The stable and crystalline phase of pure nano- structured CeO2 was directly synthesized by flame-assisted spray pyrolysis and solid state diffusion route. Different characterization techniques, including X-ray diffrac...The stable and crystalline phase of pure nano- structured CeO2 was directly synthesized by flame-assisted spray pyrolysis and solid state diffusion route. Different characterization techniques, including X-ray diffraction （XRD）, scanning electron microscopy （SEM）, Fourier trans- form infrared spectroscopy （FTIR）, ultraviolet-visible （UV- Vis）, and thermo gravimetric analysis （TGA） were employed to examine the structural, morphological, optical, and thermal properties of the final product. Similarly, the comparative carbon dioxide （CO2）-sensing response of as-synthesized CeO2 nanoparticles by both routes was also reported. The CeO2 nanoparticles synthesized by solid state diffusion method exhibit good sensitivity （3.38 %） at room temperature, low operating temperature （398 K）, fast response time （32 s）, and recovery time （36 s） along with good stability.展开更多
The converter steelmaking process represents a pivotal aspect of steel metallurgical production,with the characteristics of the flame at the furnace mouth serving as an indirect indicator of the internal smelting stag...The converter steelmaking process represents a pivotal aspect of steel metallurgical production,with the characteristics of the flame at the furnace mouth serving as an indirect indicator of the internal smelting stage.Effectively identifying and predicting the smelt-ing stage poses a significant challenge within industrial production.Traditional image-based methodologies,which rely on a single static flame image as input,demonstrate low recognition accuracy and inadequately extract the dynamic changes in smelting stage.To address this issue,the present study introduces an innovative recognition model that preprocesses flame video sequences from the furnace mouth and then employs a convolutional recurrent neural network(CRNN)to extract spatiotemporal features and derive recognition outputs.Ad-ditionally,we adopt feature layer visualization techniques to verify the model’s effectiveness and further enhance model performance by integrating the Bayesian optimization algorithm.The results indicate that the ResNet18 with convolutional block attention module(CBAM)in the convolutional layer demonstrates superior image feature extraction capabilities,achieving an accuracy of 90.70%and an area under the curve of 98.05%.The constructed Bayesian optimization-CRNN(BO-CRNN)model exhibits a significant improvement in comprehensive performance,with an accuracy of 97.01%and an area under the curve of 99.85%.Furthermore,statistics on the model’s average recognition time,computational complexity,and parameter quantity(Average recognition time:5.49 ms,floating-point opera-tions per second:18260.21 M(1 M=1×10^(6)),parameters:11.58 M)demonstrate superior performance.Through extensive repeated ex-periments on real-world datasets,the proposed CRNN model is capable of rapidly and accurately identifying smelting stages,offering a novel approach for converter smelting endpoint control.展开更多
Rechargeable lithium-metal batteries that are operated based on reversible metal plating and stripping during the charge/discharge process are known for their high energy density far beyond the conventional,graphite-a...Rechargeable lithium-metal batteries that are operated based on reversible metal plating and stripping during the charge/discharge process are known for their high energy density far beyond the conventional,graphite-anode-based Li-ion batteries[1].However,the hostless structural evolution of Li metal during the anode process easily forms dendrites and could lead to a hazardous short circuit of batteries[2].In addition.展开更多
文摘The stable and crystalline phase of pure nano- structured CeO2 was directly synthesized by flame-assisted spray pyrolysis and solid state diffusion route. Different characterization techniques, including X-ray diffraction （XRD）, scanning electron microscopy （SEM）, Fourier trans- form infrared spectroscopy （FTIR）, ultraviolet-visible （UV- Vis）, and thermo gravimetric analysis （TGA） were employed to examine the structural, morphological, optical, and thermal properties of the final product. Similarly, the comparative carbon dioxide （CO2）-sensing response of as-synthesized CeO2 nanoparticles by both routes was also reported. The CeO2 nanoparticles synthesized by solid state diffusion method exhibit good sensitivity （3.38 %） at room temperature, low operating temperature （398 K）, fast response time （32 s）, and recovery time （36 s） along with good stability.
基金financially supported by the National Natural Science Foundation of China(No.52374320).
文摘The converter steelmaking process represents a pivotal aspect of steel metallurgical production,with the characteristics of the flame at the furnace mouth serving as an indirect indicator of the internal smelting stage.Effectively identifying and predicting the smelt-ing stage poses a significant challenge within industrial production.Traditional image-based methodologies,which rely on a single static flame image as input,demonstrate low recognition accuracy and inadequately extract the dynamic changes in smelting stage.To address this issue,the present study introduces an innovative recognition model that preprocesses flame video sequences from the furnace mouth and then employs a convolutional recurrent neural network(CRNN)to extract spatiotemporal features and derive recognition outputs.Ad-ditionally,we adopt feature layer visualization techniques to verify the model’s effectiveness and further enhance model performance by integrating the Bayesian optimization algorithm.The results indicate that the ResNet18 with convolutional block attention module(CBAM)in the convolutional layer demonstrates superior image feature extraction capabilities,achieving an accuracy of 90.70%and an area under the curve of 98.05%.The constructed Bayesian optimization-CRNN(BO-CRNN)model exhibits a significant improvement in comprehensive performance,with an accuracy of 97.01%and an area under the curve of 99.85%.Furthermore,statistics on the model’s average recognition time,computational complexity,and parameter quantity(Average recognition time:5.49 ms,floating-point opera-tions per second:18260.21 M(1 M=1×10^(6)),parameters:11.58 M)demonstrate superior performance.Through extensive repeated ex-periments on real-world datasets,the proposed CRNN model is capable of rapidly and accurately identifying smelting stages,offering a novel approach for converter smelting endpoint control.
基金supported by the National Natural Science Foundation of China(22279028,22179018)the Natural Science Foundation of Hebei Province(B2021205019)the 333 Project of Hebei Province(C20231106).
文摘Rechargeable lithium-metal batteries that are operated based on reversible metal plating and stripping during the charge/discharge process are known for their high energy density far beyond the conventional,graphite-anode-based Li-ion batteries[1].However,the hostless structural evolution of Li metal during the anode process easily forms dendrites and could lead to a hazardous short circuit of batteries[2].In addition.
