Ye Tao

Amorphous NiPCo/TiO ₂ enables efficient hydrogen production from ammonia borane hydrolysis under mild conditions via synergistic active sites.

To tackle the challenges of weak sensing capacity for multi-scale objects, high missed detection rates for occluded targets, and difficulties for model deployment in detection tasks of intelligent roadside perception systems, the PDT-YOLO algorithm based on YOLOv7-tiny is proposed. Firstly, we introduce the intra-scale feature interaction module (AIFI) and reconstruct the feature pyramid structure to enhance the detection accuracy of multi-scale targets. Secondly, a lightweight convolution module (GSConv) is introduced to construct a multi-scale efficient layer aggregation network module (ETG), enhancing the network feature extraction ability while maintaining weight. Thirdly, multi-attention mechanisms are integrated to optimize the feature expression ability of occluded targets in complex scenarios, Finally, Wise-IoU with a dynamic non-monotonic focusing mechanism improves the accuracy and generalization ability of model sensing. Compared with YOLOv7-tiny, PDT-YOLO on the DAIR-V2X-C dataset improves mAP50 and mAP50:95 by 4.6% and 12.8%, with a parameter count of 6.1 million; on the IVODC dataset by 15.7% and 11.1%. We deployed the PDT-YOLO in an actual traffic environment based on a robot operating system (ROS), with a detection frame rate of 90 FPS, which can meet the needs of roadside object detection and edge deployment in complex traffic scenes.

The doping of nonmetals in multiphase metal catalysts can effectively improve the catalytic performance and reduce the catalyst cost. In this study, we synthesized transition metal phosphide (RuCo@P/CNT) catalysts loaded on carbon nanotubes (CNT) using sodium borohydride (NaBH₄) as the reducing agent. The microstructure and phase composition of RuCo@P/CNT nanoparticles were investigated using X‐ray photoelectron spectroscopy (XPS), X‐ray diffraction (XRD), transmission electron microscopy (TEM), and BET. RuCo@P/CNT nanoparticles show superior catalytic activity and cycle stability in the catalytic ammonia borane hydrolysis process compared to Ru/CNT and RuCo/CNT nanoparticles, retaining 65.74% of their initial catalytic activity after 5 reaction cycles. The test yielded values for turnover frequency and activation energy of 327.33 min^-1 and 36.77 kJ·mol^-1, respectively. Additionally, a kinetic isotope effect value of 2.61 for H₂O/D₂O showed that O‐H bond breaking in proportional acceptor water is the decisive step in the dehydrogenation of ammonia borane, and based on this discovery, a specific mechanism for the catalytic hydrolysis of AB by RuCo@P NPs is postulated.

This work proposes a centralized data-based orderly charging strategy that considers the user’s charging choices. Three charging choices for different types of users are described. Then, a scheduling model of electric vehicles based on the time dimension is established. In this strategy, the optimization model not only considers the demand of the grid side and the user side, but also takes the driving data of electric vehicles as the driver. The grid-side optimization involves minimizing the equivalent load fluctuation, and the user-side is optimized to minimize the charging cost and maximize the charging electric quantity. The scheduling capabilities of the three charging strategies are analyzed based on a series of driving data of electric vehicles. The results show that the peak-valley difference and equivalent load fluctuation of the power grid in the data-based orderly charging strategy reduced by 22.2% and 22.7%, respectively, and the charging cost of users also reduced much more than the other two charging strategies. Additionally, the effect of users’ charging choices on the charging strategy is analyzed, and the results show that the orderly charging strategy that considers users’ charging choices can effectively decrease the scheduling deviation caused by users’ charging choices. It greatly improves the security and economy of the grid.

Transition metal phosphating Pd@NiP nanoparticles supported by reduced graphene oxide were successfully synthesized by a one-step in situ synthesis method. The Pd@NiP/rGO nanoparticles were characterized by powder X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), transmission electron microscopy (TEM), confocal Raman spectroscopy (Raman), Fourier transform infrared spectroscopy (FT-IR), and BET. Compared with Pd/rGO and Pd@Ni/rGO, Pd₁@Ni_12.5P₄₀/rGO exhibited stronger catalytic activity, with TOF value reaching 133.33min^-1 and activation energy (Ea) of 29.31kJmol^-1. This excellent catalytic performance may be related to the good dispersion of nanoparticles and the synergistic electron interactions between transition metal phosphating compounds.

In this study, a method for the investigation of the available capacity of new energy based on the time-series coupling coefficients of the combination of regional wind power, photovoltaic and load was proposed, with the aim of getting an in depth understanding of the regional new energy consumption capacity, and a more accurate amount on the regional potential installation capacity. The typical scenario of new energy high output in the area was determined based on the historical measured power and load data, and the time-series coupling coefficients of the combination of wind power, photovoltaic and load in the area were summarized. The regional new energy consumption capacity and the potential installation capacity for the next three years were quantitatively calculated based on the time-series coupling coefficients. The accuracy and reliability of this method compared with traditional methods were analyzed.