MOF-Based Nano-Cuboids Electrocatalyst for OER-HER Reactions
Notice bibliographique
Résumé
Recently, metal organic framework (MOF) derived catalysts, typically synthesized using precursors consisting of transition metals and organic linkers, have emerged as promising active and inexpensive electrocatalysts due to their intrinsic advantages of high porosity, three-dimensional structures, and compositional flexibility. In particular, MOFs with organic ligands containing carbon, nitrogen, and sulfur atoms coordinated to transition metal centers have been reported to form three dimensional and uniformly porous structures, resulting in considerably increased active surface area and enhanced physical properties.[1-5] These MOFs, often composed of transition metal atoms such as nickel, cobalt, and iron, and heteroatoms such as nitrogen, sulfur, and phosphorus, are characterized by strong interactions between them[6,7], which can serve as electrocatalytically active sites for HER and OER. The advantages of MOF-derived active materials in advanced energy conversion and storage applications have been recently highlighted as efficient electrocatalysts for fuel cells, metal-air batteries, and electrolyzers. For example, Gadipelli et al. recently reported a design route for the synthesis of MOF-derived electrocatalysts, in which zeolitic imidazolate framework (ZIF) was used as the template. The resulting MOF catalyst containing active Co-N-C species demonstrated efficient ORR and OER activities. Yu et al. reported an active OER electrocatalyst based on porous carbon coated nickel phosphides (NiP) prepared using Ni-based PBA nanoplates as a template. The structural advantages of MOFs allowed NiP catalyst to demonstrate superior electrocatalytic activity towards OER compared to NiO and Ni(OH)2 counterparts. Despite these electrocatalyst developments, however, bi-functional MOF-derived electrocatalysts active towards both HER and OER for water-splitting application have rarely been reported to the best of our knowledge. In this study, we introduce novel MOF containing Ni-Co-Fe transition metal centers, denoted NCF-MOF, as a dual-function water-splitting catalyst for enhancing HER and OER activities. The morphology of the catalyst is revealed to exhibit nano-cuboid structure with multiple meso- and micro-sized pores prepared via a facile synthesis procedure. As MOF catalyst precursors, transition metal-based PBA nanocubes with a chemical formula Mx II[My III(CN)6]z▪H2O, where MII and MIII are divalent and trivalent transition metal cations, respectively, have selected to obtain unique structure and composition of NCF-MOF multi-hollow nano-cuboids. Specifically, PBA nanocube precursors containing nickel, cobalt, and iron have been utilized to maintain the general nano-cuboidal structure, while optimizing the composition of transition metal centers for efficient OER and HER activities. This makes NCF-MOF one of the most promising non-precious electrocatalysts for water-splitting applications in various ways; (i) significantly extended active surface area with high porosity, (ii) formation of active nitrogen species during facile synthesis, and (iii) optimized electronic structure of transition metals for electrocatalysis via oxidation state control. These main electrocatalytic contributors that affect OER and HER have been successfully obtained through careful selection of PBA precursors and the facile synthesis procedure. Furthermore, excellent electrochemical stability has been realized NCF-MOF catalyst which is also attributed to high uniformity of porous nano-cuboidal structure which allow rapid charge transfer and transport of active species. The dual-function of the catalyst and its stability has been confirmed by three-electrode half-cell evaluations, as well as nickel-foam loaded active electrodes for practical demonstrations as efficient and stable References [1] A. Mahmood, W. Guo, H. Tabassum and R. Zou, Adv. Energy Mater., 2016. [2] Z. Li, M. Shao, L. Zhou, R. Zhang, C. Zhang, M. Wei, D. G. Evans and X. Duan, Adv. Mater., 2016, 28, 2337-2344. [3] B. Y. Xia, Y. Yan, N. Li, H. B. Wu, X. W. Lou and X. Wang, Nat. Energy, 2016, 1, 15006. [4] X. Y. Yu, L. Yu, H. B. Wu and X. W. Lou, Angew. Chem. Int. Ed., 2015, 54, 5331-5335. [5] L. Han, X. Y. Yu and X. W. Lou, Adv. Mater., 2016, 28, 4601-4605. [6] T. Y. Ma, S. Dai, M. Jaroniec and S. Z. Qiao, J. Am. Chem. Soc., 2014, 136, 13925-13931. [7] X. F. Lu, P. Q. Liao, J. W. Wang, J. X. Wu, X. W. Chen, C. T. He, J. P. Zhang, G. R. Li and X. M. Chen, J. Am. Chem. Soc., 2016, 138, 8336-8339. Figure 1
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 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) | 0,000 | 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 | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».