SBIR Phase I: Computational Screening & Synthesis of MOFs for Hazardous Gas Storage

这个小企业创新研究（SBIR）第一阶段项目寻找更好的材料来有效和安全地存储半导体制造中使用的气体。目前，出于安全考虑，这些气体在低压下以稀浓度储存在储罐中，这需要使用许多需要经常重新填充的储罐。这增加了半导体制造的成本，从而推高了消费电子产品的成本。多孔材料可以像沐浴海绵吸水一样吸收气体，可以在保持安全的同时以更高的浓度储存这些气体。然而，我们可以用来储存这些气体的多孔材料的数量是巨大的（即，数亿），所以快速找到最好的材料需要先进的计算筛选方法。该项目将通过计算产生数百万种假设的多孔材料，并筛选它们在工业相关温度和压力下存储半导体制造中使用的气体的能力。计算筛选数据将用于在实验室合成和测试一种最佳材料，随后可以大规模生产。这个项目更广泛的影响/商业潜力将是更便宜的电子产品生产和半导体制造设施更安全的工作环境。这将为设计用于吸附热交换、碳捕获和商品气体运输等领域的其他气体储存应用的多孔材料打开大门。更广泛地说，通过大规模计算筛选成功确定最佳材料将进一步验证？大数据?在现代科学事业中。

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.