The conversion of CO 2 into useful chemicals and fuels is one way to combat the energy crisis and the greenhouse effect. The design and synthesis of inexpensive and readily available catalysts with excellent catalytic activity, good stability, and high selectivity is very important but faces enormous challenges. In this paper, we study the catalytic properties of TM-TCNQ monolayers as single atom catalysts for CO 2 reduction. The results show that the TM-TCNQ monolayers are very stable and effectively bind the isolated metal atoms in place. These bound metal atoms can then act as single-atom catalysts. Most of the ten transition metal series atoms studied also exhibit good inhibition of the hydrogen evolution reaction. The primary reduction product of Sc-TCNQ and Ti-TCNQ is CH 4 , however this reaction requires a high overpotential above 2 V. The main catalytic product of V-TCNQ, Cr-TCNQ, Mn-TCNQ, Ni-TCNQ and Cu-TCNQ is HCOOH. For Fe-TCNQ and Co-TCNQ, HCHO is dominant. CO is primarily produced from Zn-TCNQ. Remarkably, the overpotentials of these last eight materials are all small enough (0.12 to 0.45 V) to prepare competitive catalysts experimentally. These overpotentials are all much lower than the 0.77 V value for the Cu (211) surface, which is the most active stepped solid surface. Compared with the experimentally available molecular catalyst (iron(0) porphyrin complex) for CO 2 reduction with very good performance (overpotential just 0.465 V), some of our proposed 2D TM-TCNQ monolayers have comparable or even smaller overpotentials, and may provide new opportunities. Since these materials will all be stable under real world conditions, we predict that the TM-TCNQ materials will exhibit strong catalytic activity in the catalytic reduction of CO 2 , thus making TM-TCNQ monolayers exciting new CO 2 electrocatalytic reduction reaction catalysts.
Bibliographical noteFunding Information:
J.-H. L and L.-M. Y. gratefully acknowledge support from the National Natural Science Foundation of China (21673087 and 21873032), Startup Fund (2006013118 and 3004013105) and Independent Innovation Research Fund (0118013090) from the Huazhong University of Science and Technology. The authors thank the Minnesota Supercomputing Institute (MSI) at the University of Minnesota for supercomputing resources.