N-Doped Nanoporous Carbon Scaffold As an Electrocatalyst for CO<sub>2</sub> Reduction
Bibliographic record
Abstract
The rising level of CO2 in the atmosphere poses a major threat to our global climate [1]. Renewable energy are promising alternatives but the utilization of renewable energy is challenging because of its intermittency. The key solution is to develop an energy storage system that can store energy and then release it as needed. CO2 reduction reaction (CO2RR) uses abundant CO2 present in the atmosphere and renewable energy as the input power. Therefore, increasing interest has been focused on electrochemical routes to transform CO2 into useful products. However, the reduction of CO2 is thermodynamically and kinetically unfavorable. To overcome the energy barrier of CO2RR, the development of high efficiency and high selectivity catalysts is a key goal of CO2RR research. Metallic catalysts have attracted much attention for CO2RR and have achieved some successes. However, most metallic catalysts exhibit large CO2RR overpotentials and insufficient selectivity. Also, the high price of noble metals is a key obstacle to scale-up and commercialization of these materials for CO2RR. Carbon is a very promising candidate to advance CO2RR due to its high specific surface areas and good conductivity. However, carbon atoms are electrically neutral and therefore it is difficult to activate the CO2 molecules and adsorb the intermediate. Therefore, it is necessary to develop novel carbon catalysts to enhance their catalytic activity for CO2RR. Nitrogen is the most commonly used carbon doping atom due to its high electronegativity, which leads to polarization of the adjacent carbon atoms, thus enhancing the electronic/ionic conductivity [2]. Many carbon materials, such as carbon nanotubes and graphene, have been doped with N and investigated as CO2RR catalysts [3][4], with some N-doped materials exhibiting a 85% Faradaic efficiency towards CO production[5]. In this work, a nitrogen-doped templated nanoporous carbon scaffold (N-doped NCS) was investigated as a catalyst material for electrochemical CO2 reduction. The NCS is a novel, templated, binder-free, self-supported, fully tunable mesoporous carbon material[6] that gives a high active site density and good conductivity. NCS material, having a pore size of either 12, 50 or 85 nm, was heated in NH3 gas at 700 °C for 7 hours to prepare N-doped NCS. SEM and TEM were used to confirm the NCS morphology, while XPS, EDX and elemental analysis were used to determine the N content of the NCS material. The electrochemical performance of the N-doped NCS was carried out first using CV in CO2 sat. 0.1 M KHCO3 in a glass cell. After that, a membrane electrode assembly (MEA) CO2 electrolyzer was used to determine the CO2 reduction activity. An N-doped NCS (IrO2-coated) was used as the anode and an anion exchange membrane (AEM) was used as the separator during CO2 electrolysis, with humidified CO2 gas used at the cathode side. The gas products were collected from the cathode outlet and injected into a gas chromatography system for analysis. No liquid products were observed in the solution that was released to the cell outlet. The performance of N-doped NCS will be presented based on the results obtained in various solution-flow cell configurations. Effect of N-doped NCS preparation optimization will also be discussed. Based on both the CV results and the MEA CO2 electrolyzer data, the onset potential of CO2RR was comparable to what has been reported by others for N-doped carbons, but the high internal surface area of the NCS, combined with its high extent of N doping, may give the N-doped NCS some advantages. A maximum 90% FECO was achieved and the stability of the catalytic material was also studied in the flow cell systems. References [1] C. Costentin, M. Robert, and J.-M. Savéant, “Catalysis of the electrochemical reduction of carbon dioxide,” Chem. Soc. Rev., 2013. [2] T. Zheng, K. Jiang, and H. Wang, “Recent Advances in Electrochemical CO2 -to-CO Conversion on Heterogeneous Catalysts,” Adv. Mater., vol. 30, no. 48, p. 1802066, Nov. 2018. [3] X. Wang et al., “Emerging Nanostructured Carbon-based Non-precious Metal Electrocatalysts for Selectively Electrochemical CO2 Reduction to CO,” J. Mater. Chem. A, 2019. [4] H. Cui, Y. Guo, L. Guo, L. Wang, Z. Zhou, and Z. Peng, “Heteroatom-doped carbon materials and their composites as electrocatalysts for CO2 reduction,” J. Mater. Chem. A, vol. 6, no. 39, pp. 18782–18793, 2018. [5] T. Ma et al., “Heterogeneous electrochemical CO2 reduction using nonmetallic carbon-based catalysts: current status and future challenges,” Nanotechnology, vol. 28, no. 47, p. 472001, Nov. 2017. [6] Birss, Viola, L. I. Xiaoan, and Dustin Banham. "Porous carbon films." U.S. Patent Application No. 15/124,847.
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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 | 0.000 | 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".