Synthesis of Nanostructured Cobalt-Based Catalysts for Electrochemical Carbon Dioxide Reduction
Bibliographic record
Abstract
The continuous rise in carbon dioxide (CO2) concentration in the atmosphere is known to be one of the main causes of global climate change. The use of CO2 as a precursor in the production of synthetic fuels and other industrial chemicals offers a way to mitigate climate change. The direct conversion of CO2 to industrial low-carbon chemicals, such as carbon monoxide, formate and methane, using electrochemical approaches has attracted attention [1]. One of the main challenges encountered is overcoming the high activation energy required to convert surface adsorbed CO2 into CO2 •− which leads to high overpotential. The search for a cost-effective non-noble metal has led to the exploration of cobalt-based materials as a viable catalyst for CO2 reduction reaction. Although, Co is widely used as a catalyst for electrochemical water splitting [2], recent findings shows that Co and Co-oxides can lead to the reduction of CO2 to CO and formate with high faradaic efficiencies [3, 4]. In this study, different Co-based nanomaterials including nanoparticles and nanodendrites were synthesized. The formed nanomaterials were studied using a wide range of surface characterization techniques and electrochemical methods. The catalytic activity of the synthesized Co-based nanoparticles and nanodendrites towards the electrochemical reduction of carbon dioxide will be compared and discussed. References: [1] A. S. Agarwal, Y. Zhai, D. Hill, and N. Sridhar, ChemSusChem 4 (2011) 1301–1310. [2] J. Cirone, S. R. Ahmed, P. C. Wood, and A. Chen, J. Phys. Chem. C 123 (2019) 9183 - 9191. [3] G. Yin, X. Yuan, X. Du, W. Zhao, Q. Bi, and F. Huang, Chem. - A Eur. J. 24 (2018) 2157–2163. [4] S. Gao et al. Nature 529 (2016) 68–71.
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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 | 0.001 | 0.000 |
| 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".