Stable reconstruction of the (110) surface and its role in pseudocapacitance of rutile-like RuO2

Accessing complex reconstructed material structures with hybrid global optimization accelerated via on-the-fly machine learning

The complex reconstructed structure of materials can be revealed by global optimization. This paper describes a hybrid evolutionary algorithm (HEA) that combines differential evolution and genetic algorithms with a multi-tribe framework. An on-the-fly machine learning calculator is adopted to expedite the identification of low-lying structures. With a superior performance to other well-established methods, we further demonstrate its efficacy by optimizing the complex oxidized surface of Pt/Pd/Cu with different facets under (4 × 4) periodicity. The obtained structures are consistent with experimental results and are energetically lower than the previously presented model.

Structure of an Ultrathin Oxide on Pt3 Sn(111) Solved by Machine Learning Enhanced Global Optimization

Determination of the atomic structure of solid surfaces typically depends on comparison of measured properties with simulations based on hypothesized structural models. For simple structures, the models may be guessed, but for more complex structures there is a need for reliable theory-based search algorithms. So far, such methods have been limited by the combinatorial complexity and computational expense of sufficiently accurate energy estimation for surfaces. However, the introduction of machine learning methods has the potential to change this radically. Here, we demonstrate how an evolutionary algorithm, utilizing machine learning for accelerated energy estimation and diverse population generation, can be used to solve an unknown surface structure-the (4×4) surface oxide on Pt₃ Sn(111)-based on limited experimental input. The algorithm is efficient and robust, and should be broadly applicable in surface studies, where it can replace manual, intuition based model generation.

Structure of an Ultrathin Oxide on Pt3Sn(111) Solved by Machine Learning Enhanced Global Optimization

Determination of the atomic structure of solid surfaces typically depends on comparison of measured properties with simulations based on hypothesized structural models. For simple structures, the models may be guessed, but for more complex structures there is a need for reliable theory-based search algorithms. So far, such methods have been limited by the combinatorial complexity and computational expense of sufficiently accurate energy estimation for surfaces. However, the introduction of machine learning methods has the potential to change this radically. Here, we demonstrate how an evolutionary algorithm, utilizing machine learning for accelerated energy estimation and diverse population generation, can be used to solve an unknown surface structure-the (4×4) surface oxide on Pt3Sn(111)-based on limited experimental input. The algorithm is efficient and robust, and should be broadly applicable in surface studies, where it can replace manual, intuition based model generation.

A Comprehensive Review on Supercapacitor Applications and Developments

The storage of enormous energies is a significant challenge for electrical generation. Researchers have studied energy storage methods and increased efficiency for many years. In recent years, researchers have been exploring new materials and techniques to store more significant amounts of energy more efficiently. In particular, renewable energy sources and electric vehicle technologies are triggering these scientific studies. Scientists and manufacturers recently proposed the supercapacitor (SC) as an alternating or hybrid storage device. This paper aims to provide a comprehensive review of SC applications and their developments. Accordingly, a detailed literature review was first carried out. The historical results of SCs are revealed in this paper. The structure, working principle, and materials of SC are given in detail to be analysed more effectively. The advantages and disadvantages, market profile, and new technologies with manufacturer corporations are investigated to produce a techno-economic analysis of SCs. The electric vehicle, power systems, hybrid energy storage systems with integration of renewable energy sources, and other applications of SCs are investigated in this paper. Additionally, SC modelling design principles with charge and discharge tests are explored. Other components and their price to produce a compact module for high power density are also investigated.

