The esterification of free fatty acids(FFA) in waste cooking oil with methanol in the presence of Fe2(SO4)3/C(ferric sulfate/active carbon) catalyst was studied.The effects of different temperature,methanol/FFA mole r...The esterification of free fatty acids(FFA) in waste cooking oil with methanol in the presence of Fe2(SO4)3/C(ferric sulfate/active carbon) catalyst was studied.The effects of different temperature,methanol/FFA mole ratio and amount of catalyst on the conversion of FFA were investigated.The results demonstrated that under optimal esterification conditions the final acid value of the resultant system can be reduced to ～1(mg KOH)·g-1,which met fully the requirements in post-treatment for efficient separation of glycerin and biodiesel.The kinetics of the esterification were also investigated under different temperatures.The results indicated that the rate-control step could be attributed to the surface reaction and the esterification processes can be well-depicted by the as-calculated kinetic formula in the range of the experimental conditions.展开更多
In complex agricultural environments,cucumber disease identification is confronted with challenges like symptom diversity,environmental interference,and poor detection accuracy.This paper presents the DM-YOLO model,wh...In complex agricultural environments,cucumber disease identification is confronted with challenges like symptom diversity,environmental interference,and poor detection accuracy.This paper presents the DM-YOLO model,which is an enhanced version of the YOLOv8 framework designed to enhance detection accuracy for cucumber diseases.Traditional detection models have a tough time identifying small-scale and overlapping symptoms,especially when critical features are obscured by lighting variations,occlusion,and background noise.The proposed DM-YOLO model combines three innovative modules to enhance detection performance in a collective way.First,the MultiCat module employs a multi-scale feature processing strategy with adaptive pooling,which decomposes input features into large,medium,and small scales.This approach ensures that high-level features are extracted and fused effectively,effectively improving the detection of smaller and complex patterns that are often missed by traditional methods.Second,the ADC2f module incorporates an attention mechanism and deep separable convolution,which allows the model to focus on the most relevant regions of the input features while reducing computational load.The identification and localization of diseases like downy mildew and powdery mildew can be enhanced by this combination in conditions of lighting changes and occlusion.Finally,the C2fe module introduces a Global Context Block that uses attention mechanisms to emphasize essential regions while suppressing those that are not relevant.This design enables the model to capture more contextual information,which improves detection performance in complex backgrounds and small-object scenarios.A custom cucumber disease dataset and the PlantDoc dataset were used for thorough evaluations.Experimental results showed that DM-YOLO achieved a mean Average Precision(mAP50)improvement of 1.2%p on the custom dataset and 3.2%p on the PlantDoc dataset over the baseline YOLOv8.These results highlight the model’s enhanced ability to detect small-scale and overlapping disease symptoms,demonstrating its effectiveness and robustness in diverse agricultural monitoring environments.Compared to the original algorithm,the improved model shows significant progress and demonstrates strong competitiveness when compared to other advanced object detection models.展开更多
A novel coordination polymer, [Fe(C 5H 4NCOO) 2] n, was synthesized by hydrothermal reaction and characterized by elemental analysis as well as IR spectroscopy. The crystal structure (with a novel 3-D network) of this...A novel coordination polymer, [Fe(C 5H 4NCOO) 2] n, was synthesized by hydrothermal reaction and characterized by elemental analysis as well as IR spectroscopy. The crystal structure (with a novel 3-D network) of this compound belongs to monoclinic, space group P2 1/n, a=0.49544(1) nm, b=1.32443(2) nm, c=1.04983(1) nm, β=101.586(1)°. The diffuse reflectance spectra (200～2500 nm) showed that the polymer had strong absorbance in 375～563 nm (E g≈2.5 eV) region and weak absorbance from 720 to 2500 nm (near infrared spectra).展开更多
文摘The esterification of free fatty acids(FFA) in waste cooking oil with methanol in the presence of Fe2(SO4)3/C(ferric sulfate/active carbon) catalyst was studied.The effects of different temperature,methanol/FFA mole ratio and amount of catalyst on the conversion of FFA were investigated.The results demonstrated that under optimal esterification conditions the final acid value of the resultant system can be reduced to ～1(mg KOH)·g-1,which met fully the requirements in post-treatment for efficient separation of glycerin and biodiesel.The kinetics of the esterification were also investigated under different temperatures.The results indicated that the rate-control step could be attributed to the surface reaction and the esterification processes can be well-depicted by the as-calculated kinetic formula in the range of the experimental conditions.
基金supported by“Regional Innovation Strategy(RIS)”through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(MOE)(2021RIS-003).
文摘In complex agricultural environments,cucumber disease identification is confronted with challenges like symptom diversity,environmental interference,and poor detection accuracy.This paper presents the DM-YOLO model,which is an enhanced version of the YOLOv8 framework designed to enhance detection accuracy for cucumber diseases.Traditional detection models have a tough time identifying small-scale and overlapping symptoms,especially when critical features are obscured by lighting variations,occlusion,and background noise.The proposed DM-YOLO model combines three innovative modules to enhance detection performance in a collective way.First,the MultiCat module employs a multi-scale feature processing strategy with adaptive pooling,which decomposes input features into large,medium,and small scales.This approach ensures that high-level features are extracted and fused effectively,effectively improving the detection of smaller and complex patterns that are often missed by traditional methods.Second,the ADC2f module incorporates an attention mechanism and deep separable convolution,which allows the model to focus on the most relevant regions of the input features while reducing computational load.The identification and localization of diseases like downy mildew and powdery mildew can be enhanced by this combination in conditions of lighting changes and occlusion.Finally,the C2fe module introduces a Global Context Block that uses attention mechanisms to emphasize essential regions while suppressing those that are not relevant.This design enables the model to capture more contextual information,which improves detection performance in complex backgrounds and small-object scenarios.A custom cucumber disease dataset and the PlantDoc dataset were used for thorough evaluations.Experimental results showed that DM-YOLO achieved a mean Average Precision(mAP50)improvement of 1.2%p on the custom dataset and 3.2%p on the PlantDoc dataset over the baseline YOLOv8.These results highlight the model’s enhanced ability to detect small-scale and overlapping disease symptoms,demonstrating its effectiveness and robustness in diverse agricultural monitoring environments.Compared to the original algorithm,the improved model shows significant progress and demonstrates strong competitiveness when compared to other advanced object detection models.
文摘A novel coordination polymer, [Fe(C 5H 4NCOO) 2] n, was synthesized by hydrothermal reaction and characterized by elemental analysis as well as IR spectroscopy. The crystal structure (with a novel 3-D network) of this compound belongs to monoclinic, space group P2 1/n, a=0.49544(1) nm, b=1.32443(2) nm, c=1.04983(1) nm, β=101.586(1)°. The diffuse reflectance spectra (200～2500 nm) showed that the polymer had strong absorbance in 375～563 nm (E g≈2.5 eV) region and weak absorbance from 720 to 2500 nm (near infrared spectra).
