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计算机筛选碳捕获材料。

In silico screening of carbon-capture materials.

机构信息

Department of Chemical and Biomolecular Engineering, University of California, Berkeley, California 94720-1462, USA.

出版信息

Nat Mater. 2012 May 27;11(7):633-41. doi: 10.1038/nmat3336.

Abstract

One of the main bottlenecks to deploying large-scale carbon dioxide capture and storage (CCS) in power plants is the energy required to separate the CO(2) from flue gas. For example, near-term CCS technology applied to coal-fired power plants is projected to reduce the net output of the plant by some 30% and to increase the cost of electricity by 60-80%. Developing capture materials and processes that reduce the parasitic energy imposed by CCS is therefore an important area of research. We have developed a computational approach to rank adsorbents for their performance in CCS. Using this analysis, we have screened hundreds of thousands of zeolite and zeolitic imidazolate framework structures and identified many different structures that have the potential to reduce the parasitic energy of CCS by 30-40% compared with near-term technologies.

摘要

在电厂中大规模部署二氧化碳捕集与封存(CCS)的主要瓶颈之一是从烟道气中分离 CO(2)所需的能量。例如,应用于燃煤电厂的近期 CCS 技术预计将使工厂的净输出减少约 30%,并使电力成本增加 60-80%。因此,开发可减少 CCS 带来的寄生能量的捕集材料和工艺是一个重要的研究领域。我们已经开发了一种计算方法来对吸附剂在 CCS 中的性能进行排序。使用这种分析方法,我们筛选了数十万种沸石和沸石咪唑酯骨架结构,并确定了许多不同的结构,它们有可能将 CCS 的寄生能量比近期技术降低 30-40%。

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