Cation effects in electrocatalytic reduction reactions:Recent advances

Electrocatalytic reduction reactions,powered by clean energy sources such as solar energy and wind,offer a sustainable method for converting inexpensive feedstocks(e.g.,CO_(2),N_(2)/NO_(x),organics,and O_(2))into high-value-added chemicals or fuels.The design and modification of electrocatalysts have been widely implemented to improve their performance in these reactions.However,bottle-necks are encountered,making it challenging to further improve performance through catalyst development alone.Recently,cations in the electrolyte have emerged as critical factors for tuning both the activity and product selectivity of reduction reactions.This review summarizes recent advances in understanding the role of cation effects in electrocatalytic reduction reactions.First,we introduce the mechanisms underlying cation effects.We then provide a comprehensive overview of their application in electroreduction reactions.Characterization techniques and theoretical calcula-tion methods for studying cation effects are also discussed.Finally,we address remaining challeng-es and future perspectives in this field.We hope that this review offers fundamental insights and design guidance for utilizing cation effects,thereby advancing their development.

Enabling an Inorganic-Rich Interface via Cationic Surfactant for High-Performance Lithium Metal Batteries

An anion-rich electric double layer(EDL)region is favorable for fabricating an inorganic-rich solid-electrolyte interphase(SEI)towards stable lithium metal anode in ester electrolyte.Herein,cetyltrimethylammonium bromide(CTAB),a cationic surfactant,is adopted to draw more anions into EDL by ionic interactions that shield the repelling force on anions during lithium plating.In situ electrochemical surface-enhanced Raman spectroscopy results combined with molecular dynamics simulations validate the enrichment of NO_(3)^(−)/FSI−anions in the EDL region due to the positively charged CTA^(+).In-depth analysis of SEI structure by X-ray photoelectron spectroscopy and time-of-flight secondary ion mass spectrometry results confirmed the formation of the inorganic-rich SEI,which helps improve the kinetics of Li^(+)transfer,lower the charge transfer activation energy,and homogenize Li deposition.As a result,the Li||Li symmetric cell in the designed electrolyte displays a prolongated cycling time from 500 to 1300 h compared to that in the blank electrolyte at 0.5 mA cm^(-2) with a capacity of 1 mAh cm^(-2).Moreover,Li||LiFePO_(4) and Li||LiCoO_(2) with a high cathode mass loading of>10 mg cm^(-2) can be stably cycled over 180 cycles.

Label-free in-vivo classi-cation and tracking of red blood cells and platelets using Dynamic-YOLOv4 network

In-vivo flow cytometry is a noninvasive real-time diagnostic technique that facilitates continuous monitoring of cells without perturbing their natural biological environment,which renders it a valuable tool for both scienti¯c research and clinical applications.However,the conventional approach for improving classi¯cation accuracy often involves labeling cells with fluorescence,which can lead to potential phototoxicity.This study proposes a label-free in-vivo flow cytometry technique,called dynamic YOLOv4(D-YOLOv4),which improves classi¯cation accuracy by integrating absorption intensity°uctuation modulation(AIFM)into YOLOv4 to demodulate the temporal features of moving red blood cells(RBCs)and platelets.Using zebra¯sh as an experimental model,the D-YOLOv4 method achieved average precisions(APs)of 0.90 for RBCs and 0.64 for thrombocytes(similar to platelets in mammals),resulting in an overall AP of 0.77.These scores notably surpass those attained by alternative network models,thereby demonstrating that the combination of physical models with neural networks provides an innovative approach toward developing label-free in-vivoflow cytometry,which holds promise for diverse in-vivo cell classi¯cation applications.

