Functional Group-Dependent Proton Conductivity of Phosphoric Acid-Doped Ion-Pair Coordinated Polymer Electrolytes: A Molecular Dynamics Study.

Toward deployment of high-temperature polymer electrolyte membrane fuel cells (HT-PEMFCs) in our daily lives, multiple research efforts have been dedicated to develop high-performance phosphate-doped polymer electrolytes. Recently, ion-pair coordinated polymers have garnered attention for their high stability and proton conductivity. However, a comprehensive understanding of how proton transport properties are modified by the functional groups present in these polymers is still lacking. In this study, we employ molecular dynamics (MD) simulations to investigate the impact of different functional group types and conversion ratios on conductivity. We find that Grotthuss-type hopping transport predominantly governs the overall conductivity, surpassing vehicular transport by factors of 100-1000. As conductivity scales with proton concentration, we observe that less-bulky functional groups offer advantages by minimizing the volume expansion associated with increased conversion ratios. Additionally, we show that a strong ion-pair interaction between the cationic functional group and the phosphate anion disrupts the suitable intermolecular orientations required for efficient proton hopping between phosphate and phosphoric acid molecules, thereby diminishing the proton conductivity. Our study underscores the importance of optimizing the strength of ion-pair interactions to balance stability and proton conductivity, thus paving the way for the development of ion-pair coordinated polymer electrolytes with improved performance.

First-principles database driven computational neural network approach to the discovery of active ternary nanocatalysts for oxygen reduction reaction.

An elegant machine-learning-based algorithm was applied to study the thermo-electrochemical properties of ternary nanocatalysts for oxygen reduction reaction (ORR). High-dimensional neural network potentials (NNPs) for the interactions among the components were parameterized from big dataset established by first-principles density functional theory calculations. The NNPs were then incorporated with Monte Carlo (MC) and molecular dynamics (MD) simulations to identify not only active, but also electrochemically stable nanocatalysts for ORR in acidic solution. The effects of surface strain caused by selective segregation of certain components on the catalytic performance were accurately characterized. The computationally efficient and precise approach proposes a promising ORR candidate: 2.6 nm icosahedron comprising 60% of Pt and 40% Ni/Cu. Our methodology can be applied for high-throughput screening and designing of key functional nanomaterials to drastically enhance the performance of various electrochemical systems.

Carrier scattering in quasi-free standing graphene on hexagonal boron nitride.

Graphene, a two-dimensional material with a honeycomb lattice, has been promoted as a next generation material because of its ultrafast charge carriers and superior electrical properties. Hexagonal boron nitride (h-BN) is an insulator explored as an ideal substrate for graphene with lattice-matching. Using raido-frequency (RF) transmission measurement which provides specific characteristics of carrier scattering in a device, we profoundly investigated the electrical properties of quasi-free standing graphene on h-BN. RF devices with graphene supported and encapsulated with h-BN were fabricated to analyze the RF signal at low temperatures from 100 to 300 K. We demonstrated the carrier behavior in graphene with thermally excited carriers and acoustic photon scattering according to heat energy. Both h-BN supported and encapsulated graphene showed a significant enhancement in RF transmission, which is close to a gold interconnector. Our device with graphene on h-BN exhibited concealed nonlinear characteristics at a specific temperature of 180 K due to the internal effects of acoustic phonon scattering, while a usual device with graphene on SiO2/Si provided a linear variation. To anticipate the potential for electronic applications, the electrical circuit properties such as impedance, resistance, and inductance were extracted from the results of RF measurement.

Self-assembled nitrogen-doped fullerenes and their catalysis for fuel cell and rechargeable metal-air battery applications.

In this study, we report self-assembled nitrogen-doped fullerenes (N-fullerene) as non-precious catalysts, which are active for the oxygen reduction reaction (ORR) and oxygen evolution reaction (OER), and thus applicable for energy conversion and storage devices such as fuel cells and metal-air battery systems. We screen the best N-fullerene catalyst at the nitrogen doping level of 10 at%, not at the previously known doping level of 5 or 20 at% for graphene. We identify that the compressive surface strain induced by doped nitrogen plays a key role in the fine-tuning of catalytic activity.

Toward new fuel cell support materials: a theoretical and experimental study of nitrogen-doped graphene.

Nano-scale Pt particles are often reported to be more electrochemically active and stable in a fuel cell if properly displaced on support materials; however, the factors that affect their activity and stability are not well understood. We applied first-principles calculations and experimental measurements to well-defined model systems of N-doped graphene supports (N-GNS) to reveal the fundamental mechanisms that control the catalytic properties and structural integrity of nano-scale Pt particles. DFT calculations predict thermodynamic and electrochemical interactions between N-GNS and Pt nanoparticles in the methanol oxidation reaction (MOR) and oxygen reduction reaction (ORR). Moreover, the dissolution potentials of the Pt nanoparticles supported on GNS and N-GNS catalysts are calculated under acidic conditions. Our results provide insight into the design of new support materials for enhanced catalytic efficiency and long-term stability.

First principles computational study on the electrochemical stability of Pt-Co nanocatalysts.

Using density functional theory (DFT) calculations, we identify the thermodynamically stable configurations of Pt-Co alloy nanoparticles of varying Co compositions and particle sizes. Our results indicate that the most thermodynamically stable structure is a shell-by-shell configuration where the Pt atom only shell and the Co only shell alternately stack and the outermost shell consists of a Pt skin layer. DFT calculations show that the structure has substantially higher dissolution potential of the outermost Pt shell compared with pure Pt nanoparticles of approximately the same size. Furthermore, our DFT calculations also propose that the shell-by-shell structure shows much better oxygen reduction reaction (ORR) activity than conventional bulk or nanoparticles of pure Pt. These novel catalyst properties can be changed when the surfaces are adsorbed with oxygen atoms via selective segregation followed by the electrochemical dissolution of the alloyed Co atoms. However, these phenomena are thermodynamically not plausible if the chemical potentials of oxygen are controlled below a certain level. Therefore, we propose that the shell-by-shell structures are promising candidates for highly functional catalysts in fuel cell applications.