Influence of Four Factors on Discharge Capacity and Self-Discharge Rate of Iron Electrode

Ni-Fe rechargeable batteries possess the advantages of long cycle life, high theoretical specific energy, abundant raw material, low price and environmental friendship. It has a wide applied perspective. The advantages, disadvantages and preparation methods of iron electrodes were summarized. The influence of four factors on discharge capacity and self-discharge rate of iron electrode were discussed by means of orthogonal experiments, galvanostatic charges and discharges. The influences of graphite on the discharge capacity and self-discharge rate of iron electrode were the most remarkable, the most unapparent influences on the discharge capacity and self-discharge rate were HPMC (hydroxy propoxy methoxy cellulose) and sodium sulphide, respectively. The aim of the present research was to study the effects of graphite, HPMC and iron powder added in the electrodes, sodium sulphide added in the electrolytes on the discharge capacity and self-discharge rate of iron electrodes. The largest discharge capacity of the iron electrodes was 488.5 mAh/g-Fe at 66.4 mA/g-Fe in the first ten cycles, and the average self-discharge rate was 0.367% per hour.

Recent research advances of self-discharge in supercapacitors:Mechanisms and suppressing strategies

Supercapacitors are one of the most promising energy storage devices in the fields of vehicle transportation,flexible electronic devices,aerospace,etc.However,the existed self-discharge that is the spontaneous voltage decay after supercapacitors are fully charged,brings about the wide gap between experimental studies and practical utilization of supercapacitors.Although eliminating the selfdischarge completely is not reachable,suppressing the self-discharge rate to the lowest point is possible and feasible.So far,the significant endeavors have been devoted to achieve this goal.Herein,we summary and discuss the possible mechanisms for the self-discharge and the underlying influence factors.Moreover,the strategies to suppress the self-discharge are systemically summed up by three independent but unified aspects:modifying the electrode,modulating the electrolyte and tuning the separator.Finally,the major challenges to suppress the self-discharge of supercapacitors are concluded and the promising strategies are also pointed out and discussed.This review is presented with the view of serving as a guideline to suppress the self-discharge of supercapacitors and to across-the-board facilitate their widespread application.

Mitigating self-discharge of carbon-based electrochemical capacitors by modifying their electric-double layer to maximize energy efficiency

Self-discharge is a significant issue in electric double layer energy storage, which leads to a rapid voltage drop and low energy efficiency. Here, we attempt to solve this problem by changing the structure of the electric double layer into a de-solvated state, by constructing a nano-scale and ion-conductive solid electrolyte layer on the surface of a carbon electrode. The ion concentration gradient and potential field that drive the self-discharge are greatly restricted inside this electric double layer. Based on this understanding, a high-efficiency graphene-based lithium ion capacitor was built up, in which the self-discharge rate is reduced by 50% and the energy efficiency is doubled. The capacitor also has a high energy density, high power output and long life, and shows promise for practical applications.

Correlation between self-discharge behavior and heteroatoms over doped carbon sheets for enhanced pseudocapacitance

For electric double layer supercapacitors,carbon materials originating from the purely physical energystorage mechanism limit the improvement in the capabilities of charge storage.To solve this problem,doping heteroatoms into carbon skeleton is a promising&charming strategy for enhancing electrochemical performance by providing the extra pseudocapacitance.However,the self-discharge behavior of such heteroatom-doped supercapacitors has been a challenging issue for a long time.Here,the porous carbon nanosheets with a tunable total content of heteroatoms are chosen as a demo to systemically decouple the correlation between the total content of heteroatoms and the specific capacitance as well as the self-discharge behavior.The capacitance changes in a range of 164–331 F g^(-1)@1 A g^(-1)with the increased total contents of doped heteroatom,strongly dependent on and sensitive to the total content of heteroatoms.The voltage retention rate and capacitance retention rate for the porous carbon nanosheets with a tunable total content of heteroatoms completely present a quick decline tendency as the increase in the content of heteroatoms,changing from 58%to 34%and 74%to 39%,respectively,indicative of a linear negative relationship.More importantly,the self-discharge mechanisms are elaborately explored and follow the combination of activation-and diffusion-controlled Faradic reactions.This work illustrates the diverse impacts of the doped heteroatoms on the electrochemical performance of supercapacitors,covering specific capacitance and self-discharge behavior,and highlights the importance of balancing the contents of doped heteroatoms in energy storage fields.

