Explore the vast range of titles available.

The one-step electrochemical synthesis of H 2 O 2 is an on-site method that reduces dependence on the energy-intensive anthraquinone process. Oxidized carbon materials have proven to be promising catalysts due to their low cost and facile synthetic procedures. However, the nature of the active sites is still controversial, and direct experimental evidence is presently lacking. Here, we activate a carbon material with dangling edge sites and then decorate them with targeted functional groups. We show that quinone-enriched samples exhibit high selectivity and activity with a H 2 O 2 yield ratio of up to 97.8 % at 0.75 V vs. RHE. Using density functional theory calculations, we identify the activity trends of different possible quinone functional groups in the edge and basal plane of the carbon nanostructure and determine the most active motif. Our findings provide guidelines for designing carbon-based catalysts, which have simultaneous high selectivity and activity for H 2 O 2 synthesis. The identity of catalytic sites for H 2 O 2 generation in carbon-based materials remains controversial with limited experimental evidence to date. Here, the authors decorate various target functional groups on carbon materials and quinone-enriched samples exhibit the highest activity and selectivity.

Hydrogen adsorption/desorption behavior plays a key role in hydrogen evolution reaction (HER) catalysis. The HER reaction rate is a trade-off between hydrogen adsorption and desorption on the catalyst surface. Herein, we report the rational balancing of hydrogen adsorption/desorption by orbital modulation using introduced environmental electronegative carbon/nitrogen (C/N) atoms. Theoretical calculations reveal that the empty d orbitals of iridium (Ir) sites can be reduced by interactions between the environmental electronegative C/N and Ir atoms. This balances the hydrogen adsorption/desorption around the Ir sites, accelerating the related HER process. Remarkably, by anchoring a small amount of Ir nanoparticles (7.16 wt%) in nitrogenated carbon matrixes, the resulting catalyst exhibits significantly enhanced HER performance. This includs the smallest reported overpotential at 10 mA cm −2 (4.5 mV), the highest mass activity at 10 mV (1.12 A mg Ir −1 ) and turnover frequency at 25 mV (4.21 H 2 s −1 ) by far, outperforming Ir nanoparticles and commercial Pt/C. Hydrogen adsorption/desorption behavior plays a key role in hydrogen evolution reaction catalysis. Here, the authors demonstrate the rational balancing of hydrogen adsorption/desorption by orbital modulation for significantly enhanced hydrogen evolution performance.

Ammonia, one of the most important synthetic feedstocks, is mainly produced by the Haber–Bosch process at 400–500 °C and above 100 bar. The process cannot be performed under ambient conditions for kinetic reasons. Here, we demonstrate that ammonia can be synthesized at 45 °C and 1 bar via a mechanochemical method using an iron-based catalyst. With this process the ammonia final concentration reached 82.5 vol%, which is higher than state-of-the-art ammonia synthesis under high temperature and pressure (25 vol%, 450 °C, 200 bar). The mechanochemically induced high defect density and violent impact on the iron catalyst were responsible for the mild synthesis conditions. The ammonia was synthesized under ambient conditions via a mechanochemical method, reaching a final concentration of 82.5 vol%.

Identification of active sites is one of the main obstacles to rational design of catalysts for diverse applications. Fundamental insight into the identification of the structure of active sites and structural contributions for catalytic performance are still lacking. Recently, X-ray absorption spectroscopy (XAS) and density functional theory (DFT) provide important tools to disclose the electronic, geometric and catalytic natures of active sites. Herein, we demonstrate the structural identification of Zn-N 2 active sites with both experimental/theoretical X-ray absorption near edge structure (XANES) and extended X-ray absorption fine structure (EXAFS) spectra. Further DFT calculations reveal that the oxygen species activation on Zn-N 2 active sites is significantly enhanced, which can accelerate the reduction of oxygen with high selectivity, according well with the experimental results. This work highlights the identification and investigation of Zn-N 2 active sites, providing a regular principle to obtain deep insight into the nature of catalysts for various catalytic applications. Identification of active sites is one of the main obstacles to rational design of catalysts for scientific and industrial applications. Here, the authors demonstrate the synthesis and structural identification of Zn based active sites, as well as the related structural activation for oxygen species.

