Enhancing m^(6)A modification in the motor cortex facilitates corticospinal tract remodeling after spinal cord injury

Spinal cord injury typically causes corticospinal tract disruption. Although the disrupted corticospinal tract can self-regenerate to a certain degree, the underlying mechanism of this process is still unclear. N6-methyladenosine(m^(6)A) modifications are the most common form of epigenetic regulation at the RNA level and play an essential role in biological processes. However, whether m^(6)A modifications participate in corticospinal tract regeneration after spinal cord injury remains unknown. We found that expression of methyltransferase 14 protein(METTL14) in the locomotor cortex was high after spinal cord injury and accompanied by elevated m^(6)A levels. Knockdown of Mettl14 in the locomotor cortex was not favorable for corticospinal tract regeneration and neurological recovery after spinal cord injury. Through bioinformatics analysis and methylated RNA immunoprecipitation-quantitative polymerase chain reaction, we found that METTL14 regulated Trib2 expression in an m^(6)A-regulated manner, thereby activating the mitogen-activated protein kinase pathway and promoting corticospinal tract regeneration. Finally, we administered syringin, a stabilizer of METTL14, using molecular docking. Results confirmed that syringin can promote corticospinal tract regeneration and facilitate neurological recovery by stabilizing METTL14. Findings from this study reveal that m^(6)A modification is involved in the regulation of corticospinal tract regeneration after spinal cord injury.

Succinylation modification:a potential therapeutic target in stroke

Stroke is a leading cause of mortality and disability worldwide.Ischemic cell death triggered by the compromised supply of blood oxygen and glucose is one of the major pathophysiology of strokeinduced brain injury.Impaired mitochondrial energy metabolism is observed minutes after stroke and is closely associated with the progression of neuropathology.Recently,a new type of posttranslational modification,known as lysine succinylation,has been recognized to play a significant role in mitochondrial energy metabolism after ischemia.However,the role of succinylation modification in cell metabolism after stroke and its regulation are not well understood.We aimed to review the effects of succinylation on energy metabolism,reactive oxygen species generation,and neuroinflammation,as well as Sirtuin 5 mediated desuccinylation after stroke.We also highlight the potential of targeting succinylation/desuccinylation as a promising strategy for the treatment of stroke.The succinylation level is dynamically regulated by the nonenzymatic or enzymatic transfer of a succinyl group to a protein on lysine residues and the removal of succinyl catalyzed by desuccinylases.Mounting evidence has suggested that succinylation can regulate the metabolic pathway through modulating the activity or stability of metabolic enzymes.Sirtuins,especially Sirtuin 5,are characterized for their desuccinylation activity and have been recognized as a critical regulator of metabolism through desuccinylating numerous metabolic enzymes.Imbalance between succinylation and desuccinylation has been implicated in the pathophysiology of stroke.Pharmacological agents that enhance the activity of Sirtuin 5 have been employed to promote desuccinylation and improve mitochondrial metabolism,and neuroprotective effects of these agents have been observed in experimental stroke studies.However,their therapeutic efficacy in stroke patients should be validated.

Dysregulation of RNA modification systems in clinical populations with neurocognitive disorders