Waste acid recovery utilizing monovalent cation permselective membranes through selective electrodialysis

Selective electrodialysis(SED)has surfaced as a highly promising membrane separation technique in the realm of acid recovery owing to its ability to effectively separate monovalent ions through the utilization of a potential difference.However,the current SED process is limited by conventional commercial monovalent cation permselective membranes(MCPMs).This study systematically investigates the use of an independently developed MCPM in the SED process for acid recovery.Various factors such as current density,volume ratio,initial ion concentration,and waste acid systems are considered.The independently developed MCPM offers several advantages over the commercial monovalent selective cation-exchange membrane(CIMS),including higher recovered acid concentration,better ion flux ratio,improved acid recovery efficiency,increased recovered acid purity,and higher current efficiency.The SED process with the MCPM achieves a recovered acid of 95.9%and a concentration of 2.3 mol·L^(–1) in the HCl/FeCl_(2) system,when a current density of 20 mA·cm^(-2) and a volume ratio of 1:2 are applied.Similarly,in the H_(2)SO_(4)/FeSO_(4) system,a purity of over 99%and a concentration of 2.1 mol·L^(–1) can be achieved in the recovered acid.This study thoroughly examines the impact of operation conditions on acid recovery performance in the SED process.The independently developed MCPM demonstrates outstanding acid recovery performance,highlighting its potential for future commercial utilization.

Fabrication and Properties of a New Reactive Diluent for Cationic UV Curing

The reactive diluent prepared by siloxane modified Trimethylene oxide can improve the performance of the UV curing system.Therefore,1,7-bis[(3-ethyl-3-methoxyoxacylobutane)propyl]octadecylosiloxane(BEMOPOMTS)was synthesized from diethyl carbonate,trimethylopropanes,allyl bromide,and 1,1,3,3,5,5,7,7-octadecylosiloxane as the main raw materials.BEMOPOMTS can be used as reactive diluents in the field of cationic UV curing.It has good thermal stability,and the addition of BEMOPOMTS significantly improves the tensile strength and elongation at break of epoxy resin.Compared with the pure epoxy resin,adding 20%BEMOPOMTS increased the elastic modulus by 25%to 677 MPa.

Exploring the Cation Regulation Mechanism for Interfacial Water Involved in the Hydrogen Evolution Reaction by In Situ Raman Spectroscopy

Interfacial water molecules are the most important participants in the hydrogen evolution reaction(HER).Hence,understanding the behavior and role that interfacial water plays will ultimately reveal the HER mechanism.Unfortunately,investigating interfacial water is extremely challenging owing to the interference caused by bulk water molecules and complexity of the interfacial environment.Here,the behaviors of interfacial water in different cationic electrolytes on Pd surfaces were investigated by the electrochemistry,in situ core-shell nanostructure enhanced Raman spectroscopy and theoretical simulation techniques.Direct spectral evidence reveals a red shift in the frequency and a decrease in the intensity of interfacial water as the potential is shifted in the positively direction.When comparing the different cation electrolyte systems at a given potential,the frequency of the interfacial water peak increases in the specified order:Li+<Na^(+)<K^(+)<Ca^(2+)<Sr^(2+).The structure of interfacial water was optimized by adjusting the radius,valence,and concentration of cation to form the two-H down structure.This unique interfacial water structure will improve the charge transfer efficiency between the water and electrode further enhancing the HER performance.Therefore,local cation tuning strategies can be used to improve the HER performance by optimizing the interfacial water structure.

Cationic ordering transition in oxygen-redox layered oxide cathodes

Understanding the structural origin of the competition between oxygen 2p and transition-metal 3d orbitals in oxygen-redox(OR)layered oxides is eminently desirable for exploring reversible and high-energy-density Li/Na-ion cathodes.Here,we reveal the correlation between cationic ordering transition and OR degradation in ribbon-ordered P3-Na_(0.6)Li_(0.2)Mn_(0.8)O_(2) via in situ structural analysis.Comparing two different voltage windows,the OR capacity can be improved approximately twofold when suppressing the in-plane cationic ordering transition.We find that the intralayer cationic migration is promoted by electrochemical reduction from Mn^(4+)to Jahn–Teller Mn^(3+)and the concomitant NaO_(6) stacking transformation from triangular prisms to octahedra,resulting in the loss of ribbon ordering and electrochemical decay.First-principles calculations reveal that Mn^(4+)/Mn^(3+)charge ordering and alignment of the degenerate eg orbital induce lattice-level collective Jahn–Teller distortion,which favors intralayer Mn-ion migration and thereby accelerates OR degradation.These findings unravel the relationship between in-plane cationic ordering and OR reversibility and highlight the importance of superstructure protection for the rational design of reversible OR-active layered oxide cathodes.

