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Record W4412511711 · doi:10.1149/ma2025-01402148mtgabs

CO₂ Reduction at Urea-Treated Mesoporous Carbon Electrocatalysts

2025· article· en· W4412511711 on OpenAlexaboutno aff
Fatemeh Sadat Mousavizadeh Mojarad, Diego Van Der Biest, Scott Paulson, Ahmed Ali, A.P. Singh, Viola Birss

Bibliographic record

VenueECS Meeting Abstracts · 2025
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsnot available
Fundersnot available
KeywordsMesoporous materialReduction (mathematics)UreaCarbon fibersMaterials scienceChemical engineeringChemistryInorganic chemistryNanotechnologyCatalysisOrganic chemistryEngineeringComposite numberMathematicsComposite material

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.006
GPT teacher head0.222
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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