The study of modified RNA known as epitranscriptomics has become increasingly relevant in our understanding of disease-modifying mechanisms.Methylation of N6 adenosine(m^(6)A)and C5 cytosine(m^(5)C)bases occur on mRNAs,tRNA,mt-tRNA,and rRNA species as well as non-coding RNAs.With emerging knowledge of RNA binding proteins that act as writer,reader,and eraser effector proteins,comes a new understanding of physiological processes controlled by these systems.Such processes when spatiotemporally disrupted within cellular nanodomains in highly specialized tissues such as the brain,give rise to different forms of disease.In this review,we discuss accumulating evidence that changes in the m^(6)A and m^(5)C methylation systems contribute to neurocognitive disorders.Early studies first identified mutations within FMR1 to cause intellectual disability Fragile X syndromes several years before FMR1 was identified as an m^(6)A RNA reader protein.Subsequently,familial mutations within the m^(6)A writer gene METTL5,m^(5)C writer genes NSUN2,NSUN3,NSUN5,and NSUN6,as well as THOC2 and THOC6 that form a protein complex with the m^(5)C reader protein ALYREF,were recognized to cause intellectual development disorders.Similarly,differences in expression of the m^(5)C writer and reader effector proteins,NSUN6,NSUN7,and ALYREF in brain tissue are indicated in individuals with Alzheimer's disease,individuals with a high neuropathological load or have suffered traumatic brain injury.Likewise,an abundance of m^(6)A reader and anti-reader proteins are reported to change across brain regions in Lewy bodies diseases,Alzheimer's disease,and individuals with high cognitive reserve.m^(6)A-modified RNAs are also reported significantly more abundant in dementia with Lewy bodies brain tissue but significantly reduced in Parkinson's disease tissue,whilst modified RNAs are misplaced within diseased cells,particularly where synapses are located.In parahippocampal brain tissue,m^(6)A modification is enriched in transcripts associated with psychiatric disorders including conditions with clear cognitive deficits.These findings indicate a diverse set of molecular mechanisms are influenced by RNA methylation systems that can cause neuronal and synaptic dysfunction underlying neurocognitive disorders.Targeting these RNA modification systems brings new prospects for neural regenerative therapies.

Comprehensive analysis of the gut microbiome and posttranslational modifications elucidates the route involved in microbiota-host interactions

The gut microbiome interacts with the host to maintain body homeostasis,with gut microbial dysbiosis implicated in many diseases.However,the underlying mechanisms of gut microbe regulation of host behavior and brain functions remain unclear.This study aimed to elucidate the influence of gut microbiota on brain functions via post-translational modification mechanisms in the presence or absence of bacteria without any stimulation.We conducted succinylome analysis of hippocampal proteins in germ-free(GF)and specific pathogen-free(SPF)mice and metagenomic analysis of feces from SPF mice.These results were integrated with previously reported hippocampal acetylome and phosphorylome data from the same batch of mice.Subsequent bioinformatics analyses revealed 584 succinylation sites on 455 proteins,including 54 up-regulated succinylation sites on 91 proteins and 99 down-regulated sites on 51 proteins in the GF mice compared to the SPF mice.We constructed a panoramic map of gut microbiota-regulated succinylation,acetylation,and phosphorylation,and identified cross-talk and relative independence between the different types of post-translational modifications in modulating complicated intracellular pathways.Pearson correlation analysis indicated that 13 taxa,predominantly belonging to the Bacteroidetes phylum,were correlated with the biological functions of post-translational modifications.Positive correlations between these taxa and succinylation and negative correlations between these taxa and acetylation were identified in the modulation of intracellular pathways.This study highlights the hippocampal physiological changes induced by the absence of gut microbiota,and proteomic quantification of succinylation,phosphorylation,and acetylation,contributing to our understanding of the role of the gut microbiome in brain function and behavioral phenotypes.

Shifting Rule Modification Strategy of Automatic Transmission Based on Driver-vehicle-road Environment

Accidental or frequent shift often occurs when the shifting rule is built based on traditional two parameters （i.e., velocity and throttle）, because the speed of engine varies slower than change of throttle opening. Currently, modifying shift point velocity value or throttle by throttle change rate is one of common methods, but the results are not so satisfactory in some working condition such as uphill. The reason is that these methods merely consider throttle change rate which is not enough for a car driving in driver-vehicle-road environment system. So a novel fuzzy control modification strategy is proposed to avoid or reduce those abnormal shift actions. It can adjust shifting rule by the change rate of throttle, current gear position and road environment information, while different gear position and driving environment get corresponding modification value. In order to compare the results of shifting actions, fuel consumption and braking distance, emergent braking in level road and extra-urban driving cycle（EUDC） working conditions with fuzzy shifting schedule modification strategy are simulated digitally. Furthermore, a hardware-in-the-loop simulation platform is introduced to verify its effect in slope road condition according to the ON/OFF numbers of solenoid valve in hydraulic system. The simulation results show that the problem of unexpected shift in those working conditions may be resolved by fuzzy modification strategy. At last, it is concluded that although there is some slight decline in power performance in uphill situation, this fuzzy modification strategy could correctly identify slope of road, decrease braking distance, improve vehicle comfort and fuel economy effectively and prolong the life of clutch system. So, this fuzzy logic shifting strategy provides important references for vehicle intelligent shifting schedule.

