Synthesis,characterization and catalytic performance of Mo based metal-organic frameworks in the epoxidation of propylene by cumene hydroperoxide

Two types of Mo containing metal-organic frameworks,denoted as Mo@COMOC-4 and PMA@MIL-101（Cr）,were synthesized respectively by a post-synthetic modification and a ship-in-bottle approach.The catalytic performance of both compounds in the epoxidation of propylene using cumene hydroperoxide（CHP） as oxidant was compared with MoO3@SiO2.A higher conversion（46.2%） and efficiency（87.4%） of CHP was observed for Mo@COMOC-4,whereas the heteropoly acids supported MIL-101 resulted in the decomposition of CHP due to its strong acidic character.Regenerability tests demonstrated that Mo@COMOC-4 could be reused for multiple runs without significant loss in both activity and stability.

Impacts of Dirty Data on Classification and Clustering Models:An Experimental Evaluation

Data quality issues have attracted widespread attentions due to the negative impacts of dirty data on data mining and machine learning results.The relationship between data quality and the accuracy of results could be applied on the selection of the appropriate model with the consideration of data quality and the determination of the data share to clean.However,rare research has focused on exploring such relationship.Motivated by this,this paper conducts an experimental comparison for the effects of missing,inconsistent,and conflicting data on classification and clustering models.FYom the experimental results,we observe that dirty-data impacts are related to the error type,the error rate,and the data size.Based on the findings,we suggest users leverage our proposed metrics,sensibility and data quality inflection point,for model selection and data cleaning.