Zhongwei Lu

(he/him)

Research

DFT & data mining assisted catalyst discovery of Cu-based alloys for CO₂ hydrogenation to alcohols

Direct methanol synthesis from CO₂ is important to deliver a circular economy,¹ for which Cu-based catalysts are promising candidates.² Some Cu-based catalysts can achieve high methanol selectivity, but the inability of maintaining adequate CO₂ conversion rate and stability still hinders their industrial application.² One mechanistic bottleneck here is the slow reaction of formate, HCOO, to subsequent intermediates.³ Cu-based alloys possess unique electronic properties which can introduce reactivity, such as C-O scission ability, leading to easier conversion of HCOO. Density functional theory (DFT) studies on dilute alloy models, such as single-atom alloys (SAAs), have been greatly helpful in understanding alloying effects in catalysis.⁴ Data-mining methods, such as subgroup discovery (SGD)⁵, can help to identify local patterns of outstanding materials. Here, DFT and SGD were used to investigate four elementary reactions of formate on a series of Cu SAAs. The traits of alloys facilitating each reaction were captured, and analyses on their physical meaning showed electron donating and oxygen affinity are key properties determining the reactivity of alloy catalysts. Extrapolation to further alloy materials were performed based on these findings. Promising alloy catalysts, such as CuSc alloy, were proposed for further investigation.

References 

[1] Olah, G. A. Angewandte Chemie International Edition 2005, 44(18), 2636–2639.   

[2] Gao, B., et al. ChemCatChem 2024, 16(19), e202400814. 

[3] Yang, Y., et al. Journal of Physical Chemistry C 2010, 114(40), 17205–17211. 

[4] El Berch, J. N., et al. Nanoscale 2025, 17, 1936-1953