Tailoring the Size of Reduced Graphene Oxide Sheets to Fabricate Silicon Composite Anodes for Lithium-Ion Batteries.

The integration of a silicon (Si) anode into lithium-ion batteries (LIBs) holds great promise for energy storage, but challenges arise from unstable electrochemical reactions and volume changes during cycling. This study investigates the influence of reduced graphene oxide (rGO) size on the performance of rGO-protected Si composite (Si@rGO) anodes. Two sizes of graphene oxide (GO(L) and GO(S)) are used to synthesize Si@rGO composites with a core-shell structure by spray drying and thermal reduction. Electrochemical evaluations show the advantages of the Si@rGO(S) anode with improved cycle life and cycling efficiency over Si@rGO(L) and pure Si. The Si@rGO(S) anode facilitates the formation of a stable LiF-rich solid electrolyte interface (SEI) after cycling, ensuring enhanced capacity retention and swelling control. Rate capability tests also demonstrate the superior high-power performance of Si@rGO(S) with low and stable resistances in Si@rGO(S) over extended cycles. This study provides critical insights into the tailoring of graphene-protected Si composites, highlighting the critical role of rGO size in shaping structural and electrochemical properties. The Si material wrapped by graphene with an optimal lateral size of graphene emerges as a promising candidate for high-performance LIB anodes, thereby advancing electrochemical energy storage technologies.

Advancing Li-ion capacitors through dual wet chemical prelithiation.

Lithium-ion batteries (LIBs) and electrical double-layer capacitors (EDLCs) are widely used in commercial energy storage systems, but each has inherent limitations. To overcome these limitations, the lithium-ion capacitor (LIC) has emerged as a hybrid energy storage device, combining the benefits of LIBs and EDLCs. However, the introduction of active lithium into LICs poses challenges due to lithium's reactivity and instability. In this study, we propose a dual wet chemical prelithiation strategy to enhance LIC performance. By wet chemically prelithiating both the activated carbon cathodes and hard carbon anodes, significant improvements are achieved compared to traditional prelithiation methods. The dual prelithiation approach outperforms electrochemical prelithiation in terms of energy storage performance, cycle life, and process simplification. LICs with dual wet chemically prelithiated electrodes demonstrate the highest energy density and retain a substantial portion of reversible capacity even at high discharge rates. The strategy exhibits fast kinetics and wide operational stability. In contrast, LICs with metallic lithium anodes or electrochemically prelithiated hard carbon anodes exhibit inferior performance and limited cycle life. The dual wet chemical prelithiation strategy represents a breakthrough in LIC technology, offering superior performance, cycle stability, and scalability. It holds promise for alkali-ion energy storage systems and drives advancements in electrochemical energy storage technology.

Recent Configurational Advances for Solid-State Lithium Batteries Featuring Conversion-Type Cathodes.

Solid-state lithium metal batteries offer superior energy density, longer lifespan, and enhanced safety compared to traditional liquid-electrolyte batteries. Their development has the potential to revolutionize battery technology, including the creation of electric vehicles with extended ranges and smaller more efficient portable devices. The employment of metallic lithium as the negative electrode allows the use of Li-free positive electrode materials, expanding the range of cathode choices and increasing the diversity of solid-state battery design options. In this review, we present recent developments in the configuration of solid-state lithium batteries with conversion-type cathodes, which cannot be paired with conventional graphite or advanced silicon anodes due to the lack of active lithium. Recent advancements in electrode and cell configuration have resulted in significant improvements in solid-state batteries with chalcogen, chalcogenide, and halide cathodes, including improved energy density, better rate capability, longer cycle life, and other notable benefits. To fully leverage the benefits of lithium metal anodes in solid-state batteries, high-capacity conversion-type cathodes are necessary. While challenges remain in optimizing the interface between solid-state electrolytes and conversion-type cathodes, this area of research presents significant opportunities for the development of improved battery systems and will require continued efforts to overcome these challenges.

The impact of environmental knowledge and green supply chain practices in improving sustainable energy production: the moderating role of green behavior and green leadership.

