NUMAT TECHNOLOGIES INC — National Science Foundation SBIR Phase I: This Small Business Innovation Research (SBIR) Phase I project searches for better materia

NUMAT TECHNOLOGIES INC — SBIR Phase I award from National Science Foundation.

Amount
$150,000
Agency
National Science Foundation
Program / Phase
SBIR · Phase I
NAICS
Place of performance
IL
Period
2013-07-01 → 2013-12-31

Description

This Small Business Innovation Research (SBIR) Phase I project searches for better materials to efficiently and safely store gases used in semiconductor manufacturing. Currently these gases are stored in tanks at low pressures in dilute concentrations for safety reasons, which requires the use of many tanks that need to be frequently refilled. This increases the cost of semiconductor manufacturing which drives up the cost of consumer electronics. Porous materials, which soak up gases like bath sponges soak up water, can be used to store these gases in larger concentrations while maintaining safety. However, the number of porous materials that we can use to potentially store these gases is enormous (i.e., hundreds of millions) and so quickly finding the best material requires advanced computational screening methods. This project will computationally generate millions of hypothetical porous materials and screen them for their ability to store gases used in semiconductor manufacturing at industrially relevant temperatures and pressures. The computationally screening data will be used to synthesize and test an optimal material in the laboratory, which could subsequently be manufactured at larger scale. The broader impact/commercial potential of this project will be the cheaper production of electronics and a safer working environment in semiconductor manufacturing facilities. This will open the door to designing porous materials for other gas storage applications in such areas such as adsorptive heat exchange, carbon capture, and commodity gas transportation. Even more broadly, the successful determination of an optimal material via large-scale computational screening will further validate the utility of ?big data? in the modern scientific enterprise.