AAASS Press
Journal of Artificial Intelligence and Interdisciplinary Research

Hydrogen Spillover and Interfacial Oxygen Vacancy Pairing in Cu–ZrOx Catalysts for Selective CO₂ Conversion to Methanol

Read & download PDF
Abstract

Cu–ZrOx catalysts have attracted increasing attention for CO2 hydrogenation to methanol because the oxide–metal perimeter can simultaneously activate CO2-derived intermediates and dissociate hydrogen. In this work, density functional theory calculations combined with microkinetic modeling were used to clarify how hydrogen spillover and oxygen-vacancy pairing regulate methanol formation at Cu–ZrOx interfacial sites. A total of 72 Cu–ZrOx interface models were constructed by varying ZrOx nuclearity, oxygen-vacancy density, and Cu coordination environment, and 186 elementary reaction barriers were calculated for formate, dioxymethylene, methoxy, and reverse water–gas shift pathways. The results show that isolated Zrδ+ centers stabilize HCOO* by 0.34–0.51 eV relative to Cu-only sites, whereas paired oxygen vacancies reduce the HCOO* → H2COO* hydrogenation barrier from 1.12 eV to 0.76 eV. Microkinetic simulations at 523 K and 30 bar indicate that interfaces with moderate vacancy concentration achieve a methanol selectivity of 82.6%, compared with 41.8% on vacancy-poor surfaces. Degree-of-rate-control analysis identifies HCOO* hydrogenation and CH3O* protonation as the two most influential steps, contributing 46% and 31% to the overall rate sensitivity, respectively. Electronic-structure analysis further reveals that hydrogen spillover from Cu to adjacent ZrOx sites increases the Bader charge of interfacial hydrogen by 0.18–0.24 e, strengthening its reactivity toward oxygenated C1 intermediates. These findings provide an atomistic explanation for the activity of inverse Cu–ZrOx catalysts and suggest that controlling vacancy pairing, rather than simply increasing oxide loading, is critical for improving methanol productivity.