Suppression of Self-Discharge in Aqueous Supercapacitor Devices Incorporating Highly Polar Nanofiber Separators

One of the major problems limiting the applications of electric double-layer(EDLC)supercapacitor devices is their inability to maintain their cell voltage over a significant period.Self-discharge is a spontaneous decay in charged energy,often resulting in fully depleted devices in a matter of hours.Here,a new method for suppressing this self-discharge phenomenon is proposed by using directionally polarized piezoelectric electrospun nanofiber films as separator materials.Tailored engineering of polyvinylidene fluoride(PVDF)nanofiber films containing a small concentration of sodium dodecyl sulfate(SDS)results in a high proportion of polarβphases,reaching 380.5%of the total material.Inducing polarity into the separator material provides a reverse-diode mechanism in the device,such that it drops from an initial voltage of 1.6 down to 1 V after 10 h,as opposed to 0.3 V with a nonpolarized,commercial separator material.Thus,the energy retained for the polarized separator is 37%and 4%for the nonpolarized separator,making supercapacitors a more attractive solution for long-term energy storage.

Bipolar ionomer electrolytes with desirable self-discharge suppression for supercapacitors

Supercapacitors based on electric double layers are prone to serious self-discharge due to electrolyte ion desorption and the resulting energy loss severely limits the application range of supercapacitors.Rational design of polymer electrolyte systems to address this problem shows considerable generality and high feasibility.Herein,we reported a quasi-solid-state bipolar ionomer electrolyte prepared by an in-situ layer-by-layer ultraviolet-curing method,which has an integrated Janus structure with an intermediate binding layer.Based on the synergistic effect of confining impurity ions by ionizable groups and electrostatic repulsion to stabilize the electric double layers and superimposing synergies on both sides,the assembled device not only possesses ideal supercapacitor characteristics,but also exhibits an ultrahigh voltage retention of 71% after being left to stand for 100 h after being fully charged.Furthermore,through the quasi-in-situ energy dispersive X-ray spectroscopy linear scanning,the characteristics of ion diffusion in this ionomer electrolyte are revealed,suggesting its correlation with self-discharge behavior.

Self-Discharge in Valve-Regulated Sealed Lead-Acid Batteries

Factors that cause the self-discharge in valve-regulated sealed lead-acid batteries are discussed and measures to inhibit the self-discharge are put forward.

Regulating the synergy coefficient of composite materials for alleviating self-discharge of supercapacitors

Supercapacitor is an efficient energy storage device,yet its wider application is still limited by self-discharge.Currently,various composite materials have been reported to have improved inhibition on self-discharge,while the evaluation of the synergistic effect in composite materials is challenging.Herein,pairs of intercalation type pseudocapacitive niobium oxides are pre-lithiated and coupled to construct conjugatedly configured supercapacitors,within which the cathode and anode experience identical reaction environment with single type of charge carrier,thus providing ideal platform to quantify the synergistic effect of composite materials on the self-discharge process.By using titanium dioxide as the stabilizer,we have compared how the modes of forming composite would influence the selfdischarge performance of the active composite materials with similar ratio of the constituent materials.Specifically,core@shell Nb_(2)O_(5)@TiO_(2) composite using TiO_(2) as the shell shows significantly higher synergy coefficient(μ=0.61,defined as the value that evaluates the synergistic effect between composite materials,and can be quantified using the overall performance of the composite,performance of individual component as well as the ratio of the component.) than other control group samples,which corresponds to the highest retained energy of 63% at 100 h.This work is expected to provide a general method for quantifying the synergistic effect and guide the design of composite materials with specific mode of forming the composite.

