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

这个小型企业创新研究(SBIR)第一阶段项目正在寻找更好的材料来高效、安全地存储半导体制造中使用的气体。目前，出于安全原因，这些气体以稀释液浓度储存在低压储罐中，这需要使用许多储罐，这些储罐需要频繁地重新灌装。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。多孔性材料吸收气体，就像沐浴海绵吸收水一样，可以用来储存更大浓度的这些气体，同时保持安全。然而，我们可以用来潜在储存这些气体的多孔材料的数量是巨大的(即，数亿)，因此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想的多孔材料，并筛选它们在工业相关的温度和压力下存储半导体制造中使用的气体的能力。计算机筛选数据将用于在实验室合成和测试最佳材料，随后可以更大规模地生产。该项目的更广泛影响/商业潜力将是在半导体制造设施中以更低的成本生产电子产品和更安全的工作环境。这将为设计用于吸附热交换、碳捕获和商品天然气运输等领域的其他气体存储应用打开大门。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的效用。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.