Simultaneously Regulating Uniform Zn^(2+) Flux and Electron Conduction by MOF/rGO Interlayers for High‑Performance Zn Anodes

Owing to the merits of low cost,high safety and environmental benignity,rechargeable aqueous Zn-based batteries(ZBs)have gained tremendous attention in recent years.Nevertheless,the poor reversibility of Zn anodes that originates from dendrite growth,surface passivation and corrosion,severely hinders the further development of ZBs.To tackle these issues,here we report a Janus separator based on a Zn-ion conductive metal-organic framework(MOF)and reduced graphene oxide(rGO),which is able to regulate uniform Zn2+flux and electron conduction simultaneously during battery operation.Facilitated by the MOF/rGO bifunctional interlayers,the Zn anodes demonstrate stable plating/stripping behavior(over 500 h at 1 mA cm^(−2)),high Coulombic efficiency(99.2%at 2 mA cm^(−2) after 100 cycles)and reduced redox barrier.Moreover,it is also found that the Zn corrosion can be effectively retarded through diminishing the potential discrepancy on Zn surface.Such a separator engineering also saliently promotes the overall performance of Zn|MnO2 full cells,which deliver nearly 100%capacity retention after 2000 cycles at 4 A g^(−1) and high power density over 10 kW kg^(−1).This work provides a feasible route to the high-performance Zn anodes for ZBs.

Oxygen‑Deficient β‑MnO_(2)@Graphene Oxide Cathode for High‑Rate and Long‑Life Aqueous Zinc Ion Batteries

Recent years have witnessed a booming interest in grid-scale electrochemical energy storage,where much attention has been paid to the aqueous zinc ion batteries(AZIBs).Among various cathode materials for AZIBs,manganese oxides have risen to prominence due to their high energy density and low cost.However,sluggish reaction kinetics and poor cycling stability dictate against their practical application.Herein,we demonstrate the combined use of defect engineering and interfacial optimization that can simultaneously promote rate capability and cycling stability of MnO_(2) cathodes.β-MnO_(2) with abundant oxygen vacancies(VO)and graphene oxide(GO)wrapping is synthesized,in which VO in the bulk accelerate the charge/discharge kinetics while GO on the surfaces inhibits the Mn dissolution.This electrode shows a sustained reversible capacity of~129.6 mAh g^(−1) even after 2000 cycles at a current rate of 4C,outperforming the state-of-the-art MnO_(2)-based cathodes.The superior performance can be rationalized by the direct interaction between surface VO and the GO coating layer,as well as the regulation of structural evolution ofβ-MnO_(2) during cycling.The combinatorial design scheme in this work offers a practical pathway for obtaining high-rate and long-life cathodes for AZIBs.

One-pot synthesis of FeNxC as efficient catalyst for high-performance zinc-air battery

Rechargeable zinc-air batteries(ZAB)with a high theoretical energy density of 1086 Wh kg^(-1),have received tremendous research attention.However,the practical application of ZABs is still limited by high polarization and poor energy efficiency(low power density)due to the sluggish 4 electrons(e^(-))/oxygen(O_(2))kinetics over the air electrode.Here,a noble-metal-free Fe Nx C electrocatalyst is developed via a onepot approach,which provides a high density of the oxygen reduction reaction(ORR)active site and facilitates the ORR kinetics.Accordingly,the as-assembled Zn-air battery displayed a low charge–discharge voltage gap of 0.71 V at 10 m A cm^(-2),a remarkable peak power density as high as 181.2 m W cm^(-2),as well as the long-term durability for hundreds of hours,among the top level of those reported previously.Our work provides a major boost for the practical application of Zn-air battery in the future.

Mo doped Ru-based cluster to promote alkaline hydrogen evolution with ultra-low Ru loading

Green hydrogen energy developed through electrochemistry is one of the solutions to current energy problems.The less noble metal ruthenium(Ru)plays an important role in alkaline electrocatalytic hydrogen evolution reaction(HER)as an effective electrocatalyst.Nevertheless,the high cost(>110 RMB per gram)hinders the large-scale application of Ru in industrial hydrogen production.Moreover,the strong adsorption of OH*in-termediates over Ru limits the electrocatalytic performance in alkaline HER.Here,we report the Mo-doped Ru nanocluster embedded on N-doped carbon framework(RuMo/NC)as alkaline HER catalyst,which shows excellent catalytic performance with an overpotential of 24.2 mV to reach 10 mA cm-2 with only 0.4 wt%o of Ru,much lower than that of most reported Ru-based catalysts.DFT calculations reveal the introduction of Mo has improved the activity by alleviating the poisoning effect of OH*over Ru in HER.Through fully utilizing Ru in the catalyst,this work marks a step forward in the development of Ru-based catalysts in alkaline HER.

Emerging biotechnology applications in natural product and synthetic pharmaceutical analyses

Pharmaceutical analysis is a discipline based on chemical, physical, biological, and information technologies. At present, biotechnological analysis is a short branch in pharmaceutical analysis;however, bioanalysis is the basis and an important part of medicine. Biotechnological approaches can provide information on biological activity and even clinical efficacy and safety, which are important characteristics of drug quality. Because of their advantages in reflecting the overall biological effects or functions of drugs and providing visual and intuitive results, some biotechnological analysis methods have been gradually applied to pharmaceutical analysis from raw material to manufacturing and final product analysis,including DNA super-barcoding, DNA-based rapid detection, multiplex ligation-dependent probe amplification, hyperspectral imaging combined with artificial intelligence, 3D biologically printed organoids,omics-based artificial intelligence, microfluidic chips, organ-on-a-chip, signal transduction pathwayrelated reporter gene assays, and the zebrafish thrombosis model. The applications of these emerging biotechniques in pharmaceutical analysis have been discussed in this review.