While deep learning (DL) models exhibit superior simulation accuracy over traditional distributed hydrological models (DHMs), their main limitations lie in opacity and the absence of underlying physical mechanisms. The pursuit of synergies between DL and DHMs is an engaging research domain, yet a definitive roadmap remains elusive. In this study, a novel framework that seamlessly integrates a process‐based hydrological model encoded as a neural network (NN), an additional NN for mapping spatially distributed and physically meaningful parameters from watershed attributes, and NN‐based replacement models representing inadequately understood processes is developed. Multi‐source observations are used as training data, and the framework is fully differentiable, enabling fast parameter tuning by backpropagation. A hybrid DL model of the Amazon Basin (∼6 × 106 km2) was established based on the framework, and HydroPy, a global‐scale DHM, was encoded as its physical backbone. Trained simultaneously with streamflow observations and Gravity Recovery and Climate Experiment satellite data, the hybrid model yielded median Nash‐Sutcliffe efficiencies of 0.83 and 0.77 for dynamic and distributed simulations of streamflow and total water storage, respectively, 41% and 35% higher than those of the original HydroPy model. Replacing the original Penman‒Monteith formulation in HydroPy with a replacement NN produces more plausible potential evapotranspiration (PET) estimates, and unravels the spatial pattern of PET in this giant basin. The NN used for parameterization was interpreted to identify the factors controlling the spatial variability in key parameters. Overall, this study lays out a feasible technical roadmap for distributed hydrological modeling in the big data era. Key Points A fully differentiable framework that seamlessly integrates physics and deep learning was developed for distributed hydrological modeling The framework flexibly fuses multi‐source observations and improves the efficiency and accuracy of large‐scale hydrological modeling The hybrid model for the Amazon Basin exhibits excellent fidelity and physical plausibility and provides insights into the ET process

Blood–brain barrier (BBB) is a natural protective membrane that prevents central nervous system (CNS) from toxins and pathogens in blood. However, the presence of BBB complicates the pharmacotherapy for CNS disorders as the most chemical drugs and biopharmaceuticals have been impeded to enter the brain. Insufficient drug delivery into the brain leads to low therapeutic efficacy as well as aggravated side effects due to the accumulation in other organs and tissues. Recent breakthrough in materials science and nanotechnology provides a library of advanced materials with customized structure and property serving as a powerful toolkit for targeted drug delivery. In-depth research in the field of anatomical and pathological study on brain and BBB further facilitates the development of brain-targeted strategies for enhanced BBB crossing. In this review, the physiological structure and different cells contributing to this barrier are summarized. Various emerging strategies for permeability regulation and BBB crossing including passive transcytosis, intranasal administration, ligands conjugation, membrane coating, stimuli-triggered BBB disruption, and other strategies to overcome BBB obstacle are highlighted. Versatile drug delivery systems ranging from organic, inorganic, and biologics-derived materials with their synthesis procedures and unique physio-chemical properties are summarized and analyzed. This review aims to provide an up-to-date and comprehensive guideline for researchers in diverse fields, offering perspectives on further development of brain-targeted drug delivery system.

Due to the different degrees of controls exerted by biological and geochemical processes, climate changes are suggested to uncouple biogeochemical C, N and P cycles, influencing biomass accumulation, decomposition and storage in terrestrial ecosystems. However, the possible extent of such disruption in grassland ecosystems remains unclear, especially in China’s steppes which have undergone rapid climate changes with increasing drought and warming predicted moving forward in these dryland ecosystems. Here, we assess how soil C-N-P stoichiometry is affected by climatic change along a 3500-km temperate climate transect in Inner Mongolia, China. Our results reveal that the soil from more arid and warmer sites are associated with lower soil organic C, total N and P. The ratios of both soil C:P and N:P decrease, but soil C:N increases with increasing aridity and temperature, indicating the predicted decreases in precipitation and warming for most of the temperate grassland region could lead to a soil C-N-P decoupling that may reduce plant growth and production in arid ecosystems. Soil pH, mainly reflecting long-term climate change in our sites, also contributes to the changing soil C-N-P stoichiometry, indicating the collective influences of climate and soil type on the shape of soil C-N-P balance.