Understanding the Coffee ring Effect on Self-discharge Behavior of Printed micro-Supercapacitors

Printed micro-supercapacitor exhibits its flexibility in geometry design and integration,showing unprecedented potential in powering the internet of things and portable devices.However,the printing process brings undesired processing defects(e.g.,coffee ring effect),resulting in severe self-discharge of the printed micro-supercapacitors.The impact of such problems on device performance is poorly understood,limiting further development of microsupercapacitors.Herein,by analyzing the self-discharge behavior of fully printed micro-supercapacitors,the severe self-discharge problem is accelerated by the ohmic leakage caused by the coffee ring effect on an ultrathin polymer electrolyte.Based on this understanding,the coffee ring effect was successfully eradicated by introducing graphene oxide in the polymer electrolyte,achieving a decline of 99%in the self-discharge rate.Moreover,the micro-supercapacitors with uniformly printed polymer electrolyte present 7.64 F cm^(-3)volumetric capacitance(14.37 mF cm^(-2)areal capacitance),exhibiting about 50%increase compared to the one without graphene oxide addition.This work provides a new insight to understand the relationship between processing defects and device performance,which will help improve the performance and promote the application of printed micro-supercapacitors.

Prediction model for corrosion rate of low-alloy steels under atmospheric conditions using machine learning algorithms

This work constructed a machine learning(ML)model to predict the atmospheric corrosion rate of low-alloy steels(LAS).The material properties of LAS,environmental factors,and exposure time were used as the input,while the corrosion rate as the output.6 dif-ferent ML algorithms were used to construct the proposed model.Through optimization and filtering,the eXtreme gradient boosting(XG-Boost)model exhibited good corrosion rate prediction accuracy.The features of material properties were then transformed into atomic and physical features using the proposed property transformation approach,and the dominant descriptors that affected the corrosion rate were filtered using the recursive feature elimination(RFE)as well as XGBoost methods.The established ML models exhibited better predic-tion performance and generalization ability via property transformation descriptors.In addition,the SHapley additive exPlanations(SHAP)method was applied to analyze the relationship between the descriptors and corrosion rate.The results showed that the property transformation model could effectively help with analyzing the corrosion behavior,thereby significantly improving the generalization ability of corrosion rate prediction models.

Pulse rate estimation based on facial videos:an evaluation and optimization of the classical methods using both self-constructed and public datasets

Pulse rate is one of the important characteristics of traditional Chinese medicine pulse diagnosis,and it is of great significance for determining the nature of cold and heat in diseases.The prediction of pulse rate based on facial video is an exciting research field for getting palpation information by observation diagnosis.However,most studies focus on optimizing the algorithm based on a small sample of participants without systematically investigating multiple influencing factors.A total of 209 participants and 2,435 facial videos,based on our self-constructed Multi-Scene Sign Dataset and the public datasets,were used to perform a multi-level and multi-factor comprehensive comparison.The effects of different datasets,blood volume pulse signal extraction algorithms,region of interests,time windows,color spaces,pulse rate calculation methods,and video recording scenes were analyzed.Furthermore,we proposed a blood volume pulse signal quality optimization strategy based on the inverse Fourier transform and an improvement strategy for pulse rate estimation based on signal-to-noise ratio threshold sliding.We found that the effects of video estimation of pulse rate in the Multi-Scene Sign Dataset and Pulse Rate Detection Dataset were better than in other datasets.Compared with Fast independent component analysis and Single Channel algorithms,chrominance-based method and plane-orthogonal-to-skin algorithms have a more vital anti-interference ability and higher robustness.The performances of the five-organs fusion area and the full-face area were better than that of single sub-regions,and the fewer motion artifacts and better lighting can improve the precision of pulse rate estimation.

