Due to its high overpotential and low efficiency, the conversion of water to O(2) using solar energy remains a bottleneck for photocatalytic water splitting. Here the microscopic mechanisms of the oxygen evolution reaction (OER) on differently structured anatase surfaces in aqueous surroundings, namely, (101), (001), and (102), are determined and compared systematically by combining first-principles density functional theory calculations and a parallel periodic continuum solvation model. We show that OER involves the sequential removal of protons from surface oxidative species, forming surface peroxo and superoxo intermediates. The initiating step, the first proton removal, dictates the high overpotential. Only at an overpotential of 0.7 V (1.93 V vs SHE) does this rate-controlling step become surmountable at room temperature: the free energy change of the step is 0.69, 0.63, and 0.61 eV for (101), (102), and (001) surfaces, respectively. We therefore conclude that (i) OER is not sensitive to the local surface structure of anatase and (ii) visible light (<∼590 nm) is, in principle, capable of driving the photocatatlytic OER on anatase kinetically. By co-doping high-valent elements into the anatase subsurface, we demonstrate that the high overpotential of the OER can be significantly reduced, with extra occupied levels above the valence band.

Ascorbic acid (AA) levels are closely correlated with physiological and pathological events in brain diseases, but the mechanism remains unclear, mainly due to the difficulty of accurately analyzing AA levels in live brain. In this study, by engineering tunable defects and oxygen-containing species in carbon nanotubes, a novel aligned carbon nanotube fiber was developed as an accurate microsensor for the ratiometric detection of AA levels in live rat brains with Alzheimer's disease (AD). AA oxidation is greatly facilitated on the fiber surface at a low potential, leading to high sensitivity as well as high selectivity against potential sources of interference in the brain. Additionally, an unexpected, separate peak from the fiber surface remains constant as the AA concentration increases, enabling real-time and ratiometric detection with high accuracy. The results demonstrated that the AA levels were estimated to be 259 ± 6 μM in cortex, 264 ± 20 μM in striatum, and 261 ± 21 μM in hippocampus, respectively, in normal condition. However, the overall AA level was decreased to 210 ± 30 μM in cortex, 182 ± 5 μM in striatum, and 136 ± 20 μM in hippocampus in the rat brain model of AD. To the best of our knowledge, this work is the first to accurately detect AA concentrations in the brains of live animal model of AD.

No abstract

Over‐oxidation in alkaline urea oxidation to dominate NO2− formation over N2 has been discovered by Xuejing Yang, Yefei Li, Ming Gong, and co‐workers in their Research Article on page 26656. The identified nitrogen conversion network and reaction mechanism has important implications for sustainable chemistry and inspires new rationales for novel catalyst designs for the electrochemical urea oxidation into N2 products.

LASP (large-scale atomistic simulation with neural network potential) software developed by our group since 2018 is a powerful platform (www.lasphub.com) for performing atomic simulation of complex materials. The software integrates the neural network (NN) potential technique with the global potential energy surface exploration method, and thus can be utilized widely for structure prediction and reaction mechanism exploration. Here we introduce our recent update on the LASP program version 3.0, focusing on the new functionalities including the advanced neural network training based on the multi-network framework, the newly-introduced S7 and S8 power type structure descriptor (PTSD). These new functionalities are designed to further improve the accuracy of potentials and accelerate the neural network training for multiple-element systems. Taking Cu-C-H-O neural network potential and a heterogeneous catalytic model as the example, we show that these new functionalities can accelerate the training of multi-element neural network potential by using the existing single-network potential as the input. The obtained double-network potential CuCHO is robust in simulation and the introduction of S7 and S8 PTSDs can reduce the root-mean-square errors of energy by a factor of two.