Enhancing selectivity in acidic CO_(2) electrolysis:Cation effects and catalyst innovation

The electrochemical reduction of CO_(2)(eCO_(2)R)under ambient conditions is crucial for reducing carbon emissions and achieving carbon neutrality.Despite progress with alkaline and neutral electrolytes,their efficiency is limited by(bi)carbonates formation.Acidic media have emerged as a solution,addressing the(bi)carbonates challenge but introducing the issue of the hydrogen evolu-tion reaction(HER),which reduces CO_(2) conversion efficiency in acidic environments.This review focuses on enhancing the selectivity of acidic CO_(2) electrolysis.It commences with an overview of the latest advancements in acidic CO_(2) electrolysis,focusing on product selectivity and electrocatalytic activity enhancements.It then delves into the critical factors shaping selectivity in acidic CO_(2) electrolysis,with a special emphasis on the influence of cations and catalyst design.Finally,the research challenges and personal perspectives of acidic CO_(2) electrolysis are suggested.

Atomically Adjustable Rhodium Catalyst Synthesis with Outstanding Mass Activity via Surface-Li mi ted Cation Exchange

Rh has been widely studied as a catalyst for the promising hydrazine oxidation reaction that can replace oxygen evolution reactions for boosting hydrogen production from hydrazine-containing wastewater.Despite Rh being expensive,only a few studies have examined its electrocatalytic mass activity.Herein,surface-limited cation exchange and electrochemical activation processes are designed to remarkably enhance the mass activity of Rh.Rh atoms were readily replaced at the Ni sites on the surface of NiOOH electrodes by cation exchange,and the resulting RhOOH compounds were activated by the electrochemical reduction process.The cation exchange-derived Rh catalysts exhibited particle sizes not exceeding 2 nm without agglomeration,indicating a decrease in the number of inactive inner Rh atoms.Consequently,an improved mass activity of 30 A mg_(Rh)^(-1)was achieved at 0.4 V versus reversible hydrogen electrode.Furthermore,the two-electrode system employing the same CE-derived Rh electrodes achieved overall hydrazine splitting over 36 h at a stable low voltage.The proposed surface-limited CE process is an effective method for reducing inactive atoms of expensive noble metal catalysts.

Identification of Mulberry Bacterial Blight Caused by Klebsiella oxytoca in Bazhong,Sichuan,China

To provide a scientific basis for controlling mulberry bacterial blight in Bazhong,Sichuan,China(BSC),this study aimed to isolate and purify pathogenic bacteria from diseased branches of mulberry trees in the region and to clarify their taxonomic status using morphological observation,physiological and biochemical detection,molecular-level identification,and the construction of a phylogenetic tree.A total of 218 bacterial strains were isolated from samples of diseased mulberry branches.Of these,7 strains were identified as pathogenic bacteria based on pathogenicity tests conducted in accordance with Koch’s postulates.Preliminary findings from the analysis of the 16S rRNA sequence indicated that the 7 pathogenic bacteria are members of Klebsiella spp.Morphological observation revealed that the pathogenic bacteria were oval-shaped and had capsules but no spores.They could secrete pectinase,cellulase,and protease and were able to utilize D-glucose,D-mannose,D-maltose,and D-Cellobiose.The 7 strains of pathogenic bacteria exhibited the highest homology with Klebsiella oxytoca.This study identifies Klebsiella oxytoca as the causative agent of mulberry bacterial blight in BSC,laying the foundation for the prevention and control of this pathogen and further investigation into its pathogenic mechanism.

