C3.AI research award on Energy and climate security!

June 11, 2021 - June 11, 2021

AI-Driven Materials Discovery Framework for Energy-Efficient and Sustainable Electrochemical Separations


C3.AI digital transformation institute research award to Shukla group in collaboration with Prof. Xiao Su (ChBE, Illinois). The focus of the project is on removing nitrate from the agricultural waste water using redox-active polymers. You can learn more about the proposed research here.

Project Abstract: Clean water is a grand challenge of the 21st century, as 700 million people worldwide lack access due to geographic constraints and anthropogenic pollution. Ionic pollutants like heavy metals and organic nutrients are critical separation challenges, and closely related to climate and energy security. Electrically-driven water purification is advantageous, as it integrates well with renewable energy and eliminates secondary pollution from chemical inputs. Water purification and regional-scale water treatment can be some of the most carbon-intensive and chemical-intensive processes in a municipality. To address these challenges, we will establish a new paradigm for rationally designing redox-polymers for anion-selective separations, using first-principles calculations, machine learning, and molecular dynamics (MD) simulations to guide adsorbent development from redox group selection to polymer synthesis to applications in sustainable separations. Redox-active polymers offer a powerful avenue for electrochemical control of ion-selectivity and reversibility, through tailored synthesis. We will: 1) establish a machine learning framework coupled with distributed computing and quantum mechanical MD (QM/MD) for screening and informing adsorbent design, based on binding calculations between ion-receptors and target ions, and 2) integrate theory with synthesis and electrochemical tests to quantify and later predict selectivity for a range of target ions. Through close iteration between computation and experiments, our novel approach will account for both microscopic molecular binding and macroscopic energy performance. We expect this proof-of-concept project to establish a workflow for accelerating the development of new technologies for water treatment and selective contaminant remediation, with reduced carbon footprint and enhanced energy efficiency.