Real-time visualization of Zn metal plating/stripping in aqueous batteries with high areal capacities

Zinc aqueous batteries have attracted great attention due to the earth abundance and the low redox potential of Zn metal. Utilizing Zn metal as an anode, however, causes low coulombic efficiency stemming from a dendritic Zn plating and formation of byproducts such as hydrogen gas, solid zinc hydroxide and salt-related compounds. One effective way of mitigating the issues is to modify the solvation structure of the electrolyte to increase the energy barrier of the water molecules for hydrolysis and electrolysis. Nevertheless, Zn aqueous batteries still indiscriminately utilize several types of electrolytes without elucidating the correlation between electrolyte composition and the electrochemistry of Zn metal. Here, we use operando optical microscopy to visualize the microstructural evolution of Zn metal, which strongly affects the electrochemical reversibility. In ZnSO4 electrolyte, large Zn platelets grow and form loose agglomerates vulnerable to unexpected delamination from the electrodes. In Zn(OTf)(2) electrolyte, Zn platelets nucleate more homogeneously and grow smaller, which forms denser agglomerates enabling more stable cycling. We further reveal that the formation of a stable solidelectrolyte interphase layer holds the key to the excellent performance of acetonitrile-hybrid water-in-salt electrolytes. Our results show the necessity of designing proper electrolytes to develop long-life Zn aqueous batteries.

EXIT: Extrapolation and Interpolation-based Neural Controlled Differential Equations for Time-series Classification and Forecasting

Deep learning inspired by differential equations is a recent research trend and has marked the state of the art performance for many machine learning tasks. Among them, time-series modeling with neural controlled differential equations (NCDEs) is considered as a breakthrough. In many cases, NCDE-based models not only provide better accuracy than recurrent neural networks (RNNs) but also make it possible to process irregular time-series. In this work, we enhance NCDEs by redesigning their core part, i.e., generating a continuous path from a discrete time-series input. NCDEs typically use interpolation algorithms to convert discrete time-series samples to continuous paths. However, we propose to i) generate another latent continuous path using an encoder-decoder architecture, which corresponds to the interpolation process of NCDEs, i.e., our neural network-based interpolation vs. the existing explicit interpolation, and ii) exploit the generative characteristic of the decoder, i.e., extrapolation beyond the time domain of original data if needed. Therefore, our NCDE design can use both the interpolated and the extrapolated information for downstream machine learning tasks. In our experiments with 5 real-world datasets and 12 baselines, our extrapolation and interpolation-based NCDEs outperform existing baselines by non-trivial margins.Comment: main 8 page

The Action Research on the Development of the Sport-Based Character Education Program

In a situation where school violence among early adolescents is very serious, such as the recent mass assault of Busan middle school girls, the development of a character education program that takes into account the unique characteristics of adolescents due to confusion in their self-identity and values is highly requested. Accordingly, in this study, we developed a sports-based practical character education model to help early adolescents naturally acquire character elements. To this end, scientific procedures for program development and According to the action research method, two modules were developed, a classroom program using sports and an integrated program, taking school conditions into account. The subjects of the study were selected through significant sampling according to the presence or absence of sports facilities among schools wishing to participate, and ultimately, two groups of first-year middle school students in the Seoul and Gyeonggi area, 24 and 28 students in total, 52 students in total, participated. Each group once a week from September 1 to November 10, 2016 It was operated in 90-minute block times, and the classroom program was implemented for 4 weeks and the integrated program was implemented for 10 weeks. To verify the effectiveness of the program, repeated measures analysis of variance was conducted to verify the differences between groups and the average difference between pre- and post-tests, and the satisfaction results of study participants were analyzed. As a result, this program was effective in personal and social character competencies, and there was no significant difference between the classroom and integrated groups, showing that both programs were effective. In addition, in terms of intrapersonal gender competency, There was a positive effect in increasing communication and community competencies in self-identity, self-management, and social personality competencies, but there was no significant difference in vision and order competencies. This study was developed as a classroom-type, integrated character education module that can be flexibly applied in school settings and is significant as a program that allows students to naturally experience character competencies. Lastly, the limitations of this study and suggestions for follow-up research were made