Tunable catalytic surfaces synthesized and studied by in-situ methods

Suny At Stony Brook, Stony Brook NY

Investigators

Abstract

Research examining modification of oxide surfaces with nanostructure has produced exciting materials for optical, mechanical, chemical, and other applications. These nanostructured surfaces can accelerate the chemical reactions used to produce fuel, enabling cheaper, more efficient, and more environmentally-sound production. As such, this project stands to protect the Nation's security through access to more affordable fuel sources. The project will advance understanding of how aspects of the nanostructured surface formation can facilitate the transformation of atmospheric emissions into fuels. This project also seeks to integrate research and educational activities through a combination of traditional and pioneering approaches. For example, the investigator plans to introduce educational games and on-line learning tools into undergraduate and high school courses. The effectiveness of efforts to broaden the participation of underrepresented groups in STEM will be evaluated through the on-line learning outcomes. Graduate students in the investigator's "Materials Impact on the Environment" course will have the opportunity to conduct pilot testing of self-cleaning catalytic coatings, which will be deposited on on-campus solar panels. Traditional methods of controlling morphology and size distribution of nanoparticles have significant drawbacks. For instance, capping and encapsulating agents are usually difficult to remove, resulting in surface contamination, while physical deposition methods are challenging to scale up. This project offers a unique approach to forming nanostructured surfaces by establishing new structure-property relations, where particle size, metal-oxide interfaces, and shape are tuned by a relatively simple yet novel and scalable approach. Modulation of reduction temperature, concentration, type of dopants, and the presence of oxygen vacancies will enable control of particle size and morphology, which are critical for achieving unique catalytic properties. By combining new in-situ characterization techniques and theoretical methods based on machine learning tunable surfaces, capable of high conversion of carbon dioxide to fuels, will be developed. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.