We, The Simon Ensemble (simonensemble.github.io) in the School of Chemical, Biological, and Environmental Engineering (CBEE) at Oregon State University, are looking for a full-time, fully-funded PhD student to join our team starting September 2019. You will employ molecular models and simulations as well as machine learning to pinpoint optimal nano-porous materials to carry out gas separations. Candidates must have received an undergraduate degree in (preferably) chemical engineering or a related field by September 2019.
For more information about our research group, see: https://simonensemble.github.io/
See our most recent article on ACS Central Science: https://pubs.acs.org/doi/10.1021/acscentsci.8b00638
Oregon State University resides in Corvallis, Oregon, a beautiful location in the Pacific Northwest of the United States. See here for some reasons why Oregon is a great place in which to live: https://simonensemble.github.io/join/
Please apply to the CBEE PhD program at Oregon State University by Dec. 20th 2018 and mention in your application that you are interested in working in the Simon Ensemble: https://cbee.oregonstate.edu/che-graduate-program
If you have questions or would like to discuss, please contact me at Cory.Simon [at] oregonstate.edu or submit, in the form below, your CV and a brief statement of your research interests.
More about the project:
Metal-organic frameworks (MOFs) are crystalline materials that possess nano-sized pores. The enormous internal surface areas and permanent porosity of MOFs afford them adsorption-based applications in storing, separating, and sensing gases. For example, MOFs can densify hydrogen gas for onboard vehicular fuel storage, selectively capture radioactive gases emitted during the reprocessing of used nuclear fuel, and detect toxic vapors or explosives as sensors.
See here for an interactive visualization of a MOF structure: https://simonensemble.github.io/nisif6
An exciting aspect of MOFs is their modular and versatile chemistry. In the synthesis of MOFs, metal nodes/clusters and organic linker molecules self-assemble in solution to construct a porous framework. By changing the metal nodes and linker molecules, many different MOFs can be synthesized to exhibit diverse pore geometries and surface chemistries. Tens of thousands of different MOFs have been synthesized to date.
This high chemical tunability allows chemists to fine-tune MOF architectures to target specific molecules for applications in gas storage, separations, and sensing. More broadly, this versatile and modular chemistry enables the synthesis of MOFs with diverse properties and functionalities. Because of the billions of possible MOF structures, molecular models and simulations of gas adsorption in MOFs play an important role in the discovery and deployment of MOFs for separations and sensing.
This project entails employing molecular simulations in conjunction with machine learning to sift through the many variations of MOFs and predict the top performing materials for a gas separation.
International students may apply.
The project is suitable for candidates who have, or expect to obtain, a bachelor's degree (or equivalent) in chemical engineering, materials science, chemistry, physics, mathematics, or computer science.
Note that in the USA, a Master's degree is *not* required to pursue a PhD.