Genome-wide identification and transcriptional profiling analysis of PIN/PILS auxin transporter gene families in Panax ginseng

Objective:Plant hormones act as chemical messengers in the regulation of plant development and metabolism.The production of ginsenosides in Panax hybrid is promoted by auxins that are transported and accumulated by PIN-FORMED(PIN)and PIN-LIKES(PILS)auxin transporters.However,genome-wide studies of PIN/PILS of ginseng are still scarce.In current study,identification and transcriptional profiling of PIN/PILS gene families,as well as their potential relationship with ginsenoside biosynthesis in Panax ginseng were investigated.Methods:PIN/PILS genes in P.ginseng was identified via in silico genome-wide analysis,followed by phylogenetic relationships,gene structure,and protein profiles investigation.Moreover,previously reported RNA-sequence data from various tissues and roots after infection were utilized for PIN/PILS genes expression pattern analysis.The Pearson’s correlation analysis of specific PIN/PILS genes expression level and main ginsenoside contents were taken to reveal the potential relationship between auxin transports and ginsenoside biosynthesis in P.ginseng.Results:A genome-wide search of P.ginseng genome for homologous auxin transporter genes identified a total of 17 PIN and 11 PILS genes.Sequence alignment,putative motif organization,and sub-cellular localization indicated redundant and complementary biological functions of these PIN/PILS genes.Most PIN/PILS genes were differentially expressed in a tissue-specific manner,and showed significant correlations with ginsenoside content correspondingly.Eight auxin transporter genes,including both PIN and PILS subfamily members,were positively correlated with ginsenoside content(cor>0.60;P-value<0.05).The expression levels of eleven auxin transporter genes were increased dramatically in the early stage(0–0.5 DPI)after Cylindrocarpon destructans infection,accompanied with various overall expression patterns,implying the dynamic auxin transport in response to biotic stress.Conclusion:Based on the results,we speculate that the accumulation or depletion in temporal or spatial manner of auxin by PIN/PILS transporters involved in the regulation of HMGR activity and subsequent ginsenoside biosynthesis.

水系可充锌电池中H_(0.642)V_(2)O_(5)·0.143H_(2)O正极的高循环稳定性机制研究

水系可充电池中过渡金属氧化物宿主材料的循环稳定性是影响其长时间服役性能的关键.本文通过简单的预嵌入方法,将一定量的质子和水分子预嵌入到V_(2)O_(5)晶格中,重构了晶体结构,获得了高性能水系锌电池中的H_(0.642)V_(2)O_(5)·0.143H_(2)O(HVO)层状正极材料.得益于该结构重构,钒氧化物正极循环过程中的“层状/非晶”结构演化过程被抑制,由此获得极高循环稳定性(在0.5 A g^(-1)电流密度下循环500圈几乎无衰减).此外,该研究报道了HVO正极中质子和锌离子协同嵌入的储能机制,为下一代高性能钒基正极材料的设计提供了一种新的理念.

Constructing low-valent Ni nanoparticles for highly selective CO_(2) reduction

The electroreduction of CO_(2)(CO_(2)RR)into value-added chemicals is a sustainable strategy for mitigating global warming and managing the global carbon balance.However,developing an efficient and selective catalyst is still the central challenge.Here,we developed a simple two-step pyrolysis method to confine low-valent Ni-based nanoparticles within nitrogen-doped carbon(Ni-NC).As a result,such Ni-based nanoparticles can effectively reduce CO_(2)to CO,with a maximum CO Faradaic efficiency(FE)of 98%at an overpotential of 0.8 V,as long as good stability.Experimental and the density functional theory(DFT)calculation results reveal that low-valent Ni plays a key role in activity and selectivity enhancement.This study presents a new understanding of Ni-based CO_(2)RR,and provides a simple,scalable approach to the synthesis of low-valent catalysts towards efficient CO_(2)RR.

Detection and recognition of veterinary drug residues in beef using hyperspectral discrete wavelet transform and deep learning

A fast,non-destructive recognition method for veterinary drug residues in beef was proposed to mitigate the laborious sample preparation and long detection times associated with conventional chemical detection techniques.Control beef samples free of veterinary drug residues and four groups of beef sprayed with relevant concentrations of metronidazole,ofloxacin,salbutamol,and dexamethasone under ambient conditions were analyzed by 400-1000 nm hyperspectral imaging followed by multiplicative scatter correction preprocessing.Data dimension reduction was performed using Competitive Adaptive Reweighted Sampling(CARS),Principal Component Analysis(PCA),and Discrete Wavelet Transform(DWT)based on Haar,db3,bior1.5,sym5,and rbio1.3 wavelet basis functions.Treated data were subjected to Convolutional Neural Network(CNN),Multilayer Perceptron(MLP),Random Forest(RF),and Support Vector Machine(SVM)modelling.CNN,MLP,SVM,and RF algorithms achieved overall accuracies of 91.6%,88.6%,87.6%,and 86.2%,respectively,when combined with DWT(wavelet basis functions and numbers of transform layers being Haar-4,db3-2,bior1.5-4,and sym5-3,respectively).The algorithm Kappa coefficients(0.89,0.86,0.85,and 0.83,respectively)and time consumption for prediction(140.60 ms,57.85 ms,70.67 ms,and 87.16 ms,respectively)were also superior to models based on CARS and PCA.DWT combined with deep learning can shorten prediction times,considerably improve the accuracy of classification and recognition,and alleviate the Hughes phenomenon,thus providing a new method for the fast,non-destructive detection and recognition of veterinary drug residues in beef.