Search

NaTi2(PO4)3 (NTP) is considered a promising anode material for sodium-ion batteries due to its open and stable framework structure. However, its poor conductivity prevents its direct application as an anode material. In this paper, a dual strategy of La3+ doping and carbon coating is proposed to improve the conductivity of NTP. The results show that the NTP-La0.06@C electrode doped with 6.00% La and 5.00 wt% of carbon coating has excellent electrochemical performance, and the coulombic efficiency is up to 95.31% in the first cycle. The initial specific capacity is 103.05 mAh·g−1 with a capacity retention of 97.30% after 1000 cycles at 10C rate. Even after 2000 cycles, there is still a specific capacity of 98.62 mAh·g−1 with a capacity retention of 92.71%, and as well as the NTP-La0.06@C has a higher Na+-insert diffusion coefficient of 3.19×10−11 cm2·s−1 than the pristine NTP@C of 2.35×10−11 cm2·s−1 during charging. Therefore, the La3+ doping and carbon coating strategy shows great potential in improving the performance of NaTi2(PO4)3 anode materials. [ABSTRACT FROM AUTHOR]

A solar heat pump based on the photovoltaic photothermal (PV/T) module is a new technology that can improve the photovoltaic efficiency and recovery of waste heat in photovoltaic conversion. The comprehensive efficiency of a system can thus be greatly improved. At present, there is little research on the simulation of a solar heat pump based on the Simulink 2018 software. In this paper, PV/T modules are modeled and simulated using the Simulink software based on the typical meteorological parameters in Beijing city during summer and winter conditions. Considering the system characteristics of a simultaneous operation, the models of all components such as the compressor, condenser, evaporator, expansion valve, and heat storage tanks contained in a system are established based on the system's working principle, energy conservation equations, and some empirical formulas and then simulated. As PV/T modules are the key influencing factor for system performance, the model and simulation process are introduced in detail in this paper. The results show that the surface temperature of PV/T modules under different operating conditions are all significantly lower than that of ordinary photovoltaic panels. The average temperature of PV/T modules is only 15.2 °C, which is 20.2 °C lower than that of ordinary panels under summer conditions, and the average temperature of PV/T modules is only 0.5 °C, 17.8 °C lower than that of ordinary panels under winter conditions. The average photoelectric efficiency can be improved by 15.4% and 8.9%. And, the temperature change amplitude of PV/T modules is lower, which weakens the temperature fluctuation of the modules. The photovoltaic efficiency is therefore further increased. As a result, the power generation and photovoltaic efficiency are both improved significantly. [ABSTRACT FROM AUTHOR]

With the increasing demand for logistics in modern society, how to achieve low-cost and efficient logistics delivery has become an urgent research topic. A hybrid evolutionary JAYA algorithm (H-JAYA) based on global optimization was designed to address the complex path planning problem of electric vehicles. This algorithm introduces a reverse learning mechanism to calculate the current optimal and worst individuals, while using differential perturbation mechanism and sine cosine operator to update the individual's position. In addition, the study used the H-JAYA algorithm to construct a corresponding mathematical model for the optimization problem of electric vehicle paths. The results showed that in the three examples, the H-JAYA algorithm tested the optimal curve convergence speed, and it tended to stabilize after about 30 iterations. Meanwhile, in the RCDP5001 example, the total cost of the H-JAYA algorithm reached the lowest value of 623 yuan. The H-JAYA algorithm has significant advantages in solving the distribution path problem of electric vehicles, and can be well applied to practical logistics distribution, providing effective technical support for modern e-commerce logistics planning. [ABSTRACT FROM AUTHOR]

High‐resolution and accurate prediction of near‐surface weather parameters based on numerical weather prediction (NWP) models is essential for many downstream and real‐world applications. Traditional dynamical or statistical downscaling methods are insufficient to derive high‐resolution data from operational NWP forecasts, making it essential to devise new approaches. In recent years, an increasing number of researchers have explored the implementations of deep learning (DL) based models for spatial downscaling, motivated by the similarity between the super‐resolution (SR) problem in computer vision (CV) and downscaling. Furthermore, while transformer‐based models have become state‐of‐the‐art models for many SR tasks, they are rarely applied for downscaling of weather forecasts or climate projections. This study adapted transformer‐based models such as SwinIR and Uformer to downscale the temperature at 2 m (T2m$$ {T}_{2\mathrm{m}} $$) and wind speed at 10 m (WS10m$$ W{S}_{10\mathrm{m}} $$) over Eastern Inner Mongolia, encompassing the area from 39.6–46°N latitude and 111.6–118°E longitude. We used high‐resolution forecast (HRES) data from the European Centre for Medium‐range Weather Forecast (ECMWF) with a spatial resolution of 0.1° as the input and gridded observation data from the China Meteorological Administration (CMA) Land Data Assimilation System (CLDAS) at a spatial resolution of 0.01° as the target. Given that the models use observation data rather than a coarse‐grained version of forecast data as the target, they accomplish both bias correction and spatial downscaling. The results demonstrate that the performance of SwinIR and Uformer is superior to that of two convolutional neural network (CNN) based models (UNet and RCAN). Additionally, we introduced a novel module to extract features of varying resolution from the high‐resolution topography data and applied a multiscale feature fusion module to merge features of different scales, contributing to further enhancement of Uformer's performance. [ABSTRACT FROM AUTHOR]

