Research Projects

The interdisciplinary proposal is aimed in creating a cost-effective environmentally benign solution for mitigation of PFAS from wastewater. The reported works till date focused on the development of various MOF and their application in synthetic solution of various combinations of PFAS in a laboratory scale. However, the effect of real-life wastewater and scale up combination is yet to be explored. Moreover, the MOF reported in the previous literatures were made using costly linkers and under high pressure high temperature system. The present work aims in formulating 3-D structure MOF based nanoparticles with high surface area and high zeta potential which will make it competitive for the said application. The process used for the preparation of the same will be defect engineering, where the organic ligand will be replaced by low cost linkers with same functionality, one such example is preparation of iron thiomalate MOF where the linker chosen is a combination of fumaric acid and thiomalic acid. In this project, four low cost 3-D structured MOF based nanoparticles will be prepared for the first time, namely, aminated Aluminum fumarate, aminated MOF-801 and two bi-metallic MOFs, Cu-AlFu and Co-AlFu will be prepared. The efficacy of the same will be measured for perfluorooctanoic acid (PFOA), perfluorooctane sulfonate (PFOS) and other relevant PFAS contaminants. These powder materials will be needed to be encompassed in a matrix for scaling up of the system. In this regard, typically mixed matrix membranes are used and reported in literatures. However, in this project, the materials will be layered over a porous membrane structure using Thin Film Nanocomposite (TFN) technique. Throughout the scope of this effort, we would also leverage and systematically employ the unique material characterization techniques housed and developed in our facilities at IITM, specifically, advanced in-situ and operando methods (using: XRD, XPS, HPLC., HR-TEM, etc.) for guiding our scientific decisions through a positive data driven feedback loop.