Space charge governs the kinetics of metal exsolution.

Nanostructured composite electrode materials play a major role in the fields of catalysis and electrochemistry. The self-assembly of metallic nanoparticles on oxide supports via metal exsolution relies on the transport of reducible dopants towards the perovskite surface to provide accessible catalytic centres at the solid-gas interface. At surfaces and interfaces, however, strong electrostatic gradients and space charges typically control the properties of oxides. Here we reveal that the nature of the surface-dopant interaction is the main determining factor for the exsolution kinetics of nickel in SrTi0.9Nb0.05Ni0.05O3-δ. The electrostatic interaction of dopants with surface space charge regions forming upon thermal oxidation results in strong surface passivation, which manifests in a retarded exsolution response. We furthermore demonstrate the controllability of the exsolution response via engineering of the perovskite surface chemistry. Our findings indicate that tailoring the electrostatic gradients at the perovskite surface is an essential step to improve exsolution-type materials in catalytic converters.

Technological Pathways to Produce Compressed and Highly Pure Hydrogen from Solar Power.

Hydrogen (H2 ) produced from renewables will have a growing impact on the global energy dynamics towards sustainable and carbon-neutral standards. The share of green H2 is still too low to meet the net-zero target, while the demand for high-quality hydrogen continues to rise. These factors amplify the need for economically viable H2 generation technologies. The present article aims at evaluating the existing technologies for high-quality H2 production based on solar energy. Technologies such as water electrolysis, photoelectrochemical and solar thermochemical water splitting, liquid metal reactors and plasma conversion utilize solar power directly or indirectly (as carbon-neutral electrons) and are reviewed from the perspective of their current development level, technical limitations and future potential.

A Pragmatic Transfer Learning Approach for Oxygen Vacancy Formation Energies in Oxidic Ceramics.

Lower oxygen vacancy formation energy is one of the requirements for air electrode materials in solid oxide cells applications. We introduce a transfer learning approach for oxygen vacancy formation energy prediction for some ABO3 perovskites from a two-species-doped system to four-species-doped system. For that, an artificial neural network is used. Considering a two-species-doping training data set, predictive models are trained for the determination of the oxygen vacancy formation energy. To predict the oxygen vacancy formation energy of four-species-doped perovskites, a formally similar feature space is defined. The transferability of predictive models between physically similar but distinct data sets, i.e., training and testing data sets, is validated by further statistical analysis on residual distributions. The proposed approach is a valuable supporting tool for the search for novel energy materials.