Towards advanced zinc anodes by interfacial modification strategies for efficient aqueous zinc metal batteries

Developing sustainable and clean energy sources(e.g.,solar,wind,and tide energy)is essential to achieve the goal of carbon neutrality.Due to the discontinuous and inco nsistent nature of common clean energy sources,high-performance energy storage technologies are a critical part of achieving this target.Aqueous zinc metal batteries(AZMBs)with inherent safety,low cost,and competitive performance are regarded as one of the promising candidates for grid-scale energy storage.However,zinc metal anodes(ZMAs)with irreversible problems of dendrite growth,hydrogen evolution reaction,self-corrosio n,and other side reactions have seriously hindered the development and commercialization of AZMBs.An increasing number of researchers are focusing on the stability of ZMAs,so assessing the effectiveness of existing research strategies is critical to the development of AZMBs.This review aims to provide a comprehensive overview of the fundamentals and challenges of AZMBs.Resea rch strategies for interfacial modification of ZMAs are systematically presented.The features of artificial interfacial coating and in-situ interfacial coating of ZMAs are compared and discussed in detail,as well as the effect of modified interfacial ZMA on the full-battery performance.Finally,perspectives are provided on the problems and challenges of ZMAs.This review is expected to offer a constructive reference for the further development and commercialization of AZMBs.

The Even-Odd Rule on Single Covalent-Bonded Structural Formulas as a Modification of Classical Structural Formulas of Multiple-Bonded Ions and Molecules

In organic chemistry, as defined by Abegg, Kossel, Lewis and Langmuir, compounds are normally represented using structural formulas called Lewis structures. In these structures, the octet rule is used to define the number of covalent bonds that each atom forms with its neighbors and multiple bonds are frequent. Lewis’ octet rule has unfortunately shown limitations very early when applied to non-organic compounds: most of them remain incompatible with the “rule of eight” and location of charges is uncertain. In an attempt to unify structural formulas of octet and non-octet molecules or single-charge ions, an even-odd rule was recently proposed, together with a procedure to locate charge precisely. This even-odd rule has introduced a charge-dependent effective-valence number calculated for each atom. With this number and the number of covalent bonds of each element, two even numbers are calculated. These numbers are both used to understand and draw structuralformulas of single-covalent-bonded compounds. In the present paper, a procedure is proposed to adjust structural formulas of compounds that are commonly represented with multiple bonds. In order to keep them compatible with the even-odd rule, they will be represented using only single covalent bonds. The procedure will then describe the consequences of bond simplification on charges locations. The newly obtained representations are compared to their conventional structural formulas, i.e. single-bond representation vs. multiple-bond structures. Throughout the comparison process, charges are precisely located and assigned to specific atoms. After discussion of particular cases of compounds, the paper finally concludes that a rule limiting representations of multiplecovalent bonds to single covalent bonds, seems to be suitable for numerous known compounds.

Semantic Consistency and Correctness Verification of Digital Traffic Rules

The consensus of the automotive industry and traffic management authorities is that autonomous vehicles must follow the same traffic laws as human drivers.Using formal or digital methods,natural language traffic rules can be translated into machine language and used by autonomous vehicles.In this paper,a translation flow is designed.Beyond the translation,a deeper examination is required,because the semantics of natural languages are rich and complex,and frequently contain hidden assumptions.The issue of how to ensure that digital rules are accurate and consistent with the original intent of the traffic rules they represent is both significant and unresolved.In response,we propose a method of formal verification that combines equivalence verification with model checking.Reasonable and reassuring digital traffic rules can be obtained by utilizing the proposed traffic rule digitization flow and verification method.In addition,we offer a number of simulation applications that employ digital traffic rules to assess vehicle violations.The experimental findings indicate that our digital rules utilizing metric temporal logic(MTL)can be easily incorporated into simulation platforms and autonomous driving systems(ADS).

