Nanostructured Ni and Ni-Pd Catalysts for Glycerol Electro-Oxidation in Alkaline Media
Notice bibliographique
Résumé
The great interest of shifting towards biodiesel from vegetable oils or waste fats results in a tremendous increase of glycerol production as an inevitable by-product. This waste sparked the need for glycerol valorization to justify biodiesel as alternative for diesel from petroleum. Our research efforts are to partially electro-oxidize glycerol into value-added chemicals in order to make biodiesel production more environmentally benign with added financial benefits. The primary objective of this project is the identification of an active and stable electro-catalyst that can affect the selective electrooxidation of glycerol to value-added products without C-C-C bond cleavage leads to the formation of a large number of high value-added chemicals [1]. The control of catalyst selectivity and activity could be achieved through formulation of novel, nanostructured electrocatalysts [2]. Nickel is an attractive material for glycerol electrooxidation in alkaline media [3, 4], due to its natural abundance and good stability in alkaline media. Designing nanostructured 3D Ni electrodes could enhance the catalytic activity of Ni, whereas its selectivity could be altered by addition of small amounts of the second metal [5]. Monometallic Ni nanoparticles were synthesized using modified polyol method. Change of NaOH concentration resulted in the variation of Ni nanoparticles (NPs) shape: triangular NiNPs were synthesized at lower concentrations while Ni NPs of urchin-like structure were fabricated at higher NaOH concentration. Bi-metallic NixPdx-1 (x=95,90 and 80 at.%) were also synthesized by hydrazine reduction in the presence of ethylene glycol. The synthesised nanoparticles were characterized by XRD, SEM, TEM, EDS mapping and HAADF imaging. In this work, electro-oxidation of glycerol reaction (GEOR) is investigated in detail on mono- and bimetallic Ni nanoparticles in alkaline medium. In order to better understand the role of the nickel surface on GEOR, electrochemical measurements have been carried out on synthesized unsupported nickel nanoparticles using different electrochemical tests such as cyclic voltammetry (CV), chronoamperometry (CA) and linear sweep voltammetry (LSV). Results indicated that the Ni was able to catalyze the GEOR at the NiOOH surface, well known as the active species. Ni80Pd20 displays the highest activity toward glycerol electro-oxidation. Chronoamperommetry coupled with in-situ polarization modulation infrared-reflection absorption spectroscopy (PM-IRRAS) for the simultaneous analysis of products on the Ni surface and in the bulk solution showed that the main reaction products on Ni surface are glyceraldehyde, carbonyl, carboxylate ions and some carbon dioxide. The correlation of electrocatalytic activity and selectivity with nanoparticle shape, size, surface and bulk composition, as well as structure of Ni-based nanoparticles will be discussed in terms of mass activities and product distribution during GEOR. References Behr, J. Eilting, K. Irawadi, J. Leschinski, F. Lindner, Green Chemistry 10 (2008) 13. M. Simoes, S. Baranton, C. Coutanceau, Applied Catalysis B: Environmental110 (2011) 40. M.S.E. Houache, E. Cossar, S. Ntais, E.A. Baranova, J. Power Sources 375 (2018) 310. V.L. Oliveira, C. Morais, K. Servat, T.W. Napporn, G. Tremiliosi-Filho, K.B. Kokoh, Electrochim.Acta. 117 (2014) 255. M. Simoes, S. Baranton, C. Coutanceau, ChemSusChem 5 (2012) 2106.
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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) | 0,000 | 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 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».