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Enregistrement W4412511711 · doi:10.1149/ma2025-01402148mtgabs

CO₂ Reduction at Urea-Treated Mesoporous Carbon Electrocatalysts

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

Notice bibliographique

RevueECS Meeting Abstracts · 2025
Typearticle
Langueen
DomaineEnergy
ThématiqueElectrocatalysts for Energy Conversion
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMesoporous materialReduction (mathematics)UreaCarbon fibersMaterials scienceChemical engineeringChemistryInorganic chemistryNanotechnologyCatalysisOrganic chemistryEngineeringComposite numberMathematicsComposite material

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,001
Score d'incertitude au seuil0,003

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,006
Tête enseignante GPT0,222
Écart entre enseignants0,216 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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