Dynamic interactive bitwise meta-holography with ultra-high computational and display frame rates

Interactive holography offers unmatched levels of immersion and user engagement in the field of future display.Despite of the substantial progress has been made in dynamic meta-holography,the realization of real-time,highly smooth interactive holography remains a significant challenge due to the computational and display frame rate limitations.In this study,we introduced a dynamic interactive bitwise meta-holography with ultra-high computational and display frame rates.To our knowledge,this is the first reported practical dynamic interactive metasurface holographic system.We spa-tially divided the metasurface device into multiple distinct channels,each projecting a reconstructed sub-pattern.The switching states of these channels were mapped to bitwise operations on a set of bit values,which avoids complex holo-gram computations,enabling an ultra-high computational frame rate.Our approach achieves a computational frame rate of 800 kHz and a display frame rate of 23 kHz on a low-power Raspberry Pi computational platform.According to this methodology,we demonstrated an interactive dynamic holographic Tetris game system that allows interactive gameplay,color display,and on-the-fly hologram creation.Our technology presents an inspiration for advanced dynamic meta-holography,which is promising for a broad range of applications including advanced human-computer interaction,real-time 3D visualization,and next-generation virtual and augmented reality systems.

The Probability Density Function Related to Shallow Cumulus Entrainment Rate and Its Influencing Factors in a Large-Eddy Simulation

The process of entrainment-mixing between cumulus clouds and the ambient air is important for the development of cumulus clouds.Accurately obtaining the entrainment rate(λ)is particularly important for its parameterization within the overall cumulus parameterization scheme.In this study,an improved bulk-plume method is proposed by solving the equations of two conserved variables simultaneously to calculateλof cumulus clouds in a large-eddy simulation.The results demonstrate that the improved bulk-plume method is more reliable than the traditional bulk-plume method,becauseλ,as calculated from the improved method,falls within the range ofλvalues obtained from the traditional method using different conserved variables.The probability density functions ofλfor all data,different times,and different heights can be well-fitted by a log-normal distribution,which supports the assumed stochastic entrainment process in previous studies.Further analysis demonstrate that the relationship betweenλand the vertical velocity is better than other thermodynamic/dynamical properties;thus,the vertical velocity is recommended as the primary influencing factor for the parameterization ofλin the future.The results of this study enhance the theoretical understanding ofλand its influencing factors and shed new light on the development ofλparameterization.

Experimental study on the effect of unloading rate on gneiss rockburst

Rockburst are often encountered in tunnel construction due to the complex geological conditions.To study the influence of unloading rate on rockburst,gneiss rockburst experiments were conducted under three groups of unloading rates.A high-speed photography system and acoustic emission(AE)system were used to monitor the entire process of rockburst process in real-time.The results show that the intensity of gneiss rockburst decreases with decrease of unloading rate,which is manifested as the reduction of AE energy and fragments ejection velocity.The mechanisms are proposed to explain this effect:(i)The reduction of unloading rate changes the crack propagation mechanism in the process of rockburst.This makes the rockbursts change from the tensile failure mechanism at high unloading rate to the tension-shear mixed failure mechanism at low unloading rate,and more energy released in the form of shear crack propagation.Then,less strain energy is converted into kinetic energy of fragments ejection.(ii)Less plate cracking degree of gneiss has taken shape due to decrease of unloading rate,resulting in the destruction of rockburst incubation process.The enlightenments of reducing the unloading rate for the project are also described quantitatively.The rockburst magnitude is reduced from the medium magnitude at the unloading rate of 0.1 MPa/s to the slight magnitude at the unloading rate of 0.025 MPa/s,which was judged by the ejection velocity.

A new era of mutation rate analyses: Concepts and methods

The mutation rate is a pivotal biological characteristic,intricately governed by natural selection and historically garnering considerable attention.Recent advances in high-throughput sequencing and analytical methodologies have profoundly transformed our understanding in this domain,ushering in an unprecedented era of mutation rate research.This paper aims to provide a comprehensive overview of the key concepts and methodologies frequently employed in the study of mutation rates.It examines various types of mutations,explores the evolutionary dynamics and associated theories,and synthesizes both classical and contemporary hypotheses.Furthermore,this review comprehensively explores recent advances in understanding germline and somatic mutations in animals and offers an overview of experimental methodologies,mutational patterns,molecular mechanisms,and driving forces influencing variations in mutation rates across species and tissues.Finally,it proposes several potential research directions and pressing questions for future investigations.

