Micropore engineering on hollow nanospheres for ultra-stable sodium-selenium batteries

Attracted by high energy density and considerable conductivity of selenium(Se),Na-Se batteries have been deemed promising energy-storage systems.But,it still suffers from sluggish kinetic behaviors and similar“shuttling effect”to S-electrodes.Herein,utilizing uniform hollow carbon spheres as precursors,Se-material is effectively loaded through vapor-infiltration method.Owing to the distribution of optimized pores,the content of microspores could be up to~60%(<2 nm),serving important roles for the physical confinement effect.Meanwhile,the rich oxygen-containing groups and N-elements could be noted,inducing the evolution of electron-moving behaviors.More significantly,assisted by the interfacial C-Se bonds and tiny Se distributions,Se electrodes are activated during cycling.Used as cathodes for Na-Se systems,the as-resulted samples display a capacity of 593.9 mA h g^(-1)after 100 cycles at the current density of 0.1 C.Even after 6000 cycles,the capacity could be still kept at about 225 mA h g^(-1)at 5.0 C.Supported by the detailed kinetic analysis,the designed microspores size induces the increasing redox reaction of nano Se,whilst the surface traits further render the enhancement of pseudo-capacitive contributions.Moreover,after cycling,the product Sex(x<4)in pores serves as the primary active material.Given this,the work is anticipated to provide an effective strategy for advanced electrodes for Na-Se systems.

Active Anomaly Detection Technology Based on Ensemble Learning

Anomaly detection is an important problem in various research and application fields.Researchers design reliable schemes to provide solutions for effectively detecting anomaly points.Most of the existing anomaly detection schemes are unsupervised methods,such as anomaly detection methods based on density,distance and clustering.In total,unsupervised anomaly detection methods have many limitations.For example,they cannot be well combined with prior knowledge in some anomaly detection tasks.For some nonlinear anomaly detection tasks,the modeling is complex and faces dimensional disasters,which are greatly affected by noise.Sometimes it is difficult to find abnormal events that users are interested in,and users need to customize model parameters before detection.With the wide application of deep learning technology,it has a good modeling ability to solve linear and nonlinear data relationships,but the application of deep learning technology in the field of anomaly detection has many challenges.If we regard exceptions as a supervised problem,exceptions are a few,and we usually face the problem of too few labels.To obtain a model that performs well in the anomaly detection task,it requires a high initial training set.Therefore,to solve the above problems,this paper proposes a supervised learning method with manual participation.We introduce the integrated learning model and train a supervised anomaly detection model with strong stability and high accuracy through active learning technology.In addition,this paper adopts certain strategies to maximize the accuracy of anomaly detection and minimize the cost of manual labeling.In the experimental link,we will show that our method is better than some traditional anomaly detection algorithms.

CIRS:A Confidence Interval Radius Slope Method for Time Series Points Based on Unsupervised Learning

The rise of big data has brought various challenges and revolutions to many fields.Even though its development in many industries has gradually become perfect or even mature,its application and development in complex industrial scenarios is still in its infancy.We run research on single-dimensional time series point anomaly detection based on unsupervised learning:Unlike periodic time series,aperiodic or weakly periodic time series in industrial scenarios are more common.Considering the need for online real-time monitoring,we need to solve the problem of point anomaly detection of oil chromatographic characteristic gases.Thus,we propose a sliding window-based method for the unsupervised single-dimensional time series point anomaly detection problem called the confidence interval radius slope method(CIRS).CIRS is a fusion of knowledge-driven and data-driven methods to realize online real-time monitoring of possible data quality problems.From the experimental results,CIRS has obtained higher PR values than other unsupervised methods by the subject data.