Nanostructured Ni and Ni-Pd Catalysts for Glycerol Electro-Oxidation in Alkaline Media
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".