Global warming and the dreadful climate condition in China demands the sustainable energy transition and production that must be far away from coal-based energy production. The present article, thereby, intends to assess the effectiveness of environmental knowledge and green supply chain practices on sustainable energy production. The study also introduces green behavior and green leadership as a moderator to evaluate the proposed relationship. Primary data has been collected and assessed by PLS-SEM. The findings reveal that environmental knowledge, green purchases, and internal environmental management (IEM) have a positive association with sustainable energy production (SEP) in China. The outcomes also indicate that green behavior significantly moderates among environmental knowledge, green purchases, and SEP, and green leadership significantly moderates among IEM and SEP in China. The research guides the policymakers in establishing policies related to SEP using green behavior, GSC practices, and environmental knowledge.

The moderating role of leadership on the relationship between green supply chain management, technological advancement, and knowledge management in sustainable performance.

The escalation of regulatory obligations in order to reshape and implement eco-friendly operations make it essential for firms to execute the related strategies and enhance their position in a competitive market. Scholars and practitioners, thereby, have aligned their search in the area of sustainability to get to know regarding activities that might improve the outcomes of business practices. To contribute in a particular area, the study attempts to explore the critical role of knowledge management, technological advancement, and green supply chain (SC) management on SBP in the presence of a moderator-leadership. China is used as a study sample, and the study model was evaluated through structural equation modelling. Statistical findings indicate that knowledge management, technological advancement, and green SC management have a positive influence on SBP in China. The findings reveal that leadership support significantly moderates knowledge management, technological advancement, green SC management, and SBP in China. These outcomes guide regulators in making regulations related to sustainable performance using the best practice of SC, technology advancement, and knowledge management.

Predicting Heavy Metal Concentrations in Shallow Aquifer Systems Based on Low-Cost Physiochemical Parameters Using Machine Learning Techniques.

Monitoring ex-situ water parameters, namely heavy metals, needs time and laboratory work for water sampling and analytical processes, which can retard the response to ongoing pollution events. Previous studies have successfully applied fast modeling techniques such as artificial intelligence algorithms to predict heavy metals. However, neither low-cost feature predictability nor explainability assessments have been considered in the modeling process. This study proposes a reliable and explainable framework to find an effective model and feature set to predict heavy metals in groundwater. The integrated assessment framework has four steps: model selection uncertainty, feature selection uncertainty, predictive uncertainty, and model interpretability. The results show that Random Forest is the most suitable model, and quick-measure parameters can be used as predictors for arsenic (As), iron (Fe), and manganese (Mn). Although the model performance is auspicious, it likely produces significant uncertainties. The findings also demonstrate that arsenic is related to nutrients and spatial distribution, while Fe and Mn are affected by spatial distribution and salinity. Some limitations and suggestions are also discussed to improve the prediction accuracy and interpretability.

A study of students' learning perceptions and behaviors in remote STEM programming education.

In recent years, STEM education has developed students' fundamental subject knowledge, and has allowed students to integrate STEM cross-domain knowledge. Universities emphasize innovative thinking, practice and application, problem-solving, and teamwork to nurture students while learning STEM cross-domain knowledge development in remote STEM program education. When students take online STEM programs, they may encounter unanswered questions and may give up on trying to solve them. Therefore, this study proposed a problem-based learning approach with an online programming system integrated into an online STEM programming course. To help students solve the past programming assignments, the assignments were simplified, decomposed, and reorganized. The teacher guided the students to understand the STEM programming problems and taught them to use appropriate problem-solving skills to motivate them to complete the STEM programming assignments. The experiment was conducted with students in an online STEM programming course at a university in northern Taiwan. In the experimental activities, we used a problem-based learning approach for the online STEM programming activity. The problem-based learning method can be divided into four steps, namely stating the problem, understanding the problem, developing a solution plan, and executing the plan, reflecting, and debugging. This study used a problem-based learning approach and an online programming system integrated into a STEM programming curriculum to explore the differences in students' perceptions of STEM learning, learning outcomes, and learning behaviors. The experimental results found a significant difference between students' prior knowledge and learning outcomes. Students showed significant gains in learning the STEM programming content using the problem-based learning approach and the online programming system. In the analysis of their STEM learning perceptions, we found that there were significant differences in students' responses for each dimension. This shows that using the problem-based learning approach with the online programming system helped students learn the course content. The analysis of students' behaviors in answering the STEM programming assignments indicated that some students had the habit of taking notes. This helped them to easily associate and integrate STEM cross-domain knowledge with what they had learned in the online course, and enhanced their ability to implement STEM programs. In addition, students could take the initiative and focus on repeatedly watching the teacher solve the material in the online course. Students could try different solving plans to pass the code validation of the STEM programming assignments. This revealed that students wanted to complete the STEM programming assignments to achieve good learning performance.

