MOF-derived Zn-Co-Ni sulfides with hollow nanosword arrays for high-efficiency overall water and urea electrolysis

Water electrolysis is a promising technology to produce hydrogen but it was severely restricted by the slow oxygen evolution reaction(OER).Herein,we firstly reported an advanced electrocatalyst of MOF-derived hollow Zn-Co-Ni sulfides(ZnS@Co_(9)S_(8)@Ni_(3)S_(2)-1/2,abbreviated as ZCNS-1/2)nanosword arrays(NSAs)with remarkable hydrogen evolution reaction(HER),OER and corresponding water electrolysis performance.To reach a current density of 10 mA cm^(-2),the cell voltage of assembled ZCNS-1/2//ZCNS-1/2 for urea electrolysis(1.314 V)is 208 mV lower than that for water electrolysis(1.522 V)and stably catalyzed for over 15 h,substantially outperforming the most reported water and urea electrolysis electrocatalysts.Density functional theory calculations and experimental result clearly reveal that the properties of large electrochemical active surface area(ECSA)caused by hollow NSAs and fast charge transfer resulted from the Co_(9)S_(8)@Ni_(3)S_(2) heterostructure endow the ZCNS-1/2 electrode with an enhanced electrocatalytic performance.

Anisodine hydrobromide alleviates oxidative stress caused by hypoxia/reoxygenation in human cerebral microvascular endothelial cells predominantly via inhibition of muscarinic acetylcholine receptor 4

Background:Anisodine hydrobromide(AT3),an anti-cholinergic agent,could be delivered to the brain across the blood-brain barrier and has been used clinically for the treatment of cerebral ischemia/reperfusion injury.Endothelial dysfunction can be caused by hypoxia/reoxygenation(H/R)via oxidative stress and metabolic alterations.The present study investigated whether AT3 regulates the production of nitric oxide(NO)and reactive oxygen species(ROS),and the HIF-1αpathway via regulation of muscarinic acetylcholine receptors(mAChRs)in brain microvascular endothelial cells after H/R exposure.Methods:Under H/R conditions,hCMEC/D3 cerebral microvascular endothelial cells were treated with AT3.Specific inhibitors of M2-and M4-mAChRs were used to explore the mechanism by which AT3 influences oxidative stress in endothelial cells.Then,mAChRs expression was detected by western blotting and NO production was detected by Greiss reaction.The intracellular ROS level was measured using DCFH-DA probes.The expression of hypoxia-inducible transcription factor 1α(HIF-1α)was also detected.Results:While H/R induced the expression of M2-and M4-mAChRs,AT3 suppressed the H/R-upregulated M2-and M4-mAChRs.H/R also induced the production of NO,ROS,and apoptosis.AT3 and M4-mAChR inhibitors inhibited the H/R-induced production of NO and ROS and apoptosis.HIF-1αwas induced by H/R,but was suppressed by AT3.Conclusion:Thus,the in vitro evidence shows that AT3 protects against H/R injury in cerebral microvascular endothelial cells via inhibition of HIF-1α,NO and ROS,predominantly through the downregulation of M4-mAChR.The findings offer novel understandings regarding AT3-mediated attenuation of endothelial cell apoptosis and cerebral ischemia/reperfusion injury.

DMT:A model detecting multispecies of tea buds in multi-seasons

In China,tea products made from fresh leaves characterized by one leaf with one bud(1L1B)are classified as“Famous Tea”,which has better taste and higher economic value,but suffers from a labor shortage.Aiming at picking automation,existing studies focus on visual detection of 1L1B,but algorithm validation is limited to a specific variety of tea sprouting in a certain harvest season at a certain location,which limits the engineering application of developed tea picking robots working in various natural tea fields.To address this gap,a deep learning model DMT(detecting multispecies of tea)based on YOLOX-S was proposed in this paper.The DMT network takes YOLOX-S as a baseline and adds ECA-Net to the CSP Darknet and FPN of YOLOX-S.The average precision(AP),precision,and recall of DMT are 94.23%,93.39%,and 88.02%,respectively,for detecting 1L1B sprouting in spring;93.92%,93.56%,and 87.88%,respectively,for detecting 1L1Bsprouting in autumn.These experimental results are better than those of the five current object detection models.After fine-tuning the DMT network with another dataset composed of multiple tea varieties,the DMT network can detect 1L1B for different varieties of tea in multiple picking seasons.The results can promote the engineering application of picking automation of fresh tea leaves.

