CrOx supported on high-silica HZSM-5 for propane dehydrogenation

Industrial propane dehydrogenation(PDH)catalysts generally suffer from low catalytic stability due to the coke formation onto the catalyst surface to cover the active sites.The exploitation of an efficient catalyst with both high catalytic selectivity and long-term stability toward PDH is of great importance but challenging to make.Herein CrOx supported on high-silica HZSM-5 with a SiO2/Al2O3 ratio of 260(Cr/Z-5(260)is synthesized by a simple wet impregnation method,which exhibits high catalytic activity,good selectivity and excellent stability for PDH.At a weight hourly space velocity(WHSV)of 0.59 h-1,a propylene formation rate of 4.1 mmol g-1cath-1(~32.6% propane conversion and ~94.2% propylene selectivity)can be maintained over the 5%Cr/Z-5(260)catalyst after 50 h time on stream,which is much better than commercial Cr/Al2O3(Catofin process,catalyst life is several hours)at the same reaction conditions.With increasing the WHSV to 5.9 h-1,a high propylene formation rate of 27.9 mmol gcat-1h-1can be obtained over the 5%Cr/Z-5(260)catalyst after 50 h time on stream,demonstrating a very promising PDH catalyst.Characterization results and Na+doping experiments reveal that the Cr species combined with Br?nsted acid sites in Cr/HZSM-5 catalysts are responsible for the high catalytic performance.In particular,the Br?nsted acid sites in HZSM-5 zeolite could increase the propane adsorption and enhance the C–H bond activation.Furthermore,the high surface area and well-defined pores of HZSM-5 zeolite can provide a special environment for the dispersion and stabilization of Cr species,thus guaranteeing high catalytic activity and stability.

Biological Sequence Classification:A Review on Data and General Methods

With the rapid development of biotechnology,the number of biological sequences has grown exponentially.The continuous expansion of biological sequence data promotes the application of machine learning in biological sequences to construct predictive models for mining biological sequence information.There are many branches of biological sequence classification research.In this review,we mainly focus on the function and modification classification of biological sequences based on machine learning.Sequence-based prediction and analysis are the basic tasks to understand the biological functions of DNA,RNA,proteins,and peptides.However,there are hundreds of classification models developed for biological sequences,and the quite varied specific methods seem dizzying at first glance.Here,we aim to establish a long-term support website(http://lab.malab.cn/~acy/BioseqData/home.html),which provides readers with detailed information on the classification method and download links to relevant datasets.We briefly introduce the steps to build an effective model framework for biological sequence data.In addition,a brief introduction to single-cell sequencing data analysis methods and applications in biology is also included.Finally,we discuss the current challenges and future perspectives of biological sequence classification research.

Transcript profile of CLSTN3B gene in human white adipose tissue is associated with obesity and mitochondrial gene program

Dear Editor,Obesity is a major health issue with global prevalence and increases the risk of many metabolic diseases.Of particular con-cern is the increasing incidence of type 2 diabetes,the primary causes of which are obesity-driven insulin resistance in white adipose tissue(WAT),skeletal muscle,and liver,and decreased insulin secretion by pancreaticβ-cells[1].

Community assemblage of free-living diazotrophs along the elevational gradient of Mount Gongga

Mountain systems are unique for studying the responses of species distribution and diversity to environmental changes along elevational gradients.It is well known that free-living diazotrophic microorganisms are important to nitrogen cycling in mountain systems.However,the elevational patterns of free-living diazotrophs and the underlying ecological processes in controlling their turnover along broader gradients are less well documented.Here,we investigated the pattern of diazotrophic diversity along the elevational gradient(1800 m-4100 m)in Mount Gongga of China.The results showed that the α-diversity of diazotrophs did not change with the elevation from 1800 m to 2800 m,but decreased at elevations above 3000 m.Such diversity pattern was driven mainly by soil total carbon,nitrogen,and plant richness.Various diazotrophic taxa showed differential abundance-elevation relationships.Ecological processes determining diazotrophic community assemblage shift along the elevations.Deterministic processes were relatively stronger at both low and high elevations,whereas stochastic processes were stronger at the middle elevation.This study also suggested a strong relationship among aboveground plants and diazotrophs,highlighting their potential interactions,even for free-living diazotrophs.