR electrocatalyst design

Hollow Heteropoly Acid-Functionalized ZIF Composite Membrane for Proton Exchange Membrane Fuel Cells

Heteropoly acids (HPAs) have been used in perfluorinated sulfonic acid polymers such as Nafion or Aquivion to form organic/inorganic composite membranes with improved proton conductivity and water management ability. However, the HPA has a low BET surface area with water-soluble characteristics, which prevents enhancement in the number of proton-transferable sites and accelerates HPA leaching while operating the proton exchange membrane fuel cells (PEMFCs). The HPA was functionalized on zeolite imidazolate framework-67 (ZIF-67) nanoparticles to address these drawbacks. Incorporating it into the MOF made it water insoluble and enhanced the internal surface area, leading to a good proton conductor. Using a synthetic approach, we were able to form HPA-functionalized ZIF-67 (HZF), which can be optimized with simple compositional modifications and whose HPA content is controllable. The HZF nanoparticles exhibited a hollow structure that formed an HPA–ZIF shell layer because the dissociated cobalt ion and 2-methylimidazole diffused from the core side to the surface layer to interact with the HPA. The HZF/Aquivion composite membranes exhibited excellent mechanical properties and good resistance to the polymer chain swelling phenomenon. The electrochemical properties of the HZF/Aquivion composite membranes with various HZFs were characterized to determine the optimal HPA content in the HZF nanoparticles. The 3 wt % hollow HZF/Aquivion composite membrane with the appropriate HPA content exhibited higher proton conductivities than the pure Aquivion membrane, measuring 0.14 S/cm at 25 °C and 100% RH and 0.09 S/cm at 80 °C and 30% RH. This result indicates that the hollow HZF/Aquivion composite membrane can provide efficient proton transfer and water management ability, suggesting a good strategy for the PEMFC operation

Quantitative Structure Relative Volatility Relationship Model for Extractive Distillation of Ethylbenzene/<i>p</i>‑Xylene Mixtures

Extractive distillation is a highly effective process for the separation of compound pairs having low relative volatility values, such as ethylbenzene (EB) and <i>p</i>-xylene (PX) mixtures. Many solvents or solvent mixtures have been screened experimentally to identify a suitable extraction agent for EB/PX mixtures. Because the number of possible solvent and solvent mixture candidates is high, it is necessary to introduce a computer-aided extraction performance prediction technique. In this study, a knowledge-based quantitative structure relative volatility relationship (QSRVR) model was developed using multiple linear regression (MLR) and artificial neural network (ANN) models, with each model having five descriptors. The root-mean-square errors (RMSE) of the training and test sets for the MLR model were calculated as 0.01486 and 0.00905, while their squared correlation coefficients (<i>R</i>²) were 0.867 and 0.941, respectively. The <i>R</i>² and RMSE values of the total data set for the MLR model were 0.878 and 0.01408, and for the ANN model the values were 0.949 and 0.00929, respectively. The predictive ability of both models is sufficient for identifying suitable extractive distillation solvents for the separation of EB/PX mixtures