Present-day Upper-crustal Strain Rate Field in Southeastern Tibet and its Geodynamic Implications:Constraints from GPS Measurements with ABIC Method

The Earth’s surface kinematics and deformation are fundamental to understanding crustal evolution.An effective research approach is to estimate regional motion field and deformation fields based on modern geodetic networks.If the discrete observed velocity field is obtained,the velocity related fields,such as dilatation rate and maximum shear strain rate,can be estimated by applying varied mathematical approaches.This study applied Akaike's Bayesian Information Criterion(ABIC)method to calculate strain rate fields constrained by GPS observations in the southeast Tibetan Plateau.Comparison with results derived from other three methods revealed that our ABIC-derived strain rate fields were more precise.The maximum shear strain rate highlighted the Xianshuihe–Xiaojiang fault system as the main boundary for the outward migration of material in southeastern Tibet,indicating rotation of eastern Tibet material around the eastern Himalaya rather than whole extrusion along a fixed channel.Additionally,distinct dilatation rate patterns in the northeast and southwest regions of the fault system were observed.The northeast region,represented by the Longmenshan area,exhibited negative dilatational anomalies;while the southwest region,represented by the Jinsha River area north of 29°N,displayed positive dilatational anomalies.This indicates compression in the former and extension in the latter.Combined with deep geophysical observations,we believe that the upper and lower crusts of the Jinsha River area north of 29°N are in an entire expanding state,probably caused by the escape-drag effect of material.The presence of a large,low-viscosity region south of 29°N may not enable the entire escape of the crust,but instead result in a differential escape of the lower crust faster than the upper crust.

Federated Learning Model for Auto Insurance Rate Setting Based on Tweedie Distribution

In the assessment of car insurance claims,the claim rate for car insurance presents a highly skewed probability distribution,which is typically modeled using Tweedie distribution.The traditional approach to obtaining the Tweedie regression model involves training on a centralized dataset,when the data is provided by multiple parties,training a privacy-preserving Tweedie regression model without exchanging raw data becomes a challenge.To address this issue,this study introduces a novel vertical federated learning-based Tweedie regression algorithm for multi-party auto insurance rate setting in data silos.The algorithm can keep sensitive data locally and uses privacy-preserving techniques to achieve intersection operations between the two parties holding the data.After determining which entities are shared,the participants train the model locally using the shared entity data to obtain the local generalized linear model intermediate parameters.The homomorphic encryption algorithms are introduced to interact with and update the model intermediate parameters to collaboratively complete the joint training of the car insurance rate-setting model.Performance tests on two publicly available datasets show that the proposed federated Tweedie regression algorithm can effectively generate Tweedie regression models that leverage the value of data fromboth partieswithout exchanging data.The assessment results of the scheme approach those of the Tweedie regressionmodel learned fromcentralized data,and outperformthe Tweedie regressionmodel learned independently by a single party.

A performance-based hybrid deep learning model for predicting TBM advance rate using Attention-ResNet-LSTM