The effects of applying an augmented reality English teaching system on students' STEAM learning perceptions and technology acceptance.

With the inherently interdisciplinary and hands-on nature of the STEAM system, this study changes the way the English language is taught, no longer confining it to monotonous tests and dull textbooks. Teaching via Augmented Reality (AR) technology tends to pique students' interest, making them more willing to participate in learning. This study used AR technology in conjunction with gaming, where the participants were split into pairs, which promoted friendly competition among themselves, and, in turn, drove them to learn more. Moreover, the ability to track their own performance was added to the system, as well as an in-depth explanation for every question. The teacher used our teaching aids system in the lessons to make the teaching process more interesting and enjoyable. There were two English course experiments, each lasting for 4 weeks. The goal of the first experiment was to gain a general understanding of how students felt about the lessons and how well they could operate the system. After analyzing the results, some improvements for problems that arose were made in the second experiment. The goal of the second experiment was to find out how students felt about the lesson content and teaching aids and to compare the results of the two experiments. To help us find potential oversights and check how participants felt about this approach, surveys regarding STEAM learning and overall ease of use were administered. The results showed that AR English learning was widely considered helpful by the students, and also, our system was easy to operate, and competing with peers really motivated them to do better.

Smart Manufacturing Processes of Low-Tortuous Structures for High-Rate Electrochemical Energy Storage Devices.

To maximize the performance of energy storage systems more effectively, modern batteries/supercapacitors not only require high energy density but also need to be fully recharged within a short time or capable of high-power discharge for electric vehicles and power applications. Thus, how to improve the rate capability of batteries or supercapacitors is a very important direction of research and engineering. Making low-tortuous structures is an efficient means to boost power density without replacing materials or sacrificing energy density. In recent years, numerous manufacturing methods have been developed to prepare low-tortuous configurations for fast ion transportation, leading to impressive high-rate electrochemical performance. This review paper summarizes several smart manufacturing processes for making well-aligned 3D microstructures for batteries and supercapacitors. These techniques can also be adopted in other advanced fields that require sophisticated structural control to achieve superior properties.

A Literature Analysis of Consumer Privacy Protection in Augmented Reality Applications in Creative and Cultural Industries: A Text Mining Study.

Digital reality technologies (such as AR, VR, and MR) have recently become a key component of promoting creative and cultural industries (CCIs) worldwide to transform static cultural heritage exhibits into more engaging, entertaining, and immersive experiences. These technologies present an exciting example of studying how consumers would respond to the potential invasion of privacy due to these technologies. This literature review study mainly focuses on one essential branch of CCIs: museums and their applications of digital reality technologies. Because many of these location-based AR applications by museums are inherently sensitive to users' locational information, there is also a rising concern of the potential infringement of personal privacy (RQ1). A thorough examination of existing literature on how consumers respond to privacy concerns related to the museum's AR applications will help uncover how scholars have approached and studied these crucial issues in the literature (RQ2). Unlike traditional literature review analyses, we employed a text mining of retrieved 715 studies articles from Business Source Complete and Engineering Village (E.I.) databases to answer our two research questions. Our study found that privacy and user(s) /visitor(s) has dramatically increased since 2017, echoing the rising concerns of other privacy-invasive technologies. Most notably, key phrases extracted from the literature corpus include "security and privacy," "privacy and security," "privacy risks," "privacy concerns," "privacy issues," "user privacy," "location privacy," "privacy protection," and "privacy preserving" that are most pertinent to the rapid implementation of AR technology in the museum sector. Discussions and implications are provided.

The Impact of Teacher Competence in Online Teaching on Perceived Online Learning Outcomes during the COVID-19 Outbreak: A Moderated-Mediation Model of Teacher Resilience and Age.