A concise review on surface and structural modification of porous zeolite scaffold for enhanced hydrogen storage

Investigating zeolites as hydrogen storage scaffolds is imperative due to their porous nature and favorable physicochemical properties.Nevertheless,the storage capacity of the unmodified zeolites has been rather unsatisfactory(0.224%-1.082%(mass))compared to its modified counterpart.Thus,the contemporary focus on enhancing hydrogen storage capacities has led to significant attention towards the utilization of modified zeolites,with studies exploring surface modifications through physical and chemical treatments,as well as the integration of various active metals.The enhanced hydrogen storage properties of zeolites are attributed to the presence of aluminosilicates from alkaline and alkaline-earth metals,resulting in increased storage capacity through interactions with the charge density of these aluminosilicates.Therefore,there is a great demand to critically review their role such as well-defined topology,pore structure,good thermal stability,and tunable hydrophilicity in enhanced hydrogen storage.This article aimed to critically review the recent research findings based on modified zeolite performance for enhanced hydrogen storage.Some of the factors affecting the hydrogen storage capacities of zeolites that can affect the rate of reaction and the stability of the adsorbent,like pressure,structure,and morphology were studied,and examined.Then,future perspectives,recommendations,and directions for modified zeolites were discussed.

Study on the Medication Rules of Traditional Chinese Medicine Prescriptions for the Treatment of New Crown Pneumonia Based on Data Mining Technology

[Objectives]This study was conducted to analyze the medication rules of clinical prescriptions of traditional Chinese medicine decoction pieces for the treatment of novel coronavirus pneumonia(COVID-19)during the epidemic in multiple regions based on data mining technology,so as to provide a reference for the treatment of COVID-19 with traditional Chinese medicine.[Methods]The traditional Chinese medicine prescriptions used since the outbreak of COVID-19 in Hubei Province during the fight against the epidemic from February 25,2020 to February 14,2022,the traditional Chinese medicine prescriptions used by Guizhou traditional Chinese medicine expert team aiding Hubei Province,the traditional Chinese medicine prescriptions for rehabilitation and conditioning of patients in Ezhou of Hubei Province after discharge,the traditional Chinese medicine prescriptions for the prevention and treatment of COVID-19 in Guizhou Province,and the traditional Chinese medicine prescriptions for the treatment of COVID-19 collected from the end of 2019 to the present from the Chinese database of CNKI were collected as the data of this study.Excel was used to establish a database and enter it into the TCM inheritance calculation platform V3.5,and the association rules and k-means clustering algorithm were used to analyze the frequency of herbal medicines in prescriptions during the treatment of COVID-19,the frequency of four natures,five flavors,meridian distribution,and drug combinations.[Results]A total of 1859 COVID-19 patients treated with traditional Chinese medicine were included,and the proportion of males was higher than that of females,and middle-aged and elderly people were the most common group.A total of 2170 prescriptions of traditional Chinese medicine were included,involving a total of 383 traditional Chinese medicines.High-frequency medicines included poria,Radix Bupleuri,Radix Scutellariae,Herba Pogostemonis,Fructus Forsythiae,Flos Loniceraeetc.The four natures were mainly concentrated in cold,warm and neutral,and the five flavors were mainly concentrated in bitter,pungent and sweet.The herbal medicines were mainly attributed to the lungs and stomach meridians,and were mainly of heat-clearing,exterior syndrome-relieving and diuresis-promoting and damp-clearing types.A total of 24 high-frequency herbal combinations and 35 association rule were excavated,and 3 types of formulas were obtained by cluster analysis.[Conclusions]The analysis results and medicine combinations obtained in the formulas are consistent with the traditional Chinese medicine treatment theory of COVID-19 caused by wind-heat filth accompanied with damp and toxin.

