Zn-Based Catalysts for Selective and Stable Electrochemical CO<sub>2</sub> Reduction at High Current Densities
Bibliographic record
Abstract
The development of low-cost and stable catalysts is important for lowering the capital and operational cost of CO2 electro-reduction (ECR). Zinc (Zn) is an earth-abundant metal, with promising performance for the CO2-to-CO conversion.1 Zinc oxide (ZnO) has been recently employed for the CO2-to-CO conversion, recording promising selectivity (FECO) but short-term stability, in Flow-Cell configuration.2,3 ZnO phase has been proven critical for competent ECR performance, since both the oxidation state of Zn and the Zn/ZnO interface are proven critical for high FECO. 4,5In our work 6, we have synthesised various ZnO allotropes, the properties of which induced differences in their ECR performance. We have identified the ZnO nanorods (ZnO-NR) as the best performing catalyst. The latter was implemented in a zero-gap ECR electrolyser (MEA), recording partial current density for CO (jCO) of 160 mA cm-2 at cell voltage of 3.6 V. We have correlated the depletion of the ZnO phase in the MEA with the degradation of the performance (initially 15 h stability). We applied a periodic oxidation protocol in the MEA, causing the regeneration of ZnO-phase, allowing us to prolong the life-time of the catalyst. Through our strategy we were able to record 82% CO selectivity (FECO) for over 100 h, at -160 mA cm-2. This work provides an approach of practical use of inexpensive Zn-based catalysts for large-scale ECR applications. (1) Luo, W.; Zhang, J.; Li, M.; Züttel, A. Boosting CO Production in Electrocatalytic CO2 Reduction on Highly Porous Zn Catalysts. ACS Catal. 2019, 9 (5), 3783–3791. https://doi.org/10.1021/acscatal.8b05109. (2) Zeng, J.; Fontana, M.; Sacco, A.; Sassone, D.; Pirri, C. F. A Study of the Effect of Electrode Composition on the Electrochemical Reduction of CO2. Catalysis Today 2021. https://doi.org/10.1016/j.cattod.2021.07.014. (3) Zong, X.; Jin, Y.; Li, Y.; Zhang, X.; Zhang, S.; Xie, H.; Zhang, J.; Xiong, Y. Morphology-Controllable ZnO Catalysts Enriched with Oxygen-Vacancies for Boosting CO2 Electroreduction to CO. Journal of CO2 Utilization 2022, 61, 102051. https://doi.org/10.1016/j.jcou.2022.102051. (4) Nguyen, D. L. T.; Jee, M. S.; Won, D. H.; Jung, H.; Oh, H.-S.; Min, B. K.; Hwang, Y. J. Selective CO2 Reduction on Zinc Electrocatalyst: The Effect of Zinc Oxidation State Induced by Pretreatment Environment. ACS Sustainable Chem. Eng. 2017, 5 (12), 11377–11386. https://doi.org/10.1021/acssuschemeng.7b02460. (5) Geng, Z.; Kong, X.; Chen, W.; Su, H.; Liu, Y.; Cai, F.; Wang, G.; Zeng, J. Oxygen Vacancies in ZnO Nanosheets Enhance CO2 Electrochemical Reduction to CO. Angewandte Chemie International Edition 2018, 57 (21), 6054–6059. https://doi.org/10.1002/anie.201711255. (6) Stamatelos, I.; Dinh, C.-T.; Lehnert, W.; Shviro, M. Zn-Based Catalysts for Selective and Stable Electrochemical CO2 Reduction at High Current Densities. ACS Appl. Energy Mater. 2022. https://doi.org/10.1021/acsaem.2c02557. Figure 1
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".