Accelerated Electrochemical Engineering Approaches for Sustainable Chemical Manufacturing

Miguel A. Modestino is the Director of the Sustainable Engineering Initiative and the Donald F. Othmer Associate Professor of Chemical Engineering at New York University (NYU). He also serves as Distinguished Visiting Professor of Industrial Decarbonization at Tecnologico de Monterrey (Mexico). Miguel obtained his B.S. in Chemical Engineering (2007) and M.S. in Chemical Engineering Practice (2008) from the Massachusetts Institute of Technology, and his Ph.D. in Chemical Engineering from the University of California, Berkeley (2013). From 2013-2016, he was a post-doctoral researcher at the École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland. He is a winner of the MIT Technology Review Innovators Under 35 Award in Latin America (2017) and Globally (2020), the ACS Petroleum Research Fund Doctoral New Investigator Award (2018), the NSF CAREER Award (2019), the Global Change Award from the H&M Foundation (2016), and the TED Idea Search Latin America (2021). His research group at NYU operates at the intersection of advanced chemical manufacturing technologies, high throughput experimentation and its integration with AI. He is also the co-founder of Sunthetics, a startup developing machine learning solutions to accelerate R&D in the chemical industry, and Metrotech Energy, a startup developing flexible energy storage solutions for power resiliency.

The chemical industry produces more than 70,000 products (1.2 billion tons in total) via thermal processes powered by fossil fuel combustion, accounting for ~5% of the US energy utilization and >30% of the US energy-derived industrial CO2 emissions. Among these processes, the production of organic chemical commodities accounts for most of the energy consumption (>1200 TBTU/y), and the electrification of these processes via the implementation of electro-organic reactions could enable a deeper integration of renewable electricity sources with chemical plants and accelerate the decarbonization of the chemical industry. Currently, however, two major challenges prevent the deployment of electro-organic reactions at scale: their low selectivity and their low production rates. To circumvent these barriers, my group combines electrochemical reaction engineering principles, high throughput experimentation (HTE) and machine-learning methods to accelerate the development of high-performing electro-organic reaction processes.

In this presentation, I will start with a discussion of our studies on the electrochemical production of adiponitrile (ADN), a precursor to Nylon 6,6, via the electrohydrodimerization of acrylonitrile (AN). This is the largest and most successful electro-organic reaction deployed in industry and serves as a test case for the development of high-performing organic electrochemical processes. Our investigations on ADN are aimed at uncovering the relationship between the electrochemical environment at and near the electrical double layer (EDL) and reaction performance metrics (i.e., selectivity, efficiency, and productivity). I will discuss general guidelines for electrolyte formulation and provide molecular insights into the role of different species at electrode/electrolyte interfaces (e.g., buffer ions, chelating ions, selectivity-directing ions, and supporting ions) in enhancing conversions of AN to ADN. I will also present how carefully controlling pulsed electrosynthesis conditions guided by active machine learning can help mitigate mass transport limitations, control the concentration of AN near the EDL and enhance the production rate of ADN.

Leveraging our experience with ADN electrosynthesis and with the intention to accelerate the development of high-performing electrosynthetic processes, my group has recently developed HTE tools that allow us to explore 10-100 reaction environments per day, and apply machine-learning methods to accelerate and enhance chemical analysis, to maximize desired performance metrics, and to extract knowledge from complex reaction explorations in high-dimensional parameter spaces. These new tools are helping us elucidate key process-activity relationships for novel chemical transformations (e.g., production of high-value products from biomass waste and CO2) and are setting the foundations for the development of future smart reactors that effectively integrate artificial intelligence in their active control.