Density Clustering Algorithm Based on KD-Tree and Voting Rules

Traditional clustering algorithms often struggle to produce satisfactory results when dealing with datasets withuneven density. Additionally, they incur substantial computational costs when applied to high-dimensional datadue to calculating similarity matrices. To alleviate these issues, we employ the KD-Tree to partition the dataset andcompute the K-nearest neighbors (KNN) density for each point, thereby avoiding the computation of similaritymatrices. Moreover, we apply the rules of voting elections, treating each data point as a voter and casting a votefor the point with the highest density among its KNN. By utilizing the vote counts of each point, we develop thestrategy for classifying noise points and potential cluster centers, allowing the algorithm to identify clusters withuneven density and complex shapes. Additionally, we define the concept of “adhesive points” between two clustersto merge adjacent clusters that have similar densities. This process helps us identify the optimal number of clustersautomatically. Experimental results indicate that our algorithm not only improves the efficiency of clustering butalso increases its accuracy.

Emerging perovskite materials for supercapacitors:Structure,synthesis,modification,advanced characterization,theoretical calculation and electrochemical performance

As a new generation electrode materials for energy storage,perovskites have attracted wide attention because of their unique crystal structure,reversible active sites,rich oxygen vacancies,and good stability.In this review,the design and engineering progress of perovskite materials for supercapacitors(SCs)in recent years is summarized.Specifically,the review will focus on four types of perovskites,perovskite oxides,halide perovskites,fluoride perovskites,and multi-perovskites,within the context of their intrinsic structure and corresponding electrochemical performance.A series of experimental variables,such as synthesis,crystal structure,and electrochemical reaction mechanism,will be carefully analyzed by combining various advanced characterization techniques and theoretical calculations.The applications of these materials as electrodes are then featured for various SCs.Finally,we look forward to the prospects and challenges of perovskite-type SCs electrodes,as well as the future research direction.

Improved STNModels and Heuristic Rules for Cooperative Scheduling in Automated Container Terminals

Improving the cooperative scheduling efficiency of equipment is the key for automated container terminals to copewith the development trend of large-scale ships. In order to improve the solution efficiency of the existing spacetimenetwork (STN) model for the cooperative scheduling problem of yard cranes (YCs) and automated guidedvehicles (AGVs) and extend its application scenarios, two improved STN models are proposed. The flow balanceconstraints in the original model are decomposed, and the trajectory constraints of YCs and AGVs are added toacquire the model STN_A. The coupling constraint in STN_A is updated, and buffer constraints are added toSTN_A so that themodel STN_B is built.As the size of the problem increases, the solution speed of CPLEX becomesthe bottleneck. So a heuristic method containing three groups of heuristic rules is designed to obtain a near-optimalsolution quickly. Experimental results showthat the computation time of STN_A is shortened by 49.47% on averageand the gap is reduced by 1.69% on average compared with the original model. The gap between the solution ofthe heuristic rules and the solution of CPLEX is less than 3.50%, and the solution time of the heuristic rules is onaverage 99.85% less than the solution time of CPLEX. Compared with STN_A, the computation time for solvingSTN_B increases by 58.93% on average.

Influence of laser parameters on the microstructures and surface properties in laser surface modification of biomedical magnesium alloys

Biodegradable implants from magnesium(Mg)alloys have emerged in the biomedical field especially in the orthopedic and cardiovascular stent applications owing to their low density,high specific strength,excellent machinability,good biocompatibility,and biodegradability.The primary shortcoming of Mg-based implants is their low corrosion resistance in the physiological environment,which results in premature mechanical integrity loss before adequate healing and the production of excessive hydrogen gas,which is harmful to the body tissues and negatively affects the biocompatibility of the implant.Laser surface modification has recently received attention because it can improve the surface properties such as surface chemistry,roughness,topography,corrosion resistance,wear resistance,hydrophilicity,and thus cell response to the surface of the material.The composition and microstructures including textures and phases of laser-treated surfaces depend largely on the laser processing parameters(input laser power,laser scan velocity,frequency,pulse duration,pressure,gas circulation,working time,spot size,beam focal position,and laser track overlap)and the thermophysical properties of the substrate(solubility,melting point,and boiling point).This review investigates the impacts of various laser surface modification techniques including laser surface melting,laser surface alloying,laser cladding,laser surface texturing,and laser shock peening,and highlights their significance in improving the surface properties of biodegradable Mg alloys for implant applications.Additionally,we explore how different laser process parameters affect its composition,microstructure,and surface properties in each laser surface modification technique.