Warm-sector heavy rainfall often occurs along the coast of South China, and it is usually localized and long-lasting, making it challenging to predict. High-resolution numerical weather prediction (NWP) models are increasingly used to better resolve topographic features and forecast such high-impact weather events. However, when the grid spacing becomes comparable to the length scales of convection, known as the gray zone, the turbulent eddies in the atmospheric boundary layer are only partially resolved and parameterized to some extent. Whether using a convection parameterization (CP) scheme in the gray zone remains controversial. Scale-aware CP schemes are developed to enhance the representation of convective transport within the gray zone. The multi-scale Kain-Fritsch (MSKF) scheme includes modifications that allow for its effective implementation at a grid resolution as high as 2 km. In recent years, there has been an increasing application of machine learning (ML) models to various domains of atmospheric sciences, including the replacement of physical parameterizations with ML models. This work proposes a multi-output bidirectional long short-term memory (Bi-LSTM) model as a replace the scale-aware MSKF CP scheme. The Weather Research and Forecast (WRF) model is used to generate training and testing data over South China at a horizontal resolution of 5 km. Furthermore, the WRF model is coupled with the ML based CP scheme and compared with WRF simulations with original MSKF scheme. The results demonstrate that the Bi-LSTM model can achieve high accuracy, indicating the potential use of ML models to substitute the MSKF scheme in the gray zone. [ABSTRACT FROM AUTHOR]

Developing non‐noble metal catalysts for the oxygen evolution reaction (OER) is one of the main challenges in the production of hydrogen energy by water splitting reaction. Herein, taking advantage of the fact that microorganisms have high element contents and metabolic activities, we used them to recycle Ni and Fe from electroplating sludge. Based on the above, we designed a highly efficient oxygen evolution catalyst (Fe‐Ni2P@NPC) through the in situ synthesis of Fe‐doped Ni2P nanoparticles and N, P dual‐doped carbon materials coupled together. The material exhibited excellent catalytic activity, with a low overpotential of 282 mV at the current density of 10 mA cm−2 along with a fast reaction kinetics, with a small Tafel slope of 57.1 mV dec−1. This outstanding OER performance is mainly attributed to the in situ doping of Fe in Ni2P nanoparticles, resulting in a strong synergistic effect between them. Besides, the strong coupling interaction between Fe‐Ni2P and Fe‐NiOOH formed during the OER can adjust the electron distribution and optimize the adsorption capacity of the intermediates. This work provides a reference for the recycling of valuable Ni and Fe metals from electroplating sludge, as well as new insight into the preparation of low‐cost and high‐performance OER catalysts. [ABSTRACT FROM AUTHOR]

Ni-rich layered cathode materials LiNixCoyMn(1-x–y)O2 (x ≥ 0.8) suffer from capacity decay due to structural deterioration during electrochemical cycling. To overcome these problems, the fast-ion conductor Li0.33La0.56TiO3 (LLTO) is coated on the surface of LiNi0.83Co0.12Mn0.05O2 (NCM83) cathodes through the sol–gel method, which can stabilize the electrode/electrolyte interface of cathode materials and facilitate Li+ transport and charge transfer. As a result, the LLTO-coated NCM83 exhibits better cycle stability and higher rate performance. The NCM83 modified by 5 wt% LLTO has the discharge capacity of 185.9 mAh g−1 at 1C rate after 100 cycles and with higher capacity retention of 92.9% than the pristine NCM83 of 86.2%, and as well as the 5 wt% LLTO-coated NCM83 has a higher Li+-insert diffusion coefficient of 1.143 × 10−11 cm2 s−1 than the pristine NCM83 of 8.757 × 10−12 cm2 s−1. Therefore, the fast-ion conductor LLTO coating strategy shows great potential in improving the performance of Ni-rich layered cathode materials. [ABSTRACT FROM AUTHOR]