Enhancement of catalytic activity for hydrogenation of nitroaromatic by anionic metal-organic framework

Nitroaromatic hydrogenation catalysis without precious metals remains a longstanding challenge.The rate of electron transfer is the crucial factor affecting hydrogenation catalysis.Herein,an ionic Cd-based metal-organic framework(I-Cd-MOF)exhibiting a unique structure with one-dimensional(1D)opening nanochannels and good electron transfer ability was synthesized for catalyzing hydrogenation of 4-nitrophenol(4-NP).The catalytic activity of the unique I-Cd-MOF without noble metals is detected,which is higher than most reported noble metal catalysts.Remarkably,the reaction rate of I-Cd-MOF(4.28 min^(-1))is about 47.6 times higher than that of the Cd-based neutral MOF(N-Cd-MOF)with the similar crystalline structure.Liquid chromatograph mass spectrometer(LC-MS)and theoretical results demonstrate that 4-NP and five intermediates are stabilized in the channels of I-Cd-MOF,which increases the possibility of contact with H^*and H_(2)g enerated at the Cd sites.The I-Cd-MOF was extended to other nitroaromatic hydrogenation catalysis,which still displays excellent activity.More importantly,the I-MOF@Filter membrane was successfully constructed for continuous hydrogenation catalytic reactions,which maintains a high catalytic performance after 7 cycles of recycling without washing.This work fills in the application of the I-MOFs in hydrogenation catalytic reactions and provides an effective way for the rapid and green degradation of nitroaromatic compounds.

Exploring financially constrained small- and medium-sized enterprises based on a multi-relation translational graph attention network

Financing needs exploration(FNE),which exploresfinancially constrained small-and medium-sized enterprises(SMEs),has become increasingly important in industry forfinancial institutions to facilitate SMEs’development.In this paper,wefirst perform an insightful exploratory analysis to exploit the transfer phenomenon offinancing needs among SMEs,which motivates us to fully exploit the multi-relation enterprise social network for boosting the effectiveness of FNE.The main challenge lies in modeling two kinds of heterogeneity,i.e.,transfer heterogeneity and SMEs’behavior heterogeneity,under different relation types simultaneously.To address these challenges,we propose a graph neural network named Multi-relation tRanslatIonal GrapH a Ttention network(M-RIGHT),which not only models the transfer heterogeneity offinancing needs along different relation types based on a novel entity–relation composition operator but also enables heterogeneous SMEs’representations based on a translation mechanism on relational hyperplanes to distinguish SMEs’heterogeneous behaviors under different relation types.Extensive experiments on two large-scale real-world datasets demonstrate M-RIGHT’s superiority over the state-of-the-art methods in the FNE task.