Alkali-activated foams are ceramic-like materials prepared at near-ambient temperature. This study investigates them for point-of-use water disinfection, thus providing an alternative to ceramic filters fired at a high temperature. Alkali-activated foams with different compositions were characterized for the porosity, mechanical strength, shrinkage, and microstructure. The optimized foam, employing metakaolin as the raw material, was coated with a colloidal Ag solution. The disinfection performance and leaching behavior of the foams was followed in a continuous 10 week experiment, where clean water with a weekly pulse of contaminated water was distributed through the foam. The average inactivation of Escherichia coli with the Ag-coated foam was 2.84 log₁₀, which was 1.27 units higher compared to foam without Ag. A quantitative polymerase chain reaction analysis and metagenomic sequencing verified that foams with and without Ag were both capable of reducing the microbial load. Furthermore, the changes induced by the foam with Ag on the microbial community composition, antibiotic resistome, and metal and biocide resistomes were significant. The leached concentrations of Ag, Na, Si, and Al were in accordance with the drinking water guidelines. Finally, a life cycle assessment indicated the possibility of reducing the global warming potential and the total embodied energy in comparison with a conventional ceramic filter.

Edge artificial intelligence (AI) is an innovative computing paradigm that aims to shift the training and inference of machine learning models to the edge of the network. This paradigm offers the opportunity to significantly impact our everyday lives with new services such as autonomous driving and ubiquitous personalized health care. Nevertheless, bringing intelligence to the edge involves several major challenges, which include the need to constrain model architecture designs, the secure distribution and execution of the trained models, and the substantial network load required to distribute the models and data collected for training. In this article, we highlight key aspects in the development of edge AI in the past and connect them to current challenges. This article aims to identify research opportunities for edge AI, relevant to bring together the research in the fields of artificial intelligence and edge computing.