A fault-tolerant collaborative control strategy for four-wheel-drive electric vehicles is proposed to address hidden safety issues caused by one or more in-wheel motor faults; the basic design scheme is that the control system is divided into two layers of motion tracking and torque distribution, and three systems, including driving, braking, and front-wheel steering are controlled collaboratively for four-wheel torque distribution. In the layer of motion tracking, a vehicle model with two-degree-of-freedom is employed to predict the control reference values of the longitudinal force and additional yaw moment required; four types of sensors, such as wheel speed, acceleration, gyroscope, and steering wheel angle, are used to calculate the actual values. At the torque distribution layer, SSOD and MSCD distribution schemes are designed to cope with two operating conditions, namely sufficient and insufficient output capacity after local hub motor failure, respectively, focusing on the objective function, constraints, and control variables of the MSCD control strategy. Finally, two operating environments, a straight-line track, and a DLC track, are set up to verify the effectiveness of the proposed control method. The results indicate that, compared with traditional methods, the average errors of the center of mass sideslip angle and yaw rate are reduced by at least 12.9% and 5.88%, respectively, in the straight-line track environment. In the DLC track environment, the average errors of the center of mass sideslip angle and yaw rate are reduced by at least 6% and 4.5%, respectively. The proposed fault-tolerant controller ensures that the four-wheel-drive electric vehicle meets the requirements of handling stability and safety under one or more hub motor failure conditions.

With the development of science and technology, artificial intelligence and electrical automation control technology is innovating and developing. The application of artificial intelligence technology in the electrical automation control is more and more extensive, which provides the development of automation control technology a solid foundation and strong support. In this paper, artificial intelligence is introduced with their research directions, including expert system, machine learning, pattern recognition, artificial neural network and deep learning etc. At last, the applications of artificial intelligence are analyzed from several perspectives. Hopefully, this paper can provide some instruction to AI researchers.

Developing robust nonprecious-metal electrocatalysts with high activity towards sluggish oxygen-evolution reaction is paramount for large-scale hydrogen production via electrochemical water splitting. Here we report that self-supported laminate composite electrodes composed of alternating nanoporous bimetallic iron-cobalt alloy/oxyhydroxide and cerium oxynitride (FeCo/CeO 2− x N x ) heterolamellas hold great promise as highly efficient electrocatalysts for alkaline oxygen-evolution reaction. By virtue of three-dimensional nanoporous architecture to offer abundant and accessible electroactive CoFeOOH/CeO 2− x N x heterostructure interfaces through facilitating electron transfer and mass transport, nanoporous FeCo/CeO 2− x N x composite electrodes exhibit superior oxygen-evolution electrocatalysis in 1 M KOH, with ultralow Tafel slope of ~33 mV dec −1 . At overpotential of as low as 360 mV, they reach >3900 mA cm −2 and retain exceptional stability at ~1900 mA cm −2 for >1000 h, outperforming commercial RuO 2 and some representative oxygen-evolution-reaction catalysts recently reported. These electrochemical properties make them attractive candidates as oxygen-evolution-reaction electrocatalysts in electrolysis of water for large-scale hydrogen generation. Developing stable catalysts for industrial-scale current densities is challenging. Here, the authors report self-supported laminate electrodes composed of nanoporous bimetallic iron-cobalt alloy/oxyhydroxide and cerium oxynitride hybrid that can catalyze the oxygen evolution reaction at high current densities.