Effect of surface modification on the radiation stability of diamond ohmic contacts

The ohmic contact interface between diamond and metal is essential for the application of diamond detectors.Surface modification can significantly affect the contact performance and eliminate the interface polarization effect.However,the radiation stability of a diamond detector is also sensitive to surface modification.In this work,the influence of surface modification technology on a diamond ohmic contact under high-energy radiation was investigated.Before radiation,the specific contact resistivities(ρc)between Ti/Pt/Au-hydrogen-terminated diamond(H-diamond)and Ti/Pt/Au-oxygenterminated diamond(O-diamond)were 2.0×10^(-4)W·cm^(2) and 4.3×10^(-3)Wcm^(2),respectively.After 10 MeV electron radiation,the ρc of Ti/Pt/Au H-diamond and Ti/Pt/Au O-diamond were 5.3×10^(-3)W·cm^(2)and 9.1×10^(-3)W·cm^(2),respectively.The rates of change of ρc of H-diamond and O-diamond after radiation were 2550%and 112%,respectively.The electron radiation promotes bond reconstruction of the diamond surface,resulting in an increase in ρc.

Coupling effect of cement-stabilization and biopolymer-modification on the mechanical behavior of dredged sediment

Nowadays,biopolymer stabilization as a promising eco-friendly approach in soft ground improvement has attracted wide attentions.However,the feasibility of using biopolymer as a green additive of cementstabilized dredged sediment(CDS)with high water content is still unknown.In this study,guar gum(GG)and xanthan gum(XG)were adopted as typical biopolymers,and a series of unconfined compressive strength(UCS),splitting tensile strength(STS)and scanning electron microscopy(SEM)tests were performed to evaluate the mechanical and microstructural properties of XG-and GG-modified CDSs considering several factors including biopolymer modification,binderesoil ratio and wateresolid ratio.Furthermore,the micro-mechanisms revealing the evolutions of mechanical properties of biopolymermodified CDS were analyzed.The results indicate that the addition of XG can effectively improve the strength of CDS,while the GG has a side effect.The XG content of 9%was recommended,which can improve the 7 d-and 28 d-UCSs by 196%and 51.8%,together with the 7 d-and 28 d-STSs by 118.3%and 42.2%,respectively.Increasing the binderesoil ratio or decreasing the wateresolid ratio significantly improved the strength gaining but aggravated the brittleness characteristics of CDS.Adding XG to CDS contributed to the formation of microstructure with more compactness and higher cementation degrees of ordinary Portland cement(OPC)-XG-stabilized DS(CXDS).The micro-mechanism models revealing the interactions of multiple media including OPC cementation,biopolymer film bonding and bridging effects inside CXDS were proposed.The key findings confirm the feasibility of XG modification as a green and high-efficiency mean for improving the mechanical properties of CDS.

Learning Vector Quantization-Based Fuzzy Rules Oversampling Method

Imbalanced datasets are common in practical applications,and oversampling methods using fuzzy rules have been shown to enhance the classification performance of imbalanced data by taking into account the relationship between data attributes.However,the creation of fuzzy rules typically depends on expert knowledge,which may not fully leverage the label information in training data and may be subjective.To address this issue,a novel fuzzy rule oversampling approach is developed based on the learning vector quantization(LVQ)algorithm.In this method,the label information of the training data is utilized to determine the antecedent part of If-Then fuzzy rules by dynamically dividing attribute intervals using LVQ.Subsequently,fuzzy rules are generated and adjusted to calculate rule weights.The number of new samples to be synthesized for each rule is then computed,and samples from the minority class are synthesized based on the newly generated fuzzy rules.This results in the establishment of a fuzzy rule oversampling method based on LVQ.To evaluate the effectiveness of this method,comparative experiments are conducted on 12 publicly available imbalance datasets with five other sampling techniques in combination with the support function machine.The experimental results demonstrate that the proposed method can significantly enhance the classification algorithm across seven performance indicators,including a boost of 2.15%to 12.34%in Accuracy,6.11%to 27.06%in G-mean,and 4.69%to 18.78%in AUC.These show that the proposed method is capable of more efficiently improving the classification performance of imbalanced data.

