Computational Insights on Structural Sensitivity of Cobalt in NO Electroreduction to Ammonia and Hydroxylamine.

It has been reported that it was selective to produce ammonia on metallic cobalt in the electrocatalytic nitric oxide reduction reaction (eNORR), where hexagonal close-packed (hcp) cobalt outperforms face-centered cubic (fcc) cobalt. However, hydroxylamine is more selectively produced on a cobalt single-atom catalyst (Co-SAC). Herein, we uncover the structural sensitivity over hcp-Co, fcc-Co, and Co-SAC in eNORR by employing a recently developed constant potential simulation method and microkinetic modeling. It was found that the superior activity for ammonia production on hcp-Co can be attributed to its facile electron and proton transfer and a stronger lateral suppression effect from NO* over fcc-Co. The exceptional hydroxylamine selectivity on Co-SAC is due to the modified electronic structure, namely, a positively charged active center. It was found that it is more favorable to produce NOH* over hcp-Co and fcc-Co, while HNO* is more preferable on Co-SAC, which are firmly correlated with the vertical and strong NO adsorption on the former and the moderate adsorption on the latter. In other words, a key factor for selectivity control is the first step of NO* protonation. Therefore, the local structure and electronic structure of the catalysts can be critical in regulating the activity and selectivity in eNORR.

Theoretical study of surface segregation and ordering in Ni-based bimetallic surface alloys.

Ni-based bimetallic materials are promising for a series of important heterogeneous catalytic reactions because of their low cost and potential high activity. In order to understand their catalytic performances in catalytic processes, it is important to know the structural properties of these bimetallic surfaces, including, in particular, how the guest metal is distributed in the nickle host at finite temperature. By using the cluster expansion model built on density-functional theory calculations, combined with Monte Carlo simulation, we study the segregation and ordering behaviors in several frequently studied Ni-based bimetallic catalysts NiX (X = Fe, Co, and Cu). We found that Ni tends to segregate to the top most layer of the surface in NiFe and NiCo, while Cu tends to segregate to the topmost layer of NiCu surfaces. NiCo and NiCu lose short-range order quickly as the temperature increases. Under low temperature, NiFe forms an ordered Ni3Fe structure, which, however, disappears above 550 K because of the order-disorder transition. These findings can provide important information for the understanding of the stability and activity of Ni-based bimetallic catalysts at high temperatures.

Towards rational design in electrochemical denitrification by analyzing pH dependence.

A small fraction of NOx (<1%) always exists in CO2 feedstock (e.g. exhausted gas), which can significantly reduce the efficiency of CO2 electroreduction by ∼30%. Hence, electrochemical denitrification is the precondition of CO2 electroreduction. The pH effect is a key factor, and can be used to tune the selectivity between N2 and N2O production in electrochemical denitrification. However, there has been much controversy for many years about the origin of pH dependence in electrocatalysis. To this end, we present a new scheme to accurately model the pH dependence of the electrochemical mechanism. An extremely small pH variation from pH 12.7 to pH 14 can be accurately reproduced for N2O production. More importantly, the obviously different pH dependence of N2 production, compared to N2O, can be attributed to a cascade path. In other words, the N2 was produced from the secondary conversion of the as-produced N2O molecule (the major product), instead of the original reactant NO. This is further supported by more than 35 experiments over varying catalysts (Fe, Ni, Pd, Cu, Co, Pt and Ag), partial pressures (20%, 50% and 100%) and potentials (from -0.2 to 0.2 V vs. reversible hydrogen electrode). All in all, the insights herein overturn long-lasting views in the field of NO electroreduction and suggest that rational design should steer away from catalyst engineering toward reactor optimization.

Improving crop health by synthetic microbial communities: Progress and prospects.

Crop health directly affects yields and food security. At present, agrochemicals such as fertilizers and pesticides are mainly used in agricultural production to promote crop health. However, long-term excessive utilization of agrochemicals will damage the ecological environment of farmlands and increase the safety risk of agricultural products. It is urgent to explore efficient and environment-friendly agricultural products. Rhizosphere microbiome are considered as the second genome of plants, which are closely related to crop health. Understanding the key functional microbes, microbe-microbe interactions, and plant-microbe interactions are fundamental for exploring the potential of beneficial microbes in promoting crop health. However, due to the heterogeneity and complexity of the natural environment, stimulating the function of indigenous microorganisms remains uncertain. Synthetic microbial community (SynCom) is an artificial combination of two or more different strain isolates of microorganisms, with different taxonomic, genetic, or functional characteristic. Because of the advantages of maintaining species diversity and community stability, SynCom has been widely applied in the fields of human health, environmental governance and industrial production, and may also have great potential in promoting crop health. We summarized the concept and research status of SynCom, expounded the principles and methods of constructing SynCom, and analyzed the research on the promotion of crop health by exploring the mechanism of plant-microbe interactions, promoting plant growth and development, and improving stress resistance. Finally, we envisaged the future prospects to guide the using SynCom to improve crop health.

