Toward Active and Durable ORR/Oer Bifunctional Non-PGM Electrocatalysts: Surfactant-Assisted Electrodeposition of Mn Oxides
Notice bibliographique
Résumé
Development of novel bifunctional electrocatalysts with high electrocatalytic activity and durability for both oxygen reduction and evolution reactions (ORR and OER) is of outmost importance to grasp the full potential of the regenerative H2-O2 fuel cells and rechargeable metal-air batteries (e.g., Zn-air, Al-air, Mg-air, Li-air) as reliable and efficient energy conversion/storage systems. Manganese oxides have been on the spotlight as an attractive catalyst material due to cost competitiveness, environmental friendliness, natural abundance as well as excellent reactivity for ORR and to some extent for OER (1, 2). The physicochemical and electrochemical properties of MnOx are highly dependent on its morphology and crystallographic nature (3). The aim of this work is to provide a systematic study on finding an active nanostructured manganese oxide for both ORR and OER via anodic electrodeposition method. A comprehensive study has been performed to investigate the main and interaction effects of key electrodeposition factors that significantly influence the electrosynthesis of manganese oxides, i.e. Mn2+ concentration (C), applied anodic potential (E), temperature (T), surfactant type and concentration (S), on the bifunctional activity of MnOx using a two-level half-fraction factorial design. Sodium dodecyl sulfate (SDS) as anioinc, hexadecyl-trimethyl-ammonium bromide (CTAB) as cataionic and Triton X-100 as non-ionic surfactants were used in this study to electro-synthesize the nanostructured MnOx. Surface characterization methods including XPS and SEM have been employed to analyze morphology and Mn valance of the synthesized electrocatalysts. Fig. 1 shows the surface plots of three different responses studied here for the electrodeposited manganese oxides in presence of Triton X-100, correlating them to the most important factors and two-factor interactions based on the Pareto plots of estimates. In this case, high surfactant concentration together with low applied anodic potential is believed to bring the best ORR/OER bifunctional performances for the electrodeposited Mn oxides (Fig. 1A and 1B). Mn concentration was found to be an insignificant player. Temperature, on the other hand, is believed to have different effect depending on its value with high temperatures providing low ORR/OER potential window while low temperatures lead to high ORR/OER mass activities (Fig. 1). Triton X-100 samples provide best performing nano-sized structures with promising ORR and OER performances comparing to both noble metals and other non-precious metals, i.e. between 50 to 150 mV lower ORR overpotential (at -2 mA cm-2) comparing to CoMn2O4 and Core-Corona Structured Bifunctional Catalyst (CCBC) and min. 100 mV lower OER overpotential (at 2 mA cm-2) comparing to Ir, Ru and IrO2 (1, 2). Galvanostatic polarizations at 5 mA cm-2 showed low OER potentials of 490 mV (at t=2 hrs), about 40 mV lower than commercial MnOx, and degradation rate of 43 mV h-1, about 10 mV h-1 lower than its commercial counterpart. The surface modifications of MnOx via surfactant-assisted electrodeposition can help destabilizing the HOO(ads) and HO(ads) intermediates, breaking away from the linear scaling relationship between their binding energies as a major contributor to the ORR and OER overpotentials, enhancing the ORR and OER electrocatalytic activity of electrodeposited manganese oxides. The formation of hydrogen-bonded complexes, i.e. HO(ads)…H-OH, with specially configured water molecules called “activated water” (4), can further enhance the ORR activity of the catalysts, depending on the surface coverage of OH(ads) which is needed to provide sites for formation of HO(ads)…H-OH complexes. References: 1. P. H. Benhangi, A. Alfantazi and E. Gyenge, Electrochimica Acta, 123, 42 (2014). 2. P. Hosseini-Benhangi, M. A. Garcia-Contreras, A. Alfantazi and E. L. Gyenge, Journal of The Electrochemical Society, 162, F1356 (2015). 3. Y. Chabre and J. Pannetier, Prog. Solid State Ch., 23, 1 (1995). 4. J. Staszak-Jirkovský, R. Subbaraman, D. Strmcnik, K. L. Harrison, C. E. Diesendruck, R. Assary, O. Frank, L. Kobr, G. K. H. Wiberg, B. Genorio, J. G. Connell, P. P. Lopes, V. R. Stamenkovic, L. Curtiss, J. S. Moore, K. R. Zavadil and N. M. Markovic, ACS Catalysis, 5, 6600 (2015). 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) | 0,001 | 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 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
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 ».