During the COVID-19 pandemic, teachers had to conduct online classes because of the breakdown of school learning. Teacher competence has a great impact on the students' learning outcomes in online learning. Teacher resilience is also important to help teachers survive and achieve a high level of well-being in emergency situations. Previous studies have explored the protective and risk factors of teacher resilience, among which teacher competence in various aspects is included. In addition, teachers' age differences in competence and resilience have been the focus of past studies. However, few studies have investigated the impact of teacher competence on students' online learning outcomes, the mediating role of teacher resilience, and the moderating effect of age when teachers participate in emergent online teaching. To address the above gap, this study explored teachers' perceptions of students' online learning outcomes and how teacher competence in online teaching and resilience can predict these outcomes. The data of 159,203 participants were collected and subjected to correlation analyses and a moderated-mediation effect test. The results indicated that (1) teacher competence in online teaching was positively related to perceived online learning outcomes; (2) teacher resilience was positively related to the teachers' perceived online learning outcomes; (3) teacher resilience played a partial mediating role between teacher competence in online teaching and perceived online learning outcomes; and (4) teachers' age moderated the direct and indirect relation between teacher competence in online teaching and perceived online learning outcomes. The findings imply that teachers should strengthen their own teaching competence and their resilience before conducting online teaching. In addition, this study proposes intervention strategies to enhance teachers' resilience and well-being through teacher competence cultivation and provides suggestions for different age levels of teachers to develop and train their online teaching competence and resilience in the future.

Study of Virtual Reality Immersive Technology Enhanced Mathematics Geometry Learning.

Mathematics is an important foundation for the development of science education. In the past, when instructors taught mathematical concepts of geometry shapes, they usually used traditional textbooks and aids to conduct teaching activities, which resulted in students not being able to understand the principles completely. Nowadays, it has become a trend to integrate emerging technologies into mathematics courses and to use digital instructional aids. Emerging technologies can effectively enhance students' sensory experience while strengthening their impressions and understandings of subject concepts. In this paper, we apply virtual reality immersive technologies to develop a "virtual reality immersive learning mathematics geometry system," which is used to teach mathematical geometry concepts. Teachers use the system to develop three basic mathematical geometry learning materials: "Triangular pyramid volume = 1/3 prism volume," "Cone volume calculation," and "Triangle center of gravity derivation." In the experimental activity, the teacher uses virtual reality teaching aids to guide students to learn mathematical geometry concepts in a fun way so that they can achieve the effectiveness of immersive learning. This study explores the impact of using the virtual reality immersive learning mathematics geometry system on students' technology acceptance, learning motivations, and learning performance. The experimental result showed that using the virtual reality immersive learning mathematics geometry system can improve the learning motivation and learning performance of students. The findings indicated that the experimental group had better learning outcomes after completing the learning tasks of three geometric units. The experimental group used the virtual reality immersive learning mathematics geometry system which can lead to better learning outcomes. According to the ARCS questionnaire, students in the experimental group were confident to understand new subjects. At the same time, the mode of completing the game can effectively give students a sense of accomplishment. The use of emerging technologies in the classroom can be an attractive learning mode for students.

Impact of Parents' Attitudes on Learning Ineffectiveness: The Mediating Role of Parental Self-Efficacy.

Parents play a key role in children's home-based online learning. This study constructed a mediating model to explore the mechanism of parents' attitudes toward online learning (PATOL) and the perceived online learning ineffectiveness (POLI) of their children and to investigate the mediating effect of parents' self-efficacy (PSE) on PATOL and POLI. Valid questionnaire data from 18,170 middle school parents were collected by snowball sampling. The hypotheses proposed in this study were verified by using Model 4 of PROCESS. The results showed that: when controlling parents' gender, age, and children's length of online learning in regression equations, (1) both PATOL and PSE were negatively related to POLI, while PATOL was positively related to PSE; (2) PSE played a mediating role in the relationship between PATOL and POLI. This study also discusses how to support parents to assist children's home-based online learning. Schools should carry out some necessary training for parents. Parents can get guidance and advice on how to create an environment conducive to children's online learning.

Applying machine learning technologies to explore students' learning features and performance prediction.

