Fe- and Co-Containing Nitrogen-Doped Nanocarbon Catalysts from 5-Methylresorcinol for Anion Exchange Membrane Fuel Cells
Notice bibliographique
Résumé
Replacing fossil fuels with alternative energy sources has a growing scientific and societal interest because of the the environmental concerns related with their utilization. Low-temperature polymer electrolyte fuel cells have become a subject of intense research as the future cornerstone of renewable energy-based economy. Platinum nanoparticles supported on high-area carbon materials (Pt/C) are commonly used as the cathode catalyst in low-temperature fuel cells. However, the high cost of precious metal-based catalysts limits the commercial viability of such fuel cells. Considerable effort has been directed to design efficient and cheap electrocatalysts through the last decades and transition metal-containing nitrogen-doped carbon nanomaterials have shown the most promising results. A simple strategy to prepare these catalyst materials is high-temperature pyrolysis of organic carbon precursors in the presence of nitrogen and metal sources, where carbonization and N-doping occur simultaneously.1 In this work, highly active oxygen reduction reaction (ORR) electrocatalysts from 5-methylresorcinol, dicyandiamide (DCDA) and Fe and/or Co salts were prepared by a simple one-pot synthesis procedure.2 It has been proposed that DCDA polymerizes into graphitic carbon nitride at around 550 °C, which acts as a reactive template during carbonization of organic precursors, decomposing above 750 °C to produce the micro- and macropores necessary for efficient mass transfer and yielding N-doped graphene-like carbon structures.3 The SEM and TEM analysis revealed that the materials are composed of wrinkled carbon structures doped with nitrogen and metals, and also contain metal nanoparticles encapsulated in N-doped carbon layers. According to the XRD analysis, these nanoparticles are mainly composed of Co, Fe/Fe3O4 and FeCo alloy for the CoNC, FeNC and FeCoNC catalysts, respectively. The specific surface area of the materials was between 290 and 410 m2 g−1. The XPS analysis revealed that the catalysts contain metal-coordinated N centers, while pyridinic nitrogen is the most abundant N species in all materials. Electrocatalytic activity for ORR in alkaline media was evaluated via the rotating disk electrode (RDE) method. The ratio of the precursors was optimized and bimetallic catalysts were found to be more active toward ORR than their monometallic counterparts. Acid treatment slightly increased the catalysts’ ORR activity. The acid-treated bimetallic catalyst (FeCoNC-at) showed exceptional ORR performance, comparable to that of commercial Pt/C (20 wt%) (Figure 1a). This can be attributed to the high surface metal and nitrogen contents, which at least partly are present as nitrogen-coordinated metal (M-Nx) centers, as confirmed by inhibition of ORR in the presence of cyanide ions. The catalyst also demonstrated a rather high stability in short-time tests (15000 potential cycles) and good tolerance to methanol. The FeCoNC-at catalyst was further tested in an anion exchange membrane fuel cell (AEMFC) with hexamethyl-p-terphenyl poly(benzimidazolium) (HMP-PMBI) membrane,4 where a high value of peak power density (P max= 415 mW cm–2) was achieved (Figure 1b).2 References Sarapuu, E. Kibena-Põldsepp, M. Borghei, K. Tammeveski, Electrocatalysis of oxygen reduction on heteroatom-doped nanocarbons and transition metal–nitrogen–carbon catalysts for alkaline membrane fuel cells, J. Mater. Chem. A 6, 776–804 (2018). Kisand, A. Sarapuu, D. Danilian, A. Kikas, V. Kisand, M. Rähn, A. Treshchalov, M. Käärik, M. Merisalu, P. Paiste, J. Aruväli, J. Leis, V. Sammelselg, S. Holdcroft, K. Tammeveski, Transition metal-containing nitrogen-doped nanocarbon catalysts derived from 5-methylresorcinol for anion exchange membrane fuel cell application, J. Colloid Interface Sci. 584, 263–274 (2021). -T. Ren, Z.-Y. Yuan, A universal route to N-coordinated metals anchored on porous carbon nanosheets for highly efficient oxygen electrochemistry, J. Mater. Chem. A 7, 13591–13601 (2019). G. Wright, J. Fan, B. Britton, T. Weissbach, H.-F. Lee, E. A. Kitching, T. J. Peckham, S. Holdcroft, Hexamethyl-p-terphenyl poly(benzimidazolium): a universal hydroxide-conducting polymer for energy conversion devices, Energy Environ. Sci. 9, 2130–2142 (2016). Figure 1
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».