Keywords
CO2 hydrogenationmethanol synthesisCu–ZrOx interfacehydrogen spilloveroxygen vacancydensity functional theorymicrokinetic modeling
References
  1. Saha, B., Racha, A., Chaudhary, P. K., Singh, B. K., Samanta, C., & Newalkar, B. L. (2025). Enhanced production and techno-economic analysis of sustainable biofuel production via continuous hydrogenation of furfural using the Cu–ZnO–Al2O3 catalyst. ACS Sustainable Chemistry & Engineering, 13(8), 3183-3199.
  2. Yang, Z., Alexandrova, A. N., & Sautet, P. (2026). Modeling CO2 Hydrogenation to Methanol on an Ensemble of Inverse ZrO2 on Cu Catalytic Sites: Mechanism, Reactivity, and Deactivation. Angewandte Chemie, e5448247.
  3. Chen, F., Liang, H., Li, S., Yue, L., & Xu, P. (2025). Design of Domestic Chip Scheduling Architecture for Smart Grid Based on Edge Collaboration.
  4. Todaro, S., Arena, F., Cannilla, C., Corrente, C., Cajumi, A., Samperi, M., ... & Bonura, G. (2026). Metal-oxide interfaces and oxygen vacancies as dominant active sites in CO2 hydrogenation to methanol: contrasting reactivity of Cu-and In-based functionalities. Applied Catalysis B: Environment and Energy, 126614.
  5. Bao, Y., Qiu, Y., & Wang, H. (2026, May). Unified Identity Layer for Hyperscale Ecosystems: A Framework for Cross-Platform User Attribution and Experimentation. In 2026 6th International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) (pp. 540-543). IEEE.
  6. Zada, H., Yu, J., & Sun, J. (2025). Active sites for CO2 hydrogenation to methanol: mechanistic insights and reaction control. ChemSusChem, 18(4), e202401846.
  7. Liang, R., Feifan, F. N. U., Liang, Y., & Ye, Z. (2025). Emotion-Aware Interface Adaptation in Mobile Applications Based on Color Psychology and Multimodal User State Recognition. Frontiers in Artificial Intelligence Research, 2(1), 51-57.
  8. Osada, W., Ozaki, F., Tanaka, S., Mukai, K., Horio, M., Matsuda, I., ... & Yoshinobu, J. (2025). Spillover hydrogen-driven CO2 hydrogenation on a Pd/Cu (111) single atom alloy model catalyst at room temperature studied by ambient pressure X-ray photoelectron spectroscopy. Physical Chemistry Chemical Physics, 27(43), 23322-23335.
  9. Du, Y. (2025). Research on Digital Quality Traceability System for Temperature-Controlled Supply Chain of Foreign Trade Wine Driven by Blockchain and IoT. Business and Social Sciences Proceedings, 4, 57-65.
  10. Jiao, Y., Zhao, B., Wang, A., & Shi, T. (2026). Construction and Empirical Study of a Modularized Teaching System for Art Courses Based on a Unified Training Pathway.
  11. Kido, G., Ueki, H., Okazaki, M., Kikkawa, J., Kimoto, K., Nishikubo, R., ... & Maeda, K. (2026). Lewis Acid–Base-Driven Anisotropic Crystal Growth of Pyrochlore Pb2Ti2O5. 4F1. 2 with Enhanced Visible-Light H2 Evolution Activity. Chemistry of Materials, 38(4), 1980-1990.
  12. Yang, J. (2026). Stage‐Coupled Computational Framework for Stratified Accessibility and Equity Analysis in Community‐Based Elderly Care Services.
  13. Beniwal, A., Pooniya, S., Libin, V. R., Shekhawat, K., Gurjar, H., Bagaria, A., & Bhalothia, D. (2026). Sub-millisecond laser induced atomic scale surface defects enhance CO₂ hydrogenation on Cu/Ni mixed-oxide supported Pd nanoparticles. Chemical Engineering Journal, 173204.
  14. Wu, J., Wu, D., Zhang, J., & Peng, Y. (2026). Generative AI Feedback in Junior High School Artistic Creation Evaluation. Available at SSRN 7244838.
  15. Boonpalit, K., Namuangruk, S., & Montoya, A. (2026). Interfacial Mechanisms of Hydroxyl Radicals in Electrochemical Advanced Oxidation. ACS Catalysis.
  16. Gu, X. (2026). Identifying Causal Effects and Analyzing Heterogeneity of User Growth Interventions on Digital Platforms: Evidence from Large-Scale Behavioral Data. Available at SSRN 6809181.
  17. Rangarajan, S. (2026). Learning catalytic kinetic models from data: current and emerging methods. Current Opinion in Chemical Engineering, 52, 101240.
  18. You, S. (2026). Verifiable Audit Mechanisms in AI Compliance Automation: Scalability. Available at SSRN 6547458.
  19. Easton, C. D., & Morgan, D. J. (2025). Critical examination of the use of x-ray photoelectron spectroscopy (XPS) O 1s to characterize oxygen vacancies in catalytic materials and beyond. Journal of Vacuum Science & Technology A, 43(5).
  20. Jiao, Y., Shi, T., Zhao, B., & Wang, A. (2026). The Impact of VR Sketching Simulations on the Assessment of Scale and Site Suitability in Public Art Spaces.
  21. Abideen, Z. U., Malik, M., Liu, M., Huang, T., Hou, Q., Wu, W., ... & Liu, H. (2025). Diminutive tuning of lattice oxygen controlled by sulfur-mediated vacancies for oxygen evolution reaction. Journal of Colloid and Interface Science, 699, 138284.
  22. Liang, S., Zhang, X., Du, Y., & Chen, W. (2026). Supply Network Restructuring and Capacity Ramp-Up Limits in the Localized Expansion of High-Performance Computing Equipment Production. Available at SSRN 7339738.
  23. Saini, H., Gangwar, S., Yadav, C. S., & Khatri, M. S. (2025). Structure, microstructure and compositional analysis of electrodeposited Cu-Ni-W/ZrO2 metal matrix nanocomposite coatings. Materials Characterization, 115542.
  24. Bao, Y., & Wang, H. (2026). Edge-to-Cloud Infrastructure Continuum: A Unified Framework for Optimizing Industrial IoT Systems. Available at SSRN 7185578.
Publication details
Journal
Journal of Artificial Intelligence and Interdisciplinary Research
Volume
1 (2026)
Article number
aji20260002
License
CC BY 4.0