2025 AIChE Annual Meeting

(526g) Microkinetic Modelling for Electrochemical C-N Coupling By Metal-Organic Materials

Authors

Yuting Xu - Presenter, University of Massachusetts Lowell
Gregory Foley, Johns Hopkins University
Lan On, Johns Hopkins University
Jiaqi Yang, University of Massachusetts Lowell
Sara Thoi, Johns Hopkins University
Fanglin Che, University of Massachusetts Lowell
The electrochemical synthesis of urea (CO(NH₂)₂) from CO₂ and nitrate (NO₃⁻) offers a sustainable route for nitrogen–carbon coupling under ambient conditions, powered by renewable electricity. In this work, copper-based boron imidazolate frameworks (Cu-BIFs) are explored as electrocatalysts. The Cu active sites within the BIF scaffold promote the co-adsorption and reduction of CO₂ and NO₃⁻ to carbon monoxide (*CO) and hydroxylamine (*NH₂OH), which subsequently couple to form urea.

To bridge atomic-scale energetics and macroscopic activity, we integrated reaction thermodynamics and kinetic barriers—calculated via density functional theory (DFT)—into a microkinetic modeling (MKM) framework. The MKM predicts product selectivity and activity as functions of applied potential. Urea and NO₂⁻ formation peak at –0.10 VRHE and –0.20 VRHE, respectively, while NH₃ peaks at –0.55 VRHE, following volcano-type trends. These predictions align well with recent experimental findings [1,2].

Sensitivity analysis reveals that while the initial proton-electron transfers to activate CO₂ and NO₃⁻ are rate-limiting, the *CO–*NH₂OH coupling step dictates selectivity. This step promotes urea formation while suppressing NH₃ and NO₂⁻ byproducts. These insights simplify the complex reaction network into a few key steps, offering a focused pathway for catalyst design.

To generalize this approach, we propose employing active machine learning to identify new candidates across a broader space of metal–BIF (M-BIF) materials. Experimental validation is ongoing to confirm predictions and accelerate the development of high-efficiency electrocatalysts for urea production from waste carbon and nitrogen sources.

Figure 1. (a) MKM predictions for urea, NH₃, and NO₂⁻ FEs. (b) Sensitivity analysis highlighting key rate- and selectivity-determining steps.

References
[1] Yu, X., Xu, Y., Li, L. et al., Nat. Commun. 15, 1711 (2024).

[2] Gerke, C. S., Xu, Y., Yang, Y. et al., J. Am. Chem. Soc. 145, 26144–26151 (2023).