Application of Transgenic Technology in Identification for Gene Function on Grasses

Perennial grasses have developed intricate mechanisms to adapt to diverse environments,enabling their resistance to various biotic and abiotic stressors.These mechanisms arise from strong natural selection that contributes to enhancing the adaptation of forage plants to various stress conditions.Methods such as antisense RNA technology,CRISPR/Cas9 screening,virus-induced gene silencing,and transgenic technology,are commonly utilized for investigating the stress response functionalities of grass genes in both warm-season and cool-season varieties.This review focuses on the functional identification of stress-resistance genes and regulatory elements in grasses.It synthesizes recent studies on mining functional genes,regulatory genes,and protein kinase-like signaling factors involved in stress responses in grasses.Additionally,the review outlines future research directions,providing theoretical support and references for further exploration of(i)molecular mechanisms underlying grass stress responses,(ii)cultivation and domestication of herbage,(iii)development of high-yield varieties resistant to stress,and(iv)mechanisms and breeding strategies for stress resistance in grasses.

Person-Dependent Handwriting Verification for Special Education Using DeepLearning

Individuals with special needs learn more slowly than their peers and they need repetitions to be permanent.However,in crowded classrooms,it is dif-ficult for a teacher to deal with each student individually.This problem can be overcome by using supportive education applications.However,the majority of such applications are not designed for special education and therefore they are not efficient as expected.Special education students differ from their peers in terms of their development,characteristics,and educational qualifications.The handwriting skills of individuals with special needs are lower than their peers.This makes the task of Handwriting Recognition(HWR)more difficult.To over-come this problem,we propose a new personalized handwriting verification sys-tem that validates digits from the handwriting of special education students.The system uses a Convolutional Neural Network(CNN)created and trained from scratch.The data set used is obtained by collecting the handwriting of the students with the help of a tablet.A special education center is visited and the handwrittenfigures of the students are collected under the supervision of special education tea-chers.The system is designed as a person-dependent system as every student has their writing style.Overall,the system achieves promising results,reaching a recognition accuracy of about 94%.Overall,the system can verify special educa-tion students’handwriting digits with high accuracy and is ready to integrate with a mobile application that is designed to teach digits to special education students.

Third-Order Nonlinear Optical Responses of Bis(15-crown-5)-stilbenes Binding to One-or Two-Alkali Metal Cation(Li^(+),Na^(+)and K^(+))

Bis(15-crown-5)-stilbenes containing crown ether parts have been widely used in a variety of chemical applications,such as cation detectors,because of their ability to selectively bind to alkali metal cations,Bis(15-crown-5)-stilbenes and its derivatives with complexation of one-or two-alkali metal cation(Li^(+),Na^(+)and K^(+))have been theoretically investigat-ed by quantum chemistry methods.The coordination of alkali cations results in partial shrinkage of crown ethers,which directly affected natural distribution analysis charges and molecular orbital energy levels.The number of alkali metal ions has significant effects on absorption spectra and mean second hyperpolarizability.When one alkali metal ion was added to the anticonformer of bis(15-crown-5)-stilbene,the absorption spectra were obvious-ly redshifted and the mean second hyperpolarizability values were slightly increased;while two alkali metal ions were added to bis(15-crown-5)-stilbene,the absorption spectra were ob-viously blue shifted and the mean second hyperpolarizability values decreased.On the other hand,as the radius of the alkali ions increased,the mean second hyperpolarizability values of the compounds increased gradually.It is indicated that the mean second hyperpolarizability value is sensitive to the number and radius of the alkali metal cations,thus the third order nonlinear optical response can be used as a signal to detect the number and type of alkali met-al ions.

