Investigation of the Effects of Solid-State Treatments on the Structure and Mobility of Copper in Zeolites

Zeolites are microporous, aluminosilicate catalysts that play an important role in industrial applications as well as studies for the fundamental understanding of catalysts for emerging reactions of interest. The introduction of aluminum into the zeolite lattice introduces a negative charge on the framework that can be balanced with extra-framework cations. The control of the aluminum distribution and the choice of charge balancing cations allows for the ability to tailor the active sites to facilitate a desired reaction. This research focuses on studying copper active sites in zeolites. Copper oxide was used as a copper precursor to introduce copper ions in zeolites through solid-state ion-exchange (SSIE). Solid-state ion-exchange was studied using both dry air and wet air treatments at elevated temperatures. Three different zeolite topologies were studied: CHA (small pore), ZSM-5 (medium pore), and MOR (large pore). After SSIE, the copper-zeolites were characterized with atomic absorption spectroscopy (AAS) after sodium back exchange to quantify the number of ionic copper species, and temperature programmed desorption (TPD). These characterization techniques were used to understand how many copper ions were mobilized into the zeolites, which are potential active sites in zeolites. Based on current experimental data on Cu-MOR, SSIE using a wet air treatment has a greater impact for mobilizing copper in zeolites compared to a dry air treatment. The same trend is expected to follow on other zeolite topologies, ZSM-5 and CHA, that are still being studied

Metaverse Security and Privacy: An Overview

Metaverse is a living space and cyberspace that realizes the process of virtualizing and digitizing the real world. It integrates a plethora of existing technologies with the goal of being able to map the real world, even beyond the real world. Metaverse has a bright future and is expected to have many applications in various scenarios. The support of the Metaverse is based on numerous related technologies becoming mature. Hence, there is no doubt that the security risks of the development of the Metaverse may be more prominent and more complex. We present some Metaverse-related technologies and some potential security and privacy issues in the Metaverse. We present current solutions for Metaverse security and privacy derived from these technologies. In addition, we also raise some unresolved questions about the potential Metaverse. To summarize, this survey provides an in-depth review of the security and privacy issues raised by key technologies in Metaverse applications. We hope that this survey will provide insightful research directions and prospects for the Metaverse's development, particularly in terms of security and privacy protection in the Metaverse.Comment: IEEE BigData 2022. 10 pages, 2 figure

Emerging applications of integrated optical microcombs for analogue RF and microwave photonic signal processing

We review new applications of integrated microcombs in RF and microwave photonic systems. We demonstrate a wide range of powerful functions including a photonic intensity high order and fractional differentiators, optical true time delays, advanced filters, RF channelizer and other functions, based on a Kerr optical comb generated by a compact integrated microring resonator, or microcomb. The microcomb is CMOS compatible and contains a large number of comb lines, which can serve as a high performance multiwavelength source for the transversal filter, thus greatly reduce the cost, size, and complexity of the system. The operation principle of these functions is theoretically analyzed, and experimental demonstrations are presented.Comment: 16 pages, 8 figures, 136 References. Photonics West 2018 invited paper, expanded version. arXiv admin note: substantial text overlap with arXiv:1710.00678, arXiv:1710.0861

AI-Generated Content (AIGC): A Survey

To address the challenges of digital intelligence in the digital economy, artificial intelligence-generated content (AIGC) has emerged. AIGC uses artificial intelligence to assist or replace manual content generation by generating content based on user-inputted keywords or requirements. The development of large model algorithms has significantly strengthened the capabilities of AIGC, which makes AIGC products a promising generative tool and adds convenience to our lives. As an upstream technology, AIGC has unlimited potential to support different downstream applications. It is important to analyze AIGC's current capabilities and shortcomings to understand how it can be best utilized in future applications. Therefore, this paper provides an extensive overview of AIGC, covering its definition, essential conditions, cutting-edge capabilities, and advanced features. Moreover, it discusses the benefits of large-scale pre-trained models and the industrial chain of AIGC. Furthermore, the article explores the distinctions between auxiliary generation and automatic generation within AIGC, providing examples of text generation. The paper also examines the potential integration of AIGC with the Metaverse. Lastly, the article highlights existing issues and suggests some future directions for application.Comment: Preprint. 14 figures, 4 table

Large Language Models in Education: Vision and Opportunities

With the rapid development of artificial intelligence technology, large language models (LLMs) have become a hot research topic. Education plays an important role in human social development and progress. Traditional education faces challenges such as individual student differences, insufficient allocation of teaching resources, and assessment of teaching effectiveness. Therefore, the applications of LLMs in the field of digital/smart education have broad prospects. The research on educational large models (EduLLMs) is constantly evolving, providing new methods and approaches to achieve personalized learning, intelligent tutoring, and educational assessment goals, thereby improving the quality of education and the learning experience. This article aims to investigate and summarize the application of LLMs in smart education. It first introduces the research background and motivation of LLMs and explains the essence of LLMs. It then discusses the relationship between digital education and EduLLMs and summarizes the current research status of educational large models. The main contributions are the systematic summary and vision of the research background, motivation, and application of large models for education (LLM4Edu). By reviewing existing research, this article provides guidance and insights for educators, researchers, and policy-makers to gain a deep understanding of the potential and challenges of LLM4Edu. It further provides guidance for further advancing the development and application of LLM4Edu, while still facing technical, ethical, and practical challenges requiring further research and exploration.Comment: IEEE BigData 2023. 10 page

Multimodal Large Language Models: A Survey

The exploration of multimodal language models integrates multiple data types, such as images, text, language, audio, and other heterogeneity. While the latest large language models excel in text-based tasks, they often struggle to understand and process other data types. Multimodal models address this limitation by combining various modalities, enabling a more comprehensive understanding of diverse data. This paper begins by defining the concept of multimodal and examining the historical development of multimodal algorithms. Furthermore, we introduce a range of multimodal products, focusing on the efforts of major technology companies. A practical guide is provided, offering insights into the technical aspects of multimodal models. Moreover, we present a compilation of the latest algorithms and commonly used datasets, providing researchers with valuable resources for experimentation and evaluation. Lastly, we explore the applications of multimodal models and discuss the challenges associated with their development. By addressing these aspects, this paper aims to facilitate a deeper understanding of multimodal models and their potential in various domains.Comment: IEEE BigData 2023. 10 page