Yixuan Huang

Developing green heterogeneous catalysts with excellent Fenton-like activity is critical for water remediation technologies. However, current catalysts often rely on toxic transitional metals, and their catalytic performance is far from satisfactory as alternatives of homogeneous Fenton-like catalysts. In this study, a green catalyst based on Zn single-atom was prepared in an ammonium atmosphere using ZIF−8 as a precursor. Multiple characterization analyses provided evidence that abundant intrinsic defects due to the edge sites were created, leading to the formation of a thermally stable edge-hosted Zn−N 4 single-atom catalyst (ZnN 4 −Edge). Density functional theory calculations revealed that the edge sites equipped the single-atom Zn with a super catalytic performance, which not only promoted decomposition of peroxide molecule (HSO 5 − ) but also greatly lowered the activation barrier for • OH generation. Consequently, the as-prepared ZnN 4 −Edge exhibited extremely high Fenton-like performance in oxidation and mineralization of phenol as a representative organic contaminant in a wide range of pH, realizing its quick detoxification. The atom-utilization efficiency of the ZnN 4 −Edge was ~10 4 higher than an equivalent amount of the control sample without edge sites (ZnN 4 ), and the turnover frequency was ~10 3 times of the typical benchmark of homogeneous catalyst (Co 2+ ). This study opens up a revolutionary way to rationally design and optimize heterogeneous catalysts to homogeneous catalytic performance for Fenton-like application.

A unique encapsulated structure was used for peroxide activation. The strong interfacial interactions facilitated electronic communication, thus promoting the generation of NG–PMS*, which reacted with phenol by an anodic-like nonradical process.

The integration of generative artificial intelligence (GenAI) into web-based individual formative e-assessments in higher education is a nascent field that warrants further exploration. This study investigated the use of GenAI within an 8-week undergraduate-level research methods course at a university in the United States of America, aiming to understand how students leverage GenAI tools during individual formative e-assessments questions. The research revealed that a significant majority of students initially preferred traditional study resources over GenAI. However, a gradual shift towards more balanced use of both resources was observed, particularly in formative e-assessments involving statistical analysis and calculation questions. In their interactions with GenAI, students primarily used it for multiple-choice and true/false questions, often by directly copying and pasting the question prompt into the GenAI interface. Students were able to discern and accept accurate responses generated by GenAI and reject those that were incorrect or contradicted their existing knowledge. Students’ reported primary motivations for turning to GenAI were to seek answers to assessment items as well as to corroborate the accuracy of their own responses. This study contributes to the growing body of literature empirically investigating actual usage behaviours with GenAI tools and the motivation behind these behaviours. We discuss the implications and limitations of these findings. Implications for practice or policy: Educators should develop AI literacy programmes and integrate them into pedagogy strategies. Educators and researchers need clear guidelines for ethical AI use in formative e-assessments. Educators should encourage students’ critical thinking and source evaluation on the information that GenAI provides.

It is non-trivial to provide semantic security for user data while achieving deduplication in cloud storage. Some studies deploy a trusted party to store deterministic tags for recording data popularity, then provide different levels of security for data according to popularity. However, deterministic tags are vulnerable to offline brute-force attacks. In this paper, we first propose a popularity-based secure deduplication scheme with fully random tags, which avoids the storage of deterministic tags. Our scheme uses homomorphic encryption (HE) to generate comparable random tags to record data popularity and then uses the binary search in the AVL tree to accelerate the tag comparisons. Besides, we find the popularity tamper attacks in existing schemes and design a proof of ownership (PoW) protocol against it. To achieve scalability and updatability, we introduce the multi-key homomorphic proxy re-encryption (MKH-PRE) to design a multi-tenant scheme. Users in different tenants generate tags using different key pairs, and the cross-tenant tags can be compared for equality. Meanwhile, our multi-tenant scheme supports efficient key updates. We give comprehensive security analysis and conduct performance evaluations based on both synthetic and real-world datasets. The results show that our schemes achieve efficient data encryption and key update, and have high storage efficiency.