In the wake of blue phosphorene's (BP) computational discovery and experimental realization, it has emerged as a versatile material with interesting optical, electrical, and mechanical properties. In this study, using first principles density functional theory calculations, we have investigated the adsorption and diffusion of Na and K over monolayer BP to assess its suitability as Na-ion and K-ion battery anodes. The optimized adsorption energies were found to be -0.96 eV for Na and -1.54 eV for K, which are sufficiently large to ensure stability and safety during operation. In addition, BP could adsorb Na and K atoms up to a stoichiometric ratio of 1:1 which yields a high storage capacity of 865 mA h/g for both adatom species. Through examination of the electronic structure and projected density of states of BP as a function of Na/K concentration, we predict that the band gap of the system increasingly shrinks, and in the case of maximum K adsorption, the band gap diminishes completely. Additionally, the diffusion of Na and K over BP is observed to be ultrafast, especially for K, and anisotropic with modest energy barriers of 0.11 and 0.093 eV for Na and K, respectively. Building upon these findings, we employed vibrational analysis techniques with transition state theory to incorporate kinetic effects and predicted a diffusivity of 7.2 × 10 cm/s for Na and 8.58 × 10 cm/s for K on BP. Given these advantages, that is, ultrahigh capacity, electrical conductivity, and high Na/K diffusivity, we conclude that BP can be considered as an excellent candidate for anodes in Na- and K-ion batteries.

Defect engineering of blue phosphorene in lithium–sulphur (Li–S) batteries allows for greater specific capacities and faster rate-capabilities.

Li–air batteries can yield exceptionally high predicted energy densities. However, for this technology to become realizable, round-trip efficiency issues and slow kinetics at the cathode require implementation of a catalyst. With design parameters not well understood and limitations on material selection, choosing an ideal catalyst is complex. In Li–air batteries, energy storage is achieved by reactions between Li and O (oxygen reduction reaction for discharge and oxygen evolution reaction for charge). Here, phosphorene is proposed as a solution through simulations of its catalytic behavior toward discharge initiated via either O2 dissociation or Li adsorption. After obtaining intermediate geometries for both nucleation paths leading to either Li2O2 or 2­(Li2O), free-energy diagrams are generated to predict the promoted discharge product of Li2O2. Furthermore, considering a final product of Li2O2, the overpotentials are predicted to be 1.44 V for discharge and 2.63 V for charge. Activation barriers for the catalytic decomposition of Li2O2 (during charge) are found to be 1.01 eV for phosphorene versus 2.06 eV for graphene. This leads to a major difference in reaction rate up to 1017 times in favor of phosphorene. These results, complemented by electronic analysis, establish phosphorene as a promising catalyst for Li–air batteries.

Electrochemical ammonia synthesis forms a key part of sustainable chemical synthesis. Single-atom catalysts have emerged as a promising class of electrocatalysts that could be capable of electrochemically reducing nitrogen into ammonia. The analysis of electrochemical reduction of nitrogen is complicated by multiple mechanistic pathways and the competing hydrogen evolution reaction. The identified pathways using thermodynamic analysis based on density functional theory calculations is strongly dependent on the choice of the exchange correlation functional. In this work, we provide a computational methodological framework using the single-atom systems as an example material class for ammonia synthesis that is robust toward parameter selection. Applying this to Pt1/g-C3N4, Ru1/g-C3N4, and Fe1/g-C3N4, we generate ensembles of limiting potentials, using the ensemble of functionals collected via Bayesian error estimation functionals, to robustly predict catalytic activity. We then extend this to study the scaling between the nitrogen reduction reaction intermediates and use it to identify NNH* as the best descriptor for these relations. In addition, a procedure to investigate selectivity is outlined, and a more robust way to analyze the selectivity–activity trade-off is presented. For this single-atom material class, we find choosing catalysts that lie on the strong binding leg of the activity volcano is worth further exploration. Given the ease of integration of the proposed method with minimal additional computational cost, we believe this should become a routine part of the analysis workflow for multielectron electrochemical reactions.

The implementation of automation and machine learning surrogatization within closed-loop computational workflows is an increasingly popular approach to accelerate materials discovery. However, the scale of the speedup associated with this...