Adaptive Electric Fields Embedded Electrochemical Barrier Calculations.

Electrochemical interfaces are grand canonical ensembles of varying electrons. Simulating them by standard first-principles methods is a challenging task, since the number of electrons (or charge) is fixed in the calculation. Under the constant charge framework, we developed a constant potential simulation method realized by adding an adaptive electric field to a charge neutral system. Electric field is the controlling variable. In addition, we defined an internal reversible hydrogen electrode potential (ϕIRHE), which can ensure the model independence of our method. To validate our method, the reaction energies of some electrochemical reactions are calculated, the results are comparable with the computational hydrogen electrode model and experiments. At last, the evolution of transition state structures and charge transfer coefficients of some electrochemical reactions on Ag(111) surface were discussed by our method.

Machine Learning Force Field Aided Cluster Expansion Approach to Configurationally Disordered Materials: Critical Assessment of Training Set Selection and Size Convergence.

Cluster expansion (CE) is a powerful theoretical tool to study the configuration-dependent properties of substitutionally disordered systems. Typically, a CE model is built by fitting a few tens or hundreds of target quantities calculated by first-principles approaches. To validate the reliability of the model, a convergence test of the cross-validation (CV) score to the training set size is commonly conducted to verify the sufficiency of the training data. However, such a test only confirms the convergence of the predictive capability of the CE model within the training set, and it is unknown whether the convergence of the CV score would lead to robust thermodynamic simulation results such as order-disorder phase transition temperature Tc. In this work, using carbon defective MoC1-x as a model system and aided by the machine-learning force field technique, a training data pool with about 13000 configurations has been efficiently obtained and used to generate different training sets of the same size randomly. By conducting parallel Monte Carlo simulations with the CE models trained with different randomly selected training sets, the uncertainty in calculated Tc can be evaluated at different training set sizes. It is found that the training set size that is sufficient for the CV score to converge still leads to a significant uncertainty in the predicted Tc and that the latter can be considerably reduced by enlarging the training set to that of a few thousand configurations. This work highlights the importance of using a large training set to build the optimal CE model that can achieve robust statistical modeling results and the facility provided by the machine-learning force field approach to efficiently produce adequate training data.

NixCoy Nanocatalyst Supported by ZrO2 Hollow Sphere for Dry Reforming of Methane: Synergetic Catalysis by Ni and Co in Alloy.

NixCoy/H-ZrO2 catalysts composed of highly dispersed NixCoy nanoparticles supported by mesoporous ZrO2 hollow sphere are synthesized by templating and impregnation processes. According to thermogravimetric analysis, X-ray photoelectron spectroscopy, and dry reforming results, a synergetic reaction mechanism is proposed to explain the better performance of alloy catalysts compared to Ni/H-ZrO2 or Co/H-ZrO2. In dry reforming of methane (DRM) reaction, Ni and Co act as catalysts for CH4 cracking and CO2 reduction, respectively, and the induced carbon deposits on Ni can be oxidized by the active oxygen left on Co, which regenerate the metal surface for the following reaction. Among all the alloy catalysts, the Ni0.8Co0.2/H-ZrO2 catalyst presents the highest activity and stability because the strong metal-support interaction prevents the sintering of nanocatalysts at high temperature and the hollow structure enhances the mass transportation of reactants and products. More importantly, Ni and Co can synergistically balance the speed of CH4 cracking and CO2 reduction, which effectively avoid coke accumulation/catalyst oxidation and ensure fast and stable conversion for DRM reaction.

Ultralow thermal conductivity in highly anion-defective aluminates.

Ultralow thermal conductivity (1.1 W/m.K, 1000 degrees C) in anion-deficient Ba2RAlO5 (R=Dy, Er, Yb) compounds was reported. The low thermal conductivity was then analyzed by kinetic theory. The highly defective structure of Ba2RAlO5 results in weak atomic bond strength and low sound speeds, and phonon scattering by large concentration of oxygen vacancies reduces the phonon mean free path to the order of interatomic distance. Ba2DyAlO5 exhibits the shortest phonon mean free path and lowest thermal conductivity among the three compositions investigated, which can be attributed to additional phonon scattering by DyO6 octahedron tilting as a result of a low tolerance factor. The Ba2RAlO5 (R=Dy, Er, Yb) compounds have shown great potential in high-temperature thermal insulation applications, particularly as a thermal barrier coating material.