Nanoparticle O-H Bond Dissociation Free Energies from Equilibrium Measurements of Cerium Oxide Colloids

A novel equilibrium strategy for measuring the hydrogen atom affinity of colloidal metal oxide nanoparticles is presented. Reactions between oleate-capped cerium oxide nanoparticle colloids (nanoceria) and organic proton-coupled electron transfer (PCET) reagents are used as a model system. Nanoceria redox changes, or hydrogen loadings, and overall reaction stoichiometries were followed by both ¹H NMR and X-ray absorption near-edge spectroscopies. These investigations revealed that, in many cases, reactions between nanoceria and PCET reagents reach equilibrium states with good mass balance. Each equilibrium state is a direct measure of the bond strength, or bond dissociation free energy (BDFE), between nanoceria and hydrogen. Further studies, including those with larger nanoceria, indicated that the relevant bond is a surface O-H. Thus, we have measured surface O-H BDFEs for nanoceria-the first experimental BDFEs for any nanoscale metal oxide. Remarkably, the measured CeO-H BDFEs span 13 kcal mol^-1 (0.56 eV) with changes in the average redox state of the nanoceria colloid. Possible chemical models for this strong dependence are discussed. We propose that the tunability of ceria BDFEs may be important in explaining its effectiveness in catalysis. More generally, metal oxide BDFEs have been used as predictors of catalyst efficacy that, traditionally, have only been accessible by computational methods. These results provide important experimental benchmarks for metal oxide BDFEs and demonstrate that the concepts of molecular bond strength thermochemistry can be applied to nanoscale materials.

Metallic porous nitride single crystals at two-centimeter scale delivering enhanced pseudocapacitance

Pseudocapacitors that originate from chemisorption contain redox active sites mainly composed of transition metal ions with unsaturated coordination in lattice on the electrode surface. The capacitance is generally dictated by the synergy of the porous microstructure, electronic conduction and active sites in the porous electrode. Here we grow metallic porous nitride single crystals at 2-cm scale to enhance pseudocapacitance through the combination of large surface area with porous microstructure, high conductivity with metallic states and ordered active sites with unsaturated coordination at twisted surfaces. We show the enhanced gravimetric and areal pseudocapacitance and excellent cycling stability both in acidic and alkaline electrolyte with porous MoN, Ta5N6 and TiN single crystals. The long-range ordering of active metal-nitrogen sites account for the fast redox reactions in chemisorption while the high conductivity together with porous microstructure facilitate the charge transfer and species diffusion in electrodes.

Novel Unexpected Reconstructions of (100) and (111) Surfaces of NaCl: Theoretical Prediction

We have predicted stable reconstructions of the (100) and (111) surfaces of NaCl using the global optimization algorithm USPEX. Several new reconstructions, together with the previously reported ones, are found. For the cleaved bare (100) surface, pure Na and pure Cl are the only stable surface phases. Our study of the (111) surface shows that a newly predicted Na₃Cl-(1 × 1) reconstruction is thermodynamically stable in a wide range of chlorine chemical potentials. It has a sawtooth-like profile where each facet reproduces the (100) surface of rock-salt NaCl, hinting on the preferred growth of the (100) surface. We used Bader charge analysis to explain the preferable formation of this sawtooth-like Na₃Cl-(1 × 1) reconstruction of the (111) surface of NaCl. We find that at a very high chemical potential of Na, the polar (and normally absent) (111) surface becomes part of the equilibrium crystal morphology. At both very high and very low chemical potentials of Cl, we predict a large decrease of surface energy and fracture toughness (the Rehbinder effect).

A first-principles roadmap and limits to design efficient supercapacitor electrode materials

A road map to guide researchers to predict the desired properties is presented based on the DFT calculations to allow researchers decide which property of the material they wish to predict or develop and how to choose the proper DFT route to do so.

Two-Dimensional VO2 Mesoporous Microarrays for High-Performance Supercapacitor

Two-dimensional (2D) mesoporous VO₂ microarrays have been prepared using an organic-inorganic liquid interface. The units of microarrays consist of needle-like VO₂ particles with a mesoporous structure, in which crack-like pores with a pore size of about 2 nm and depth of 20-100 nm are distributed on the particle surface. The liquid interface acts as a template for the formation of the 2D microarrays, as identified from the kinetic observation. Due to the mesoporous structure of the units and high conductivity of the microarray, such 2D VO₂ microarrays exhibit a high specific capacitance of 265 F/g at 1 A/g and excellent rate capability (182 F/g at 10 A/g) and cycling stability, suggesting the effect of unique microstructure for improving the electrochemical performance.