Post-synthetic treatment of high-silica as-made ZSM-5 with organic template in the micropores was explored to reduce/remove the external surface acid density of ZSM-5. It is found that Na_2H_2 EDTA treatment can selec...Post-synthetic treatment of high-silica as-made ZSM-5 with organic template in the micropores was explored to reduce/remove the external surface acid density of ZSM-5. It is found that Na_2H_2 EDTA treatment can selectively remove the surface Al atoms, but generates new acid sites（likely silanol nests） on the external surface. H_3PO_4 treatment is unable to remove surface Al atoms, while small amount of P is left on the external surface, which effectively decreases the acid density. The catalytic performance of the resultant materials is evaluated in the methanol conversion reaction. H_3PO_4 treatment can effectively improve both the catalytic lifetime and the stability of propene selectivity.This occurs due to a combination of the increased tolerance to the external coke deposition and the depressed coking rate（reduced side reactions）. Na_2H_2 EDTA treatment only prolongs the catalytic lifetime, resulting from the improved tolerance to the external coke deposition. Under the optimized H_3PO_4 treatment condition, the resultant ZSM-5 gives a catalytic lifetime of about 1.5 times longer than the precursor. Moreover, the propene selectivity is improved, showing a slight increasing trend until the deactivation.展开更多
Solar flares are solar storm events driven by the magnetic field in the solar activity area.Solar flare,often associated with solar proton event or CME,has a negative impact on ratio communication,aviation,and aerospa...Solar flares are solar storm events driven by the magnetic field in the solar activity area.Solar flare,often associated with solar proton event or CME,has a negative impact on ratio communication,aviation,and aerospace.Therefore,its forecasting has attracted much attention from the academic community.Due to the limitation of the unbalanced distribution of the observation data,most techniques failed to effectively learn complex magnetic field characteristics,leading to poor forecasting performance.Through the statistical analysis of solar flare magnetic map data observed by SDO/HMI from 2010 to 2019,we find that unsupervised clustering algorithms have high accuracy in identifying the sunspot group in which the positive samples account for the majority.Furthermore,for these identified sunspot groups,the ensemble model that integrates the capability of boosting and convolutional neural network(CNN)achieves high-precision prediction of whether the solar flares will occur in the next 48 hours.Based on the above findings,a two-stage solar flare early warning system is established in this paper.The F1 score of our method is 0.5639,which shows that it is superior to the traditional methods such as logistic regression and support vector machine(SVM).展开更多
文摘Post-synthetic treatment of high-silica as-made ZSM-5 with organic template in the micropores was explored to reduce/remove the external surface acid density of ZSM-5. It is found that Na_2H_2 EDTA treatment can selectively remove the surface Al atoms, but generates new acid sites（likely silanol nests） on the external surface. H_3PO_4 treatment is unable to remove surface Al atoms, while small amount of P is left on the external surface, which effectively decreases the acid density. The catalytic performance of the resultant materials is evaluated in the methanol conversion reaction. H_3PO_4 treatment can effectively improve both the catalytic lifetime and the stability of propene selectivity.This occurs due to a combination of the increased tolerance to the external coke deposition and the depressed coking rate（reduced side reactions）. Na_2H_2 EDTA treatment only prolongs the catalytic lifetime, resulting from the improved tolerance to the external coke deposition. Under the optimized H_3PO_4 treatment condition, the resultant ZSM-5 gives a catalytic lifetime of about 1.5 times longer than the precursor. Moreover, the propene selectivity is improved, showing a slight increasing trend until the deactivation.
基金supported by the National Key Research and Development Program of China (2022YFC2303100)the National Natural Science Foundation of China (T2325010, 22305082, 52203162, and 22075078)+6 种基金Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism (Shanghai Municipal Education Commission)the Program of Shanghai Academic/Technology Research Leader (20XD1421400)the Open Research Fund of the State Key Laboratory of Polymer Physics and Chemistry (Changchun Institute of Applied Chemistry, Chinese Academy of Sciences)the Open Project of Engineering Research Center of Dairy Quality and Safety Control Technology (Ministry of Education, R202201)China National Postdoctoral Program for Innovative Talents (BX2021102)China Postdoctoral Science Foundation (2022M710050)the support of the Analysis and Testing Center of School of Chemical Engineering, East China university of Science and Technology。
基金This work is support in part by the National Natural Science Foundation of China under Grant Nos.12071166 and 42171351Hubei Key Laboratory of Applied Mathematics under Grant Nos.HBAM 202004 and 201612Hubei Provincial Natural Science Foundation of China under Grant No.2021CFA087.
文摘Solar flares are solar storm events driven by the magnetic field in the solar activity area.Solar flare,often associated with solar proton event or CME,has a negative impact on ratio communication,aviation,and aerospace.Therefore,its forecasting has attracted much attention from the academic community.Due to the limitation of the unbalanced distribution of the observation data,most techniques failed to effectively learn complex magnetic field characteristics,leading to poor forecasting performance.Through the statistical analysis of solar flare magnetic map data observed by SDO/HMI from 2010 to 2019,we find that unsupervised clustering algorithms have high accuracy in identifying the sunspot group in which the positive samples account for the majority.Furthermore,for these identified sunspot groups,the ensemble model that integrates the capability of boosting and convolutional neural network(CNN)achieves high-precision prediction of whether the solar flares will occur in the next 48 hours.Based on the above findings,a two-stage solar flare early warning system is established in this paper.The F1 score of our method is 0.5639,which shows that it is superior to the traditional methods such as logistic regression and support vector machine(SVM).