The technology of tunnel boring machine(TBM)has been widely applied for underground construction worldwide;however,how to ensure the TBM tunneling process safe and efficient remains a major concern.Advance rate is a key parameter of TBM operation and reflects the TBM-ground interaction,for which a reliable prediction helps optimize the TBM performance.Here,we develop a hybrid neural network model,called Attention-ResNet-LSTM,for accurate prediction of the TBM advance rate.A database including geological properties and TBM operational parameters from the Yangtze River Natural Gas Pipeline Project is used to train and test this deep learning model.The evolutionary polynomial regression method is adopted to aid the selection of input parameters.The results of numerical exper-iments show that our Attention-ResNet-LSTM model outperforms other commonly-used intelligent models with a lower root mean square error and a lower mean absolute percentage error.Further,parametric analyses are conducted to explore the effects of the sequence length of historical data and the model architecture on the prediction accuracy.A correlation analysis between the input and output parameters is also implemented to provide guidance for adjusting relevant TBM operational parameters.The performance of our hybrid intelligent model is demonstrated in a case study of TBM tunneling through a complex ground with variable strata.Finally,data collected from the Baimang River Tunnel Project in Shenzhen of China are used to further test the generalization of our model.The results indicate that,compared to the conventional ResNet-LSTM model,our model has a better predictive capability for scenarios with unknown datasets due to its self-adaptive characteristic.

Construction of a Computational Scheme for the Fuzzy HIV/AIDS Epidemic Model with a Nonlinear Saturated Incidence Rate

This work aimed to construct an epidemic model with fuzzy parameters.Since the classical epidemic model doesnot elaborate on the successful interaction of susceptible and infective people,the constructed fuzzy epidemicmodel discusses the more detailed versions of the interactions between infective and susceptible people.Thenext-generation matrix approach is employed to find the reproduction number of a deterministic model.Thesensitivity analysis and local stability analysis of the systemare also provided.For solving the fuzzy epidemic model,a numerical scheme is constructed which consists of three time levels.The numerical scheme has an advantage overthe existing forward Euler scheme for determining the conditions of getting the positive solution.The establishedscheme also has an advantage over existing non-standard finite difference methods in terms of order of accuracy.The stability of the scheme for the considered fuzzy model is also provided.From the plotted results,it can beobserved that susceptible people decay by rising interaction parameters.

Machine learning-based comparison of factors influencing estimated glomerular filtration rate in Chinese women with or without nonalcoholic fatty liver

BACKGROUND The prevalence of non-alcoholic fatty liver(NAFLD)has increased recently.Subjects with NAFLD are known to have higher chance for renal function impairment.Many past studies used traditional multiple linear regression(MLR)to identify risk factors for decreased estimated glomerular filtration rate(eGFR).However,medical research is increasingly relying on emerging machine learning(Mach-L)methods.The present study enrolled healthy women to identify factors affecting eGFR in subjects with and without NAFLD(NAFLD+,NAFLD-)and to rank their importance.AIM To uses three different Mach-L methods to identify key impact factors for eGFR in healthy women with and without NAFLD.METHODS A total of 65535 healthy female study participants were enrolled from the Taiwan MJ cohort,accounting for 32 independent variables including demographic,biochemistry and lifestyle parameters(independent variables),while eGFR was used as the dependent variable.Aside from MLR,three Mach-L methods were applied,including stochastic gradient boosting,eXtreme gradient boosting and elastic net.Errors of estimation were used to define method accuracy,where smaller degree of error indicated better model performance.RESULTS Income,albumin,eGFR,High density lipoprotein-Cholesterol,phosphorus,forced expiratory volume in one second(FEV1),and sleep time were all lower in the NAFLD+group,while other factors were all significantly higher except for smoking area.Mach-L had lower estimation errors,thus outperforming MLR.In Model 1,age,uric acid(UA),FEV1,plasma calcium level(Ca),plasma albumin level(Alb)and T-bilirubin were the most important factors in the NAFLD+group,as opposed to age,UA,FEV1,Alb,lactic dehydrogenase(LDH)and Ca for the NAFLD-group.Given the importance percentage was much higher than the 2nd important factor,we built Model 2 by removing age.CONCLUSION The eGFR were lower in the NAFLD+group compared to the NAFLD-group,with age being was the most important impact factor in both groups of healthy Chinese women,followed by LDH,UA,FEV1 and Alb.However,for the NAFLD-group,TSH and SBP were the 5th and 6th most important factors,as opposed to Ca and BF in the NAFLD+group.