Ultrafast degradation of organic dyes via PMS activation by CNT-loaded MOF-derived Co nanoparticles
Peer-Reviewed Publication
Updates every hour. Last Updated: 7-Jun-2026 06:16 ET (7-Jun-2026 10:16 GMT/UTC)
A novel catalyst developed by the research team offers a powerful solution for combating hard-to-degrade organic pollutants. By skillfully combining metal-organic frameworks (MOFs) with carbon nanotubes (CNTs), the team created a cobalt-based catalyst that efficiently activates peroxymonosulfate (PMS) through a highly selective non-radical pathway. This innovative approach ensures effective pollutant degradation across a wide pH range with strong anti-interference ability, marking a significant advance in green and sustainable water treatment technology.
Hydrogels have been highlighted as effective wound dressings for tissue regeneration, while microbeads serve as versatile carriers for controlled drug release and targeted delivery, allowing customization for specific therapeutic needs. Ahmed, Guo and Huang et al. from Taiyuan University of Technology provide a comprehensive review published by Frontiers of Materials Science on microbeads-assisted antibacterial hydrogels for wound healing.
AI is rapidly entering classrooms worldwide, but current education governance models are not designed to manage its systemic impact. A new study argues that AI should be understood not merely as a teaching tool, but as a governance actor that reshapes authority, accountability, and professional autonomy in education systems. The article proposes a reconfigured hybrid governance framework to help education systems harness AI’s benefits while protecting democratic values, learner autonomy, and professional judgment.
The School Digital Renewal Process (SDRP) has evolved from infrastructure-focused adoption to deep pedagogical transformation centered on personalized, competence-based learning. Traditional indicators—such as device availability or connectivity—lose relevance at advanced SDRP stages. This article proposes a novel, evidence-based approach to constructing indicators that capture shifts in learning content and organization through automated analysis of schools’ digital footprints (publicly available digital materials) using AI tools. Drawing on Bloom’s Revised Taxonomy and empirical data from international schools, we demonstrate the feasibility of tracking second-order educational change without relying on teacher surveys. The framework supports comparative monitoring of digital transformation aligned with the demands of the AI era. The article introduces a groundbreaking innovation: the use of AI tools for gathering and analyzing indicators from publicly available digital sources in education institutions. This approach offers a scalable and cost-efficient way to track and evaluate SDRP at later stages of its development.