NSF DMREF PSU LTE

Membrane Databases – New Schema and Dissemination

Recipient Penn State University (PI: Michael A. Hickner)

Subs National Institute of Standards and Technology/NIST (PI: Debra Audus) and Rensselaer Polytechnic Institute/RPI (PI: Chulsung Bae)

HydroGEN Node Experts

National Renewable Energy Laboratory:

• Shaun Alia
• Guido Bender
• Kristin Munch
• Bryan Pivovar

Water Splitting Technology LTE

Status Awarded

Abstract The DMREF project has generated a wealth of new chemical structures for anion exchange membranes (AEMs), new fundamental materials data on these materials, and computationally-led design motifs for improvements in membrane properties. The Penn State and RPI groups have approximately 200 materials cataloged from their own work and from the literature and are now ready to interface this knowledge with a national repository to build out the community knowledge in this area. HydroGEN is focused on water splitting materials, of which AEMs are a key material, and will have access to novel AEM materials to carry out electrolysis water splitting experiments. HydroGEN also has a national Data Hub that is an ideal repository for storing AEM data, including water splitting results. Additionally, the DMREF team will use NIST and CHiMaD investigators as advisors to the project to ensure that proper protocols and polymer naming schemes are being used to construct a durable database for functional polymeric materials that has fidelity across national platforms. The database is currently large enough for some limited data mining operations concerning structure-property relationships. HydroGEN will be able to more effectively mine the possible structures for new membranes, including block and random copolymers that the DMREF team has pioneered. The DMREF team’s current membrane design thinking and smart database can be utilized to accelerate the development of new membranes for water splitting. This will be the first work of this kind connecting a database of experimental results, to device performance, to the data analysis tools and integrated view of design of high-performance membranes. We aim for a paradigm shift in membrane design beyond structure-property relationships to more sophisticated data mining tools and informed choices on points of validation and learning to drive new generations of materials.