Assessing Exchange-Correlation Functionals for Heterogeneous Catalysis of Nitrogen Species.

Increasing interest in the sustainable synthesis of ammonia, nitrates, and urea has led to an increase in studies of catalytic conversion between nitrogen-containing compounds using heterogeneous catalysts. Density functional theory (DFT) is commonly employed to obtain molecular-scale insight into these reactions, but there have been relatively few assessments of the exchange-correlation functionals that are best suited for heterogeneous catalysis of nitrogen compounds. Here, we assess a range of functionals ranging from the generalized gradient approximation (GGA) to the random phase approximation (RPA) for the formation energies of gas-phase nitrogen species, the lattice constants of representative solids from several common classes of catalysts (metals, oxides, and metal-organic frameworks (MOFs)), and the adsorption energies of a range of nitrogen-containing intermediates on these materials. The results reveal that the choice of exchange-correlation functional and van der Waals correction can have a surprisingly large effect and that increasing the level of theory does not always improve the accuracy for nitrogen-containing compounds. This suggests that the selection of functionals should be carefully evaluated on the basis of the specific reaction and material being studied.

From Data to Discovery: Recent Trends of Machine Learning in Metal-Organic Frameworks.

Renowned for their high porosity and structural diversity, metal-organic frameworks (MOFs) are a promising class of materials for a wide range of applications. In recent decades, with the development of large-scale databases, the MOF community has witnessed innovations brought by data-driven machine learning methods, which have enabled a deeper understanding of the chemical nature of MOFs and led to the development of novel structures. Notably, machine learning is continuously and rapidly advancing as new methodologies, architectures, and data representations are actively being investigated, and their implementation in materials discovery is vigorously pursued. Under these circumstances, it is important to closely monitor recent research trends and identify the technologies that are being introduced. In this Perspective, we focus on emerging trends of machine learning within the field of MOFs, the challenges they face, and the future directions of their development.

Boosting Alkaline Hydrogen Oxidation Activity of Ru Single-Atom Through Promoting Hydroxyl Adsorption on Ru/WC1- x Interfaces.

The sluggish kinetics of the hydrogen oxidation reaction (HOR) in alkaline conditions continue to pose a significant challenge for the practical implementation of anion-exchange membrane fuel cells. Developing single-atom catalysts can accelerate the pace of new HOR catalyst discovery for highly cost-effective and active HOR performance. However, single-atom catalysts (SACs) for the alkaline HOR have rarely been reported, and fundamental studies on the rational design of SACs are still required. Herein, the design of Ru SAC supported on the tungsten carbide (Ru SA/WC1- x ) via in situ high-temperature annealing strategy is reported. The resulting Ru SA/WC1- x catalyst exhibits remarkably enhanced HOR performance in alkaline media, a level of activity that can not be achieved with carbon-supported Ru SAC. Electrochemical results and density functional theory demonstrate that promoting the hydroxyl adsorption on Ru SA/WC1- x interfaces, which is derived from the low potential of zero charge of WC1- x support, has a significant effect on enhancing the HOR performance of SACs. This enhancement leads to 5.8 and 60.1 times higher Ru mass activity of Ru SA/WC1- x than Ru nanoparticles on carbon and Ru single-atom on N-doped carbon, respectively. This work provides new insights into the design of highly active SACs for alkaline HOR.

Triphenylene-Based 2D cMOFs: Unraveling the H2S Sensing Mechanism and Applications for a Real-Time Wireless Chemiresistive Sensor.

Two-dimensional conductive metal-organic frameworks (2D cMOFs) stand at the forefront of chemiresistive sensing innovations due to their high surface areas, distinctive morphologies, and substantial electronic conductivity. Particularly, 2D cMOFs crafted using 2,3,6,7,10,11-hexahydroxytriphenylene (HHTP) and 2,3,6,7,10,11-hexaiminotriphenylene (HITP) organic ligands have garnered a large amount of attention due to their designable active sites and proper conductive characteristics. Nevertheless, a deeper exploration into their sensing mechanisms is imperative for a comprehensive understanding of the intrinsic chemistry, which is crucial for the intricate design of specialized 2D cMOF chemiresistive sensors. In this study, we fabricate six M-HXTP (M = Co, Ni, and Cu; X = H and I) chemiresistive sensors, focusing on the application of hydrogen sulfide (H2S) detection. Among these, the 2D cMOFs incorporating Cu metal manifested a remarkably enhanced response to H2S. A combination of experimental and computational studies unveils the mechanisms of sulfur oxidation and Cu reduction, wherein distortion of the reduced MX4 cluster markedly amplifies the sensing response. Lastly, a real-time and portable wireless H2S sensing module has been demonstrated by using the Cu-HHTP composite material, highlighting the substantial practical significance and potential applicability.

Computational Prediction of Stacking Mode in Conductive Two-Dimensional Metal-Organic Frameworks: An Exploration of Chemical and Electrical Property Changes.

Conductive two-dimensional metal-organic frameworks (2D MOFs) have attracted interest as they induce strong charge delocalization and improve charge carrier mobility and concentration. However, characterizing their stacking mode depends on expensive and time-consuming experimental measurements. Here, we construct a potential energy surface (PES) map database for 36 2D MOFs using density functional theory (DFT) for the experimentally synthesized and non-synthesized 2D MOFs to predict their stacking mode. The DFT PES results successfully predict the experimentally synthesized stacking mode with an accuracy of 92.9% and explain the coexistence mechanism of dual stacking modes in a single compound. Furthermore, we analyze the chemical (i.e., host-guest interaction) and electrical (i.e., electronic structure) property changes affected by stacking mode. The DFT results show that the host-guest interaction can be enhanced by the transition from AA to AB stacking, taking H2S gas as a case study. The electronic band structure calculation confirms that as AB stacking displacement increases, the in-plane charge transport pathway is reduced while the out-of-plane charge transport pathway is maintained or even increased. These results indicate that there is a trade-off between chemical and electrical properties in accordance with the stacking mode.

Theoretical study of induced selective N2 binding under an electric field in MOF-74: application for N2/CH4 separations.

In this theoretical study, selective binding of dinitrogen to the coordinatively unsaturated metal site in M-MOF-74 (M = Mg, Mn, Fe, Co, Ni, Cu, Zn) under an external electric field is investigated. Simulation results suggest that an external electric field enhances the π* back-bonding between the transition metal and dinitrogen molecule while weakening the σ bond between the metal and other small gas molecules such as CO2 and CH4. In particular, Co-MOF-74 and Fe-MOF-74 show the highest dinitrogen binding energy in the presence of an electric field, twice as high as that of methane. Our work demonstrates that the asymmetric effect of the electric field on different gas molecules can serve as another dimension of design that can be exploited in small gas molecule separation in metal-organic frameworks.