Improved Efficiency and Stability of Organic Solar Cells by Interface Modification Using Atomic Layer Deposition of Ultrathin Aluminum Oxide

The interfacial contacts between the electron transporting layers(ETLs)and the photoactive layers are crucial to device performance and stability for OSCs with inverted architecture.Herein,atomic layer deposition(ALD)fabricated ultrathin Al_(2)O_(3)layers are applied to modify the ETLs/active blends(PM6:BTP-BO-4F)interfaces of OSCs,thus improving device performance.The ALD-Al_(2)O_(3)thin layers on ZnO significantly improved its surface morphology,which led to the decreased work function of ZnO and reduced recombination losses in devices.The simultaneous increase in open-circuit voltage(V_(OC)),short-circuit current density(J_(SC))and fill factor(FF)were achieved for the OSCs incorporated with ALD-Al_(2)O_(3)interlayers of a certain thickness,which produced a maximum PCE of 16.61%.Moreover,the ALD-Al_(2)O_(3)interlayers had significantly enhanced device stability by suppressing degradation of the photoactive layers induced by the photocatalytic activity of ZnO and passivating surface defects of ZnO that may play the role of active sites for the adsorption of oxygen and moisture.

A Facile Li_(2)TiO_(3) Surface Modification to Improve the Structure Stability and Electrochemical Performance of Full Concentration Gradient Li-Rich Oxides

Full concentration gradient lithium-rich layered oxides are catching lots of interest as the next generation cathode for lithium-ion batteries due to their high discharge voltage,reduced voltage decay and enhanced rate performance,whereas the high lithium residues on its surface impairs the structure stability and long-term cycle performance.Herein,a facile multifunctional surface modification method is implemented to eliminate surface lithium residues of full concentration gradient lithium-rich layered oxides by a wet chemistry reaction with tetrabutyl titanate and the post-annealing process.It realizes not only a stable Li_(2)TiO_(3)coating layer with 3D diffusion channels for fast Li^(+)ions transfer,but also dopes partial Ti^(4+)ions into the sub-surface region of full concentration gradient lithium-rich layered oxides to further strengthen its crystal structure.Consequently,the modified full concentration gradient lithium-rich layered oxides exhibit improved structure stability,elevated thermal stability with decomposition temperature from 289.57℃to 321.72℃,and enhanced cycle performance(205.1 mAh g^(-1)after 150 cycles)with slowed voltage drop(1.67 mV per cycle).This work proposes a facile and integrated modification method to enhance the comprehensive performance of full concentration gradient lithium-rich layered oxides,which can facilitate its practical application for developing higher energy density lithium-ion batteries.

Effect of Modification Treatment on Chloride Ions Permeability and Microstructure of Recycled Brick-mixed Aggregate Concrete

The modification methods of pozzolan slurry combined with sodium silicate and silicon-based additive were respectively adopted to treat recycled coarse brick-mixed aggregate(RCBA)in this study.The compressive strength and chloride permeability resistance of recycled aggregate concrete(RAC)before and after modification treatment were tested,and the microstructure of RAC was analyzed by mercury intrusion porosimetry(MIP)and scanning electron microscopy(SEM).The results show that the physical properties of RCBA strengthened by modification treatment are improved,and the compressive strength and chloride permeability resistance of treated RAC are also significantly improved.The modification treatment optimizes the pore size distribution of RAC,which increases the number of gel pores and transition pores,and decreases the number of capillary pores and macro pores.The surface fractal dimension shows a significant correlation with chloride diffusion coefficient,indicating that the variation of chloride permeability of treated RAC is consistent with the microstructure evolution.