密度泛函理论在氮还原反应催化剂设计中的应用进展
Recent progress on catalyst design of nitrogen reduction reaction by density functional theory
代天一 1杨春成 1蒋青1
作者信息
- 1. Key Laboratory of Automobile Materials,Ministry of Education,and School of Materials Science and Engineering,Jilin University,Changchun 130022,China
- 折叠
摘要
发展电化学氮气还原反应(NRR)的合成氨技术可以有效缓解工业上的哈伯-博什法合成氨的化石燃料消耗与碳排放问题.同时,氨是一种无碳的能源载体,NRR可以实现可再生能源的转换,因此具有广阔的发展前景.然而,高效催化剂的缺乏限制了 NRR技术的发展.为此,人们对开发高效催化剂进行了广泛的探索,其中密度泛函理论(DFT)计算在辅助催化剂设计方面发挥了重要作用.在本综述中,我们总结了最近的催化剂设计策略,这些策略的目的是提高NRR的催化活性和选择性.此外,本综述还回顾了具有代表性的计算工作,并对进一步改善催化性能提出了见解.最后,本综述简要讨论了通过DFT计算进行催化剂设计所面临的挑战和机遇.目的在于指导人们采用更有效的设计策略来实现高效的NRR过程.
Abstract
The electrochemical nitrogen reduction reaction(NRR)technique has great potential for alleviating the high fossil fuel consumption and carbon emissions of the industrial Haber-Bosch method for ammonia(NH3)synthesis.More-over,the NRR provides great prospects for fully exploiting renewable energy since NH3 is a promising energy carrier without carbon emissions.However,the development of the NRR technique is limited by the lack of efficient catalysts.Great efforts have been made to develop high-efficiency cat-alysts thus far,in which density functional theory(DFT)cal-culations have played an important role in assisting catalyst design.Herein,we summarize the recent catalyst design strategies to boost the NRR performance,i.e.,the activity and selectivity.Additionally,representative computational studies are reviewed,accompanied by insights into further improving the catalytic behavior.Finally,we briefly discuss the challenges and opportunities in catalyst design via DFT calculations.The purpose of this review is to motivate more intelligent design strategies for high-efficiency NRR.
关键词
nitrogen reduction reaction/density functional the-ory/materials design/two-dimensional materials/machine learningKey words
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出版年
2024
NETL
NSTL
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