New publication in Environmental Science & Technology: A deep learning neural network approach for predicting the sorption of ionizable and polar organic pollutants to a wide range of carbonaceous materials


Contaminants can be removed from the environment via a range of techniques. One of the most common approaches is the use of carbonaceous materials such as activated carbon, carbon nanomaterials and biochar. These materials can be used for water purification as well as for contaminant remediation in soils and sediments. How strongly a contaminant is bound is often difficult to foresee, but is important for the successful use of carbonaceous materials. Researchers from the University of Vienna and the University of Waterloo have now developed a model to address this issue. The results were published inĀ Environmental Science & Technology.