To understand students' learning behaviors, this study uses machine learning technologies to analyze the data of interactive learning environments, and then predicts students' learning outcomes. This study adopted a variety of machine learning classification methods, quizzes, and programming system logs, found that students' learning characteristics were correlated with their learning performance when they encountered similar programming practice. In this study, we used random forest (RF), support vector machine (SVM), logistic regression (LR), and neural network (NN) algorithms to predict whether students would submit on time for the course. Among them, the NN algorithm showed the best prediction results. Education-related data can be predicted by machine learning techniques, and different machine learning models with different hyperparameters can be used to obtain better results.

Influencing Factors of University Relocation on College Students' Intention to Engage in Local Entrepreneurship and Employment.

In modern society, the power of college students has been able to provide creative growth for the local economy, so the work situation of college students is closely related to the social dynamics. Colleges and universities are important places for talent cultivation and output. They are closely related to the cultivation of college students and the choice of employment and entrepreneurship of college students. Entrepreneurship and employment are interdependent. It is not enough to rely only on entrepreneurs to make enterprises stronger. In order to increase the creativity and environmental adaptability of enterprises, there need to be sufficient and excellent employees who are willing to work in the regions where enterprises are located. Therefore, enterprises need college students with innovation and creativity in their regions. In this study, graduates from a university in Zhejiang Province were selected as the subjects. Based on the Theory of Reasoned Action, literature analysis and interview method were combined to systematically construct a research model affecting college Students' choice of entrepreneurship and employment. The questionnaire survey method and structural equation model (PLS-SEM) were used to test the hypothesis. A total of 798 valid questionnaires were collected. The results show that local attraction and family factors are the two most important factors affecting the entrepreneurship and employment of college students. Family factors have a significant positive impact on the attitude toward entrepreneurship and employment, subjective norms and intention to entrepreneurship and employment; local attraction also has a significant positive impact on the subjective norms, intention to entrepreneurship and employment, and attitude toward entrepreneurship and employment. The results of this study provide a reference for the decision-making of improving local attractiveness and promoting college Students' employment in start-up enterprises under the background of higher education popularization.

Push-Pull-Mooring Analysis of Massive Open Online Courses and College Students During the COVID-19 Pandemic.

The partial least squares structural equation modeling (PLS-SEM) provides researchers with an analysis tool for prediction theory. As the coronavirus disease 2019 (COVID-19) brings risks to teaching and learning, students have been forced to switch from classroom learning to online learning and most subjects have chosen massive open online courses (MOOCs) for online learning in China. This study examines whether MOOCs can replace traditional classroom education and explores the factors that influence the intentions of switching of the students from offline to online. We sequenced the PLS-SEM analysis of data with 397 students from a university in Zhejiang province of China, testing the model parameters, and discussing the push-pull-mooring (PPM) theory. Our data demonstrate that security risk is a push factor, switching costs are a mooring factor, and perceived usefulness and task-technology fit are pull factors that pull students from traditional, offline learning to MOOCs. In addition, the PPM model of the analysis results provides a more specific understanding of the importance-performance analysis of each factor. Our findings suggest that to constantly improve the switching intention to address unexpected challenges in the future, teachers should establish an effective emergency management measures, including curriculum design, to be consistent with their needs.

An Empirical Exploration of Sports Sponsorship: Activation of Experiential Marketing, Sponsorship Satisfaction, Brand Equity, and Purchase Intention.

The purpose of this study is to investigate the relationship between the activation of experiential marketing, satisfaction with sponsored sporting events, brand equity, and subsequent product purchase intentions in a small-scale sponsorship campaign. Survey data were collected from 238 actual runners in the terminal rest area after they had completed a race. Structural equation modeling with the bootstrap method was carried out to examine the proposed hypotheses. Results revealed that in terms of product purchase intention, brand equity was the most influential factor, followed by experiential marketing activation and satisfaction with the sponsorship. Specifically, experiential marketing activation significantly influenced the sports sponsorship and the sponsor's brand equity; however, satisfaction showed an insignificant effect on purchase intention. In addition, the mediation test shows that brand equity is an important mediator of experience marketing and satisfaction to product purchase intention. Findings provide some empirical insights into how small-scale sponsorship can benefit sponsoring companies, including intangible brand assets and tangible product consumption. The results could encourage more companies to organize small-scale sponsorship races and to present brand-related experiences (e.g., experiencing product packaging, on-the-spot experience areas). In this way, opportunities may be provided to attract more runners (or potential consumers) to participate in the event and have a deeper brand experience. This study contributes to a better understanding of the effectiveness of small-scale sponsorship in Asia to increase the generalization of the small sponsorship literature. Small-scale sporting events can bring public attention and economic benefits to the host company, and encourage more people to participate, thereby resulting in long-term social and health benefits in the community.