Polyoxometalates as the redox anolyte for efficient conversion of biomass to formic acid

Electrolytic water is considered to be the first choice for large-scale industrial hydrogen production in the future due to its good matching with wind power,photovoltaic and other renewable energy power generation systems[1].In recent years,the emerging proton exchange membrane(PEM)water electrolysis for hydrogen production(Fig.1(a))has gradually become the mainstream development direction due to its advantages such as fast start and stop,high working current density(greater than 1 A.cm^(-2)),small size of electrolytic cell,and good matching with renewable electric energy.However,in acidic electrolytes,the ki-netics of oxygen evolution reaction(OER)half-reaction is very slow.In addition,the lower commercial value of anode O_(2)further reduces the economic benefits of traditional PEM technology.Therefore,from the perspective of reducing energy consumption and improving economic benefits,finding a more dynamic and/or thermodynamic OER alternative oxidation reaction has become the key to promote the practical appli-cation of hydrogen production by electrolytic water[2].

Rotating machinery fault diagnosis based on convolutional neural network and infrared thermal imaging

Rotating machinery is widely applied in industrial applications.Fault diagnosis of rotating machinery is vital in manufacturing system,which can prevent catastrophic failure and reduce financial losses.Recently,Deep Learning(DL)-based fault diagnosis method becomes a hot topic.Convolutional Neural Network(CNN)is an effective DL method to extract the features of raw data automatically.This paper develops a fault diagnosis method using CNN for InfRared Thermal(IRT)image.First,IRT technique is utilized to capture the IRT images of rotating machinery.Second,the CNN is applied to extract fault features from the IRT images.In the end,the obtained features are fed into the Softmax Regression(SR)classifier for fault pattern identification.The effectiveness of the proposed method is validated using two different experimental data.Results show that the proposed method has a superior performance in identification various faults on rotor and bearings comparing with other deep learning models and traditional vibration-based method.

Design and experiment of the comb-brush harvesting machine with variable spacing for oil-tea camellia fruit

Oil-tea camellia tree is an important oil plant in China that has long flexible branches.The most challenging feature for the mechanized harvest of oil-tea fruits is that its flower and fruit grow synchronously.In order to improve the harvesting efficiency and avoid damaging the flower bud,a hand-held fruit harvesting machine with a variable spacing comb brush was proposed.The harvesting machine can generate three kinds of actuation to detach fruit when it runs.The main actuation results from the brushing of multiple comb fingers.The other two kinds of actuation result from the beating of comb fingers on the fruits and the branches.The finger spacing of the comb brush can be adjusted consequently through moving the spacing adjusting crossbar.Hence,when the finger spacing is smaller than the diameter of the oil-tea fruit,the fruit is brushed off,but the flower bud and leaf pass through the finger gap.When the finger spacing is bigger than the fruit diameter,the fruit stuck between the fingers is loosened to ensure the continuous operation of the machine.Nylon was used as the material of the brush finger to avoid damage,which can also reduce the overall weight.The dynamic simulation of the harvesting machine was carried out with ADAMS,and the acceleration of the front end of the comb finger and the variation of the finger spacing were analyzed.The prototype of the harvesting machine was built and tested in the field.Field experiment results showed that when the speed of the comb finger drive shaft was 480 r/min,the average harvesting percentage of oil-tea fruit was 80%,and the flower bud was seldom detached,which met the working requirements of oil-tea fruit harvesting.