Isotope-Resolved Transient Analysis of Formate Turnover on Reducible Oxide–Copper Interfaces

Oliver Bennett; Emily Carter

Abstract

Surface formate is widely observed during catalytic CO2 conversion, but its kinetic role remains debated because steady-state spectra alone cannot distinguish spectator species from reactive intermediates. Herein, isotope-resolved transient analysis was used to quantify formate turnover on reducible oxide–copper interfaces under methanol-forming conditions. Oxide-modified Cu catalysts containing Zr, Ce, and Zn surface domains were prepared by sequential wet impregnation and reduced at 300 °C before reaction. Transient switching experiments using 13CO2/H2, 12CO2/D2, and 13CO2/D2 were conducted at 230–260 °C and 3.0 MPa, combined with operando DRIFTS, online mass spectrometry, and kinetic isotope effect analysis. Among the tested catalysts, the Zr-modified Cu sample showed the fastest 13C incorporation into methanol, reaching 90% isotopic exchange within 11.6 min, compared with 24.8 min for Zn-modified Cu and 19.3 min for Ce-modified Cu. The apparent formate consumption rate on Zr–Cu interfacial sites was 2.7 times higher than that on unmodified Cu, while the surface formate lifetime decreased from 41.2 to 15.4 s. Deuterium substitution reduced the methanol formation rate by a factor of 2.4, confirming that hydrogen addition to formate-derived intermediates is kinetically significant. Operando spectra showed that reactive bidentate formate bands at 1588 and 1372 cm⁻¹ decayed synchronously with the appearance of 13CH3OH, whereas strongly bound carbonate species remained largely unchanged during isotope switching. These results demonstrate that only a fraction of the observed formate pool participates directly in methanol formation and that reducible oxide–copper interfaces accelerate the turnover of this reactive formate fraction. The findings offer kinetic evidence for the ensemble-site mechanism of oxide-promoted copper catalysts.

Keywords

formate turnoverisotope transient analysisreducible oxidecopper catalystoperando DRIFTSkinetic isotope effectmethanol formation

References

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Publication details

Journal: AI Frontiers in Science and Society
Volume: 1 (2026)
Article number: osm20260002
License: CC BY 4.0

[1] Stamatelos, I., Guo, Z., Pasel, J., & Saei, W. (2026). Oxide-derived metals and alloys: from dynamic reconstruction to function across electrocatalytic reactions. Energy Advances.

[2] 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.

[3] Anouar, A., Dhakshinamoorthy, A., Xu, F., Navalon, S., Primo, A., Yu, J., & Garcia, H. (2026). Engineering MXenes for Thermal and Photothermal Catalysis. Chemical Reviews, 126(6), 3664.

[4] 12.Chen, F., Liang, H., Li, S., Yue, L., & Xu, P. (2025). Design of Domestic Chip Scheduling Architecture for Smart Grid Based on Edge Collaboration.

[5] Yang, J. (2026). Stage‐Coupled Computational Framework for Stratiﬁed Accessibility and Equity Analysis in Community‐Based Elderly Care Services.

[6] Shakil, R., Akhe, A. S., Wahid, F., Imran, A. B., & Chowdhury, A. N. (2026). Carbon‐based Solar Fuels: Methanol, Ethanol, and Beyond. Solar Fuels: Technologies and Applications for a Sustainable Energy Future, 1, 289-324.

[7] Wu, J., Wu, D., Zhang, J., & Peng, Y. (2026). Generative AI Feedback in Junior High School Artistic Creation Evaluation. Available at SSRN 7244838.

[8] 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.

[9] Baumgarten, L., Kusumowidagdo, E., Klag, L., Schulte, M., Bauer, E. N. O. A., Zevaco, T. A., ... & Grunwaldt, J. D. (2026). Tuning Cu-Zn-Mg Catalysts by Flame Spray Pyrolysis for CO2-to-Methanol synthesis. Catalysis Science & Technology.

[10] 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.

[11] You, S. (2026). Verifiable Audit Mechanisms in AI Compliance Automation: Scalability. Available at SSRN 6547458.

[12] Maity, S., Pal, P., Singh, A. K., Prakash, A., Kondratowicz-Maciejewska, K., Prus, P., & Sarangi, P. K. (2026). Valorisation of Food Processing Wastes into High-Value Platform Chemicals: Industrial Pathways and Circular Bioeconomy Perspectives. Resources, 15(8), 98.

[13] 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.

[14] Luchowski, M., Pophali, A., Kwon, G., Park, J., Kwon, E. E., Halada, G., & Kim, T. (2026). Influence of thermal treatment on structure and catalytic performance of ceria-zirconia supported copper oxide (CuOx/CeyZr1-yO2) catalysts for CO oxidation. Applied Catalysis A: General, 120911.

[15] 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.

[16] LaCour, R. A., Heindel, J. P., Zhao, R., & Head-Gordon, T. (2025). The role of interfaces and charge for chemical reactivity in microdroplets. Journal of the American Chemical Society, 147(8), 6299-6317.

[17] 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.

[18] Sajnani, S., Memon, M. A., Memon, S. A., Kumar, A., Mehvish, D., Ameen, S., ... & Liu, Y. (2025). CO2 to methanol conversion: a bibliometric analysis with insights into reaction mechanisms, and recent advances in catalytic conversion. Processes, 13(2), 314.

[19] 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.

[20] Dharmalingam, B. C., Mureddu, M., Ferrara, F., Pettinau, A., Aghalayam, P., Kaisare, N. S., & Varghese, J. J. (2025). Cu facet-dependent activity of Cu/ZnOx catalyst during methanol synthesis from industrially relevant CO-CO2 gas mixtures. Applied Catalysis B: Environment and Energy, 371, 125213.

[21] 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.

[22] Galliano, G., Bracciotti, E., Jouve, A., Braglia, L., Calligaro, R., Borfecchia, E., ... & Bordiga, S. (2025). Understanding Surface Properties in CeO2 Catalysts for the Synthesis of Dimethyl Carbonate: A Combined In Situ IR and NEXAFS Study. The Journal of Physical Chemistry. C, Nanomaterials and Interfaces, 129(44), 19794.

[23] BAYDIR, E., & Ömür, A. R. A. S. (2026). Mixing Effect and Zirconia Promotion in Disk-Type Reactors for Methanol Steam Reforming Using Spray-Pyrolyzed Cu/ZnO–ZrO2 Thin-Film Catalysts. Journal of Analytical and Applied Pyrolysis, 107935.

[24] Bao, Y., & Wang, H. (2026). Edge-to-Cloud Infrastructure Continuum: A Unified Framework for Optimizing Industrial IoT Systems. Available at SSRN 7185578.