Hydrogen has tremendous potential as an eco-friendly fuel, but it is expensive to produce. Now researchers at Princeton University and Rutgers University have moved a step closer to harnessing nature to produce hydrogen for us.
The team, led by Princeton chemistry professor Annabella Selloni, takes inspiration from bacteria that make hydrogen from water using enzymes called di-iron hydrogenases. Selloni's team uses computer models to figure out how to incorporate the magic of these enzymes into the design of practical synthetic catalysts that humans can use to produce hydrogen from water.
In this latest paper, Selloni and co-authors present a solution to an issue that has dogged the field: the catalysts designed so far are susceptible to poisoning by the oxygen present during the reaction. By making changes to the catalyst to improve the stability of the structure in water, the researchers found that they had also created a catalyst that is tolerant to oxygen without sacrificing efficiency. What is more, their artificial catalyst could be made from abundant and cheap components, such as iron, indicating that the catalyst could be a cost-effective way of producing hydrogen.
Selloni and her team conducted their research in silico -- that is, using computer modeling. The goal is to learn enough about how these catalysts work to someday create working catalysts that can make vast quantities of inexpensive hydrogen for use in vehicles and electricity production.
The team included Patrick Hoi-Land Sit, an associate research scholar in chemistry at Princeton; Roberto Car, Princeton's Ralph W. *31 Dornte Professor in Chemistry, and Morrel H. Cohen, a Senior Chemist at Princeton and Member of the Graduate Faculty of Rutgers University. Selloni is Princeton's David B. Jones Professor of Chemistry.
Citation: Sit, Patrick H.-L., Roberto Car, Morrel H. Cohen, and Annabella Selloni. Oxygen tolerance of an in silico-designed bioinspired hydrogen-evolving catalyst in water. PNAS 2013; published ahead of print January 22, 2013, doi:10.1073/pnas.1215149110
This work was supported by the Department of Energy, Office of Basic Energy Sciences, Division of Materials Sciences and Engineering under Award DE-FG02-06ER-46344. We also used resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the US Department of Energy under Contract DE-AC02-05CH11231. The team also used computational resources from the Princeton Institute for Computational Science and Engineering (PICSciE) and the Office of Information Technology (OIT) High Performance Computing Center and Visualization Laboratory at Princeton University.