(4a) Shuting Xiang

The continuous rise in global CO₂ emissions is a critical factor contributing to global warming. The conversion of CO₂ into fuels not only mitigates its atmospheric release but also produces valuable chemicals for various industrial processes. To achieve CO₂ conversion and utilization, CO₂ reduction can be conducted via electrocatalysis, photocatalysis, and thermal catalysis. Different elements, such as Rh and Co, are utilized as catalysts for CO₂ conversion, such as Rh and Co.

My research interests focus on investigating the structure-function relationship of the catalysis system. Specifically, I have focused on the Rh-based thermal catalytic system supported by a manganese oxide three-dimensional framework, including octahedral molecular sieve structure (OMS2) and octahedral layered structure (OL1). In addition to Rh, different secondary metals, including Na, Zn, and V, are not only used as promoters for CO₂ hydrogenation but also added to stabilize the structure. I have explored different combinations, such as Rh-Na-OL1, Rh-Zn-OL1, Rh-Zn-OMS2, and Rh-V-OMS2, which are very complex catalytic systems.

It is challenging to explore the structure information within this 3D matric system with different elements, especially during reactions. My research not only evaluates the catalytic performance but also employs advanced characterization techniques.¹ Our findings indicate that Rh-Na-OL1 provided the highest CH₄ selectivity of 95% around 250 °C, and generally, OL1 catalysts gave higher CH₄ selectivity compared to OMS2 catalysts, while OMS2 catalysts had initially lower reactivity but better stability, as tested over 48 hours under CO₂ hydrogenation. To elucidate the structure and dispersion of the catalysts, techniques such as temperature-programmed reduction (TPR), diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS), XRD, TEM-EDS, and XAFS were utilized. Our studies showed that Rh existed initially in the Rh³⁺ state under ambient conditions. To understand the local structure change surrounding Rh atoms during CO₂ hydrogenation, in-situ XAFS was performed, showing a change from Rh²⁺ towards Rh¹⁺-Rh⁰ at operating conditions. By leveraging the structural information from in-situ XAFS and the surface species evidenced via in-situ DRIFTS, all at reaction conditions, we are poised to gain a strong understanding of the structure-function relationship for Rh catalysts with controlled nuclearity and discern the kinetic and mechanistic influence of secondary metal addition for CO₂ hydrogenation.

My expertise in XAFS analysis includes not only the convectional XAFS analysis but also the machine learning-assisted XAFS analysis. I mastered the conventional XANES analysis and EXAFS simulation in our studies on different catalysts. I also utilized different machine learning techniques such as principal component analysis (PCA), K-means clustering, linear combination fitting, and neural network to extract the structural information from XANES spectra.²

References

(1) Xiang, S.; Jiménez, J. D.; Posada, L. F.; Rubio, S. J. B.; Khanna, H. S.; Hwang, S.; Leshchev, D.; Suib, S. L.; Frenkel, A. I.; Senanayake, S. D. CO₂ hydrogenation over rhodium cluster catalyst nucleated within a manganese oxide framework. Applied Catalysis A: General 2024, 683, 119845. DOI: https://doi.org/10.1016/j.apcata.2024.119845.

(2) Xiang, S.; Huang, P.; Li, J.; Liu, Y.; Marcella, N.; Routh, P. K.; Li, G.; Frenkel, A. I. Solving the structure of “single-atom” catalysts using machine learning – assisted XANES analysis. Phys Chem Chem Phys 2022, 24 (8), 5116-5124, 10.1039/D1CP05513E. DOI: 10.1039/D1CP05513E.