Misclassification analysis of discriminant model

This paper extends the criterion of the misclassification ratio of discriminant model and presents a new selection method of discriminant model.For selecting the discriminant model,this method establishes the rule of misclassification degree ratio through misclassification ratio of the discriminant model and misclassification degree of the samples.To test the effect of this method,this work uses seven UCI data sets.Numerical experiments on these examples indicate that this method has certain rationality and has a better effect to select a discriminant model.

An Ordinal Multi-Dimensional Classification(OMDC)for Predictive Maintenance

Predictive Maintenance is a type of condition-based maintenance that assesses the equipment's states and estimates its failure probability and when maintenance should be performed.Although machine learning techniques have been frequently implemented in this area,the existing studies disregard to the nat-ural order between the target attribute values of the historical sensor data.Thus,these methods cause losing the inherent order of the data that positively affects the prediction performances.To deal with this problem,a novel approach,named Ordinal Multi-dimensional Classification(OMDC),is proposed for estimating the conditions of a hydraulic system's four components by taking into the natural order of class values.To demonstrate the prediction ability of the proposed approach,eleven different multi-dimensional classification algorithms(traditional Binary Relevance(BR),Classifier Chain(CC),Bayesian Classifier Chain(BCC),Monte Carlo Classifier Chain(MCC),Probabilistic Classifier Chain(PCC),Clas-sifier Dependency Network(CDN),Classifier Trellis(CT),Classifier Dependency Trellis(CDT),Label Powerset(LP),Pruned Sets(PS),and Random k-Labelsets(RAKEL))were implemented using the Ordinal Class Classifier(OCC)algorithm.Besides,seven different classification algorithms(Multilayer Perceptron(MLP),Support Vector Machine(SVM),k-Nearest Neighbour(kNN),Decision Tree(C4.5),Bagging,Random Forest(RF),and Adaptive Boosting(AdaBoost))were chosen as base learners for the OCC algorithm.The experimental results present that the proposed OMDC approach using binary relevance multi-dimensional classification methods predicts the conditions of a hydraulic system's multiple components with high accuracy.Also,it is clearly seen from the results that the OMDC models that utilize ensemble-based classification algorithms give more reliable prediction performances with an average Hamming score of 0.853 than the others that use traditional algorithms as base learners.

A novel cationic collector for silicon removal from collophane using reverse flotation under acidic conditions

We analyzed a novel cationic collector using chemical plant byproducts,such as cetyltrimethylammonium bromide(CTAB)and dibutyl phthalate(DBP).Our aim is to establish a highly effective and economical process for the removal of quartz from collophane.A microflotation test with a 25 mg·L^(−1)collector at pH value of 6-10 demonstrates a considerable difference in the floatability of pure quartz and fluorapatite.Flotation tests for a collophane sample subjected to the first reverse flotation for magnesium removal demonstrates that a rough flotation process(using a 0.4 kg·t−1 new collector at pH=6)results in a collophane concentrate with 29.33wt%P_(2)O_(5)grade and 12.66wt%SiO2 at a 79.69wt%P_(2)O_(5)recovery,providing desirable results.Mechanism studies using Fourier transform infrared spectroscopy,zeta potential,and contact angle measurements show that the adsorption capacity of the new collector for quartz is higher than that for fluorapatite.The synergistic effect of DBP increases the difference in hydrophobicity between quartz and fluorapatite.The maximum defoaming rate of the novel cationic collector reaches 142.8 mL·min−1.This is considerably higher than that of a conventional cationic collector.

SF-CNN: Deep Text Classification and Retrieval for Text Documents

Researchers and scientists need rapid access to text documents such as research papers,source code and dissertations.Many research documents are available on the Internet and need more time to retrieve exact documents based on keywords.An efficient classification algorithm for retrieving documents based on keyword words is required.The traditional algorithm performs less because it never considers words’polysemy and the relationship between bag-of-words in keywords.To solve the above problem,Semantic Featured Convolution Neural Networks(SF-CNN)is proposed to obtain the key relationships among the searching keywords and build a structure for matching the words for retrieving correct text documents.The proposed SF-CNN is based on deep semantic-based bag-of-word representation for document retrieval.Traditional deep learning methods such as Convolutional Neural Network and Recurrent Neural Network never use semantic representation for bag-of-words.The experiment is performed with different document datasets for evaluating the performance of the proposed SF-CNN method.SF-CNN classifies the documents with an accuracy of 94%than the traditional algorithms.