Safe Sexual Behavior Intentions among College Students: The Construction of an Extended Theory of Planned Behavior.

Sexual health education is an essential part of quality-oriented education for college students. It aims to help these students to acquire knowledge of sexual physiology, sexual psychology, and sexual social norms that is consistent with the maturity of the students. Along with college students'attitudes toward sex, their perceptions regarding sexual behavior have also undergone profound changes. The importance of safe sexual behavior, sexual taboos, and sexual autonomy are gaining increasing attention as Chinese society is becoming more open. For college students who have just reached adulthood and have full autonomy of themselves, however, are they really going to have sexual behavior without careful consideration? Or is it something they have planned to do in the first place? To answer the above questions, this study was conducted to understand the relationship between college students' attitudes toward sex, subjective norms, and behavioral control of their sexual behavior intentions by applying the Theory of Planned Behavior. In this study, 460 valid questionnaires were collected from Chinese college students and analyzed with partial least squares structural equation modeling (PLS-SEM). This study analyzes the relationship of multiple factors, including those influencing college students' sexual behavior intentions. Meanwhile, it also compares the differences in factors affecting sexual behavior intentions between college students with or without sexual experience and those of different genders. Based on the results of the study, it was found that, first, subjective norms and perceived behavioral control of college students had a significant effect on safe sexual behavior intentions, while attitudes did not have a significant effect on safe sexual behavior intentions. Second, the gender and sexual experience of college students had a significant effect on safe sexual behavior intentions. Third, non-sexually experienced college students were more likely to be influenced by external factors. Relevant future research suggestions will be proposed based on the results of this study. Finally, this study helps to provide substantive suggestions for enhancing safe sexual behavior among college students in the context of universal higher education, as well as strengthening the self-protection of college students and providing practical advice for the development of sex education in China.

Applying Educational Data Mining to Explore Viewing Behaviors and Performance With Flipped Classrooms on the Social Media Platform Facebook.

In recent years, learning materials have gradually been applied to flipped classrooms. Teachers share learning materials, and students can preview the learning materials before class. During class, the teacher can discuss students' questions from their notes from previewing the learning materials. The social media platform Facebook provides access to learning materials and diversified interactions, such as sharing knowledge, annotating learning materials, and establishing common objectives. Previous studies have explored the effect of flipped classrooms on students' learning engagement, attitudes, and performance. In this paper, we apply educational data mining to explore the relationship between students' viewing behaviors in accessing learning materials and their performance in flipped classrooms. The participants are classified into an experimental group and a control group to engage in flipped classroom activities. The experimental group uses the social media platform Facebook for flipped learning, and the control group uses a learning management system for flipped learning. The results show that there is a significant difference in the learning performance between the two groups, with the average score of the experimental group being higher than that of the control group. Furthermore, we find that the viewing behaviors and performance of the students within the experimental group differ significantly.

A Study on Traditional Teaching Method Transferring to E-Learning Under the Covid-19 Pandemic: From Chinese Students' Perspectives.

In response to the Covid-19 pandemic, online learning has been carried out in many countries with different types of online learning models being promoted and implemented. In the global pandemic continues, the education environment is forced to change from traditional classroom or blended teaching mode to online learning teaching model. With the outbreak of COVID-19, China was the first to announce that online courses are to be implemented in February 2020. In China, whether online learning can replace traditional offline teaching has become a topic worth discussing. Therefore, this study investigates university students in China by questionnaires and discussions of this topic. The study is based on the Push-Pull Mooring model. Based on 854 valid responses collected from an online survey questionnaire, structural equation modeling was employed to examine the research model. The results show that push effects (Perceived security risk, Learning convenience, and Service quality), pull effects (Usefulness, Ease of use, Teacher's Teaching Attitude, Task-technology Fit), and mooring effects (habit) all significantly influence users' switching intentions from offline to online learning platform. Finally, this study explores whether push-pull-mooring can be a reference for promoting and implementing online learning courses in Chinese colleges and universities in the future after the pandemic.