CO₂ Reduction at Urea-Treated Mesoporous Carbon Electrocatalysts
Bibliographic record
Abstract
Carbon dioxide (CO2) is a major greenhouse gas, with its rapidly increasing atmospheric concentration having become a critical driver of global climate change, including rising temperatures. Electrochemical CO2 reduction offers a compelling solution to mitigate this challenge by converting CO2 into valuable chemicals and fuels. Among the various CO2 reduction products, carbon monoxide (CO) holds significant industrial importance as a key feedstock for producing fuels via the catalytic Fischer-Tropsch process. However, the electrocatalytic reduction of CO2 presents persistent challenges, such as the instability of catalyst morphology over time, reliance on costly and scarce metals, and limited selectivity for CO2 reduction products. Carbon-based catalysts are becoming recognized for their effectiveness in CO2 reduction due to several inherent advantages, including customizable and stable porous structures, high surface area, and low cost. Although pure carbon is inactive toward CO2 reduction reaction (CO2RR), introducing defects through heteroatom doping of the surface can enhance its catalytic properties. For instance, nitrogen doping alters the charge distribution within the carbon structure, thereby improving the catalyst activity and selectivity for CO formation. We have been developing a new class of mesoporous carbon materials, known as colloid-imprinted carbon (CIC) powders, which are fully tunable and monodisperse. These materials are engineered with precise and monodisperse pore sizes, ranging from 10 to 100 nm, providing exceptional structural uniformity. The unique mesoporosity of the CICs offers distinct advantages vs traditional microporous carbons, such as improved accessibility to both gaseous and liquid phases within the pores. Additionally, their highly defect-rich surfaces make them ideal candidates for functionalization and customization, enabling a broad range of applications in catalysis and other fields. The CICs are synthesized by combining dry silica nanoparticles of the desired size with mesophase pitch, followed by carbonization at 900 °C for 2 hours. Afterwards, the silica is removed by treatment with a NaOH solution, resulting in the final CIC product [1]. In a previous study, we synthesized nitrogen-doped CICs-85 by treating CIC with ammonia gas at 800 °C for 7 hours. An 85 nm pore size was selected in this work for its relatively large mesopore size, which facilitates efficient solution flow and enhances mass transport during electrochemical reactions. It was found that these N-doped-CIC-85 catalysts show good activity for CO2 reduction, but that their CO selectivity is limited to a maximum of 50%, while also lacking long-term stability [2]. Therefore, here, we employed a different approach for the preparation of nitrogen-doped CICs by using urea as the nitrogen source. Urea was chosen for its high nitrogen content and its ability to decompose during heat treatment, forming uniformly distributed active nitrogen species throughout the carbon matrix. The urea-derived N-doped CIC catalysts were prepared by sonicating a mixture of the CIC-85 powder with urea to ensure uniform dispersion, drying, and then grinding into a fine powder. The powder was then heat-treated at 700 °C for 2 h. X-ray photoelectron spectroscopy (XPS) confirmed the successful incorporation of nitrogen species at approximately 2 at% of the bulk material, which translates to a much higher value at the surface where the doping is done. A high proportion of pyrrolic and pyridinic nitrogen was produced, both known to be catalytically active for CO2 reduction, compared to graphite nitrogen. Energy-dispersive X-ray spectroscopy (EDS) mapping further demonstrated the uniform distribution of nitrogen dopants on the pore surfaces of the CIC-85 material. Electrochemical testing of the nitrogen-doped CIC catalysts was performed in an H-cell configuration using a CO2-saturated 0.5 M potassium bicarbonate (KHCO3) as the electrolyte. CO generation was seen at overpotentials as low as 0.29 V, with the CO selectivity, being as high as 100%, evaluated at a range of potentials by analyzing the products formed in the headspace during electrolysis at specific potentials with gas chromatography. The stability at the potential that exhibited the highest CO selectivity was also investigated. Future studies will aim at further optimization of the synthesis parameters, including heat treatment temperature, the ratio of carbon to nitrogen, and the pore size of the CICs, to further enhance the nitrogen content and improve selectivity at high currents. Acknowledgements: Many thanks to Dr. Christine Li for her guidance and suggestions, and to the Natural Sciences and Engineering Research Council of Canada (NSERC) and CANSTOREnergy for the financial support of this work. Banham, D., et al., Catalysts, 2015. 5(3): p. 1046-1067. Li, J., Tuning the Catalytic Performance of Nitrogen-and Iron-Nitrogen-Doped Mesoporous Carbons for CO2 Reduction. 2024.
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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 | 0.000 | 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".