Addressing cation mixing in layered structured cathodes for lithium-ion batteries:A critical review

High-performance lithium-ion batteries(LIB)are important in powering emerging technologies.Cathodes are regarded as the bottleneck of increasing battery energy density,among which layered oxides are the most promising candidates for LIB.However,a limitation with layered oxides cathodes is the transition metal and Li site mixing,which significantly impacts battery capacity and cycling stability.Despite recent research on Li/Ni mixing,there is a lack of comprehensive understanding of the origin of cation mixing between the transition metal and Li;therefore,practical means to address it.Here,a critical review of cation mixing in layered cathodes has been provided,emphasising the understanding of cation mixing mechanisms and their impact on cathode material design.We list and compare advanced characterisation techniques to detect cation mixing in the material structure;examine methods to regulate the degree of cation mixing in layered oxides to boost battery capacity and cycling performance,and critically assess how these can be applied practically.An appraisal of future research directions,including superexchange interaction to stabilise structures and boost capacity retention has also been concluded.Findings will be of immediate benefit in the design of layered cathodes for high-performance rechargeable LIB and,therefore,of interest to researchers and manufacturers.

Disaster Monitoring of Satellite Image Processing Using Progressive Image Classification

The analysis of remote sensing image areas is needed for climate detec-tion and management,especially for monitoringflood disasters in critical environ-ments and applications.Satellites are mostly used to detect disasters on Earth,and they have advantages in capturing Earth images.Using the control technique,Earth images can be used to obtain detailed terrain information.Since the acquisi-tion of satellite and aerial imagery,this system has been able to detectfloods,and with increasing convenience,flood detection has become more desirable in the last few years.In this paper,a Big Data Set-based Progressive Image Classification Algorithm(PICA)system is introduced to implement an image processing tech-nique,detect disasters,and determine results with the help of the PICA,which allows disaster analysis to be extracted more effectively.The PICA is essential to overcoming strong shadows,for proper access to disaster characteristics to false positives by operators,and to false predictions that affect the impact of the disas-ter.The PICA creates tailoring and adjustments obtained from satellite images before training and post-disaster aerial image data patches.Two types of proposed PICA systems detect disasters faster and more accurately(95.6%).

One-step crosslinking preparation of tannic acid particles for the adsorption and separation of cationic dyes

In this study,a new tannic acid adsorbent(ethylene glycol diglycidyl ether crosslinked tannic acid,TAEGDE)for adsorptive removal of dyes from water was prepared using EGDE as a cross-linking agent.The resultant TA-EGDE was in particulate form with rough surface morphology and a diameter ranging from 10 to 30μm.The adsorption performance of the TA-EGDE was evaluated in a flow-through mode using water samples contaminated with methylene blue(MB)and two-component mixed dyes,respectively.The TA-EGDE provided adsorption capacity up to 721.8 mg·g^(-1)at 65°C for MB.It showed a high removal efficiency(99%)of MB(50 mg·L^(-1))from the water sample and could recovery 90%of the adsorbed MB by eluting with acidic ethanol aqueous solution.The excellent adsorption of MB and neutral red on the TA-EGDE may be the result of the synergy of electrostatic interaction andπ-πinteraction.Furthermore,the TA-EGDE could separate dyes from water samples contaminated with twocomponent mixed dyes with a separation coefficient ranging from 1.8 to 36.5.The anionic TA-EGDE would be an effective adsorbent to remove and recycle dyes from the contaminated water.