Understanding and Designing Oxygen Reduction/Evolution Reaction (ORR/OER) Catalysts By Combining Experimental and Ab-Initio Studies
Notice bibliographique
Résumé
The increased awareness of low-carbon economy and sustainable energy generation continues to push the development of next-generation of energy conversion and storage systems, which aims to alleviate dependence on fossil fuels and reduce carbon emissions that cause global warming (1). There has been a tremendous interest in pursuing both fundamental and applied research towards developing new types of sustainable energy systems such as fuel cells, metal-air batteries and electrochemical water splitting system (1-3). The significant and share reactions of those applications are oxygen reactions which are oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) (1-3). So far, it is well known that the best catalysts are precious metals like Pt and Pt-based electrocatalysts, which are very expensive (especially in acidic media) because of their critical challenges: i) large overpotentials resulting from insufficient drive for sluggish ORR and OER kinetics, and ii) catalyst degradation by relatively unstable electrochemistry in operating condition. These challenges arise from insufficient activity and durability of the air electrode, which includes electrocatalysts that lower activation energies of the reactions to reduce overpotential in working condition (1-3). Hence recent research efforts in energy materials development have been focused on addressing the above challenges (4, 5). Recent approaches in density functional theory (ab-initio studies, DFT) combining with experimental studies have allowed researchers to precisely simulate catalytic activities and gain fundamental understandings of the bifunctional oxygen reactions (4-6). Especially, it enables designing of efficient non-precious material-based catalysts and effectively minimizing the use of noble metals to render sufficiently active low cost catalysts such as non-precious transition metal-based materials, functionalized carbon-based materials, metal–nitrogen complex and noble metals (7). Particularly in the case of minimizing the use of noble metals, Pt3Ni has been revealed to show enhanced ORR activity due to downward shifted d-band center in electronic structure by Nørskov and associates, which results in a weak adsorption with oxygen intermediates on catalytic surface (6). Understanding eg orbital of valence electrons makes it possible to predict the oxygen reactivity that can be controlled by the number of outer electrons of transition metal in non-precious catalysts (4, 8). In addition, the electrochemical stability has been associated with the dissolution potential and cohesive energy term modelled by changing the morphology and size of the transition metal nanoparticles, as well as support materials (9, 10). Accordingly, a synergetic approach using both experimental and ab initio computational studies with physicochemical analyses is required to efficiently and accurately develop a new catalyst with highly improved activity. To apply energy conversion and storage devices such as fuel cells, metal-air batteries systems, in this work, we have predicted the ORR and OER activity and stability for self-assembled nitrogen-doped fullerenes (N-fullerene), and studied the perovskite oxides for the reaction mechanism in aspect of understanding OER activity. In addition, a highly efficient bifunctional oxygen electrocatalyst, combining Pd and three-dimensionally ordered mesoporous spinel cobalt oxide (3DOM Co3O4), has manly discussed in terms of obtaining a stability. This study provides a way of rationally designing efficient electro-catalyst based on the principle that governs thermodynamic and electrochemical activities and stabilities by applying first principles calculations and state of the art experimental measurements to well-defined model systems. M. E. Scofield, H. Liu and S. S. Wong, Chem. Soc. Rev., 44, 5836 (2015). Z.-L. Wang, D. Xu, J.-J. Xu and X.-B. Zhang, Chem Soc Rev, 43, 7746 (2014). J. K. Norskov and C. H. Christensen, Science 312, 1322 (2006). M. H. Seo, H. W. Park, D. U. Lee, M. G. Park and Z. Chen, Acs Catal, 5, 4337 (2015). M. G. Park, D. U. Lee, M. H. Seo, Z. P. Cano and Z. Chen, Small, 12, 2707 (2016). J. Greeley, I. E. L. Stephens, A. S. Bondarenko, T. P. Johansson, H. A. Hansen, T. F. Jaramillo, J. Rossmeisl, I. Chorkendorff and J. K. Nørskov, Nat. Chem., 1, 552 (2009). Z. Chen, D. Higgins, A. Yu, L. Zhang and J. Zhang, Energy Environ. Sci., 4, 3167 (2011). F. Calle-Vallejo, O. A. Díaz-Morales, M. J. Kolb and M. T. M. Koper, ACS Catal., 5, 869 (2015). M. H. Seo, S. M. Choi, E. J. Lim, I. H. Kwon, J. K. Seo, S. H. Noh, W. B. Kim and B. Han, Chemsuschem, 7, 2609 (2014). D. Higgins, M. A. Hoque, M. H. Seo, R. Wang, F. Hassan, J.-Y. Choi, M. Pritzker, A. Yu, J. Zhang and Z. Chen, Adv. Funct. Mater., 24, 4325 (2014).
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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,002 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,002 |
| 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 ».