Researchers harness nature to produce the fuel of the future

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.

Read the abstract.

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.