Electrochemical Modification of Ni-Rich Bimetallic Ni<sub>1-x </sub>M<sub>x</sub> (M= Bi, Pd and Au) Nanoparticles for Glycerol Electro-Oxidation in Alkaline Media
Bibliographic record
Abstract
Glycerol partial electro-oxidation to produce various chemicals has attracted a great amount of attention in the decade [1]. Glycerol is the main by-product of biodiesel production and could be converted into high value-added chemicals. The control of catalyst selectivity and activity could be achieved through formulation of novel, nano-structured electrocatalysts. Nickel (Ni) is an attractive material for glycerol electro-oxidation (GEOR) in alkaline media [2], due to its natural abundance, anti-poising and good stability in alkaline media. Designing inexpensive nano-structured 3D Ni electrodes could improve the catalytic activity of Ni, whereas its selectivity could be modified by adding second metal [1]. In the present work, GEOR was investigated on Ni-based mono and bi-metallic Ni x Au 1-x, Ni x Bi 1-x and Ni x Pd 1-x (x = 98, 95 and 90 at. %) nanoparticles in 1M KOH. Ni nanoparticles were synthesized by heatless chemical reduction in ethanol using sodium borohydride (NaBH4) as the reducing agent. Resulting Ni-based NPs were characterized using XRD, XPS, TEM, SEM, EDS mapping, HAADF imaging and EELS. Electrochemical measurements were carried out on Ni-based NPs using cyclic voltammetry (CV), chronoamperometry (CA) and linear sweep voltammetry (LSV) at room temperature. Results showed that Ni, NiAu and NiBi NPs are active for GEOR and that reaction proceeds through the formation of NiOOH active phase similar to bulk Ni electrodes [3]. Coupling CA with in-situ polarization modulation infrared-reflection absorption spectroscopy (PM-IRRAS) for the simultaneous analysis of products in the bulk electrolyte solution and on the Ni surface demonstrated that the main reaction products on Ni surface are glyceraldehyde, carbonyl and carboxylate ions. Whereas HPLC analysis reveals that Au, Bi and Pd adatoms strongly influence the reaction products by suppressing the pathways with C–C bond cleavage, hindering the formation of formate and enhancing the production of C2 and C3 products at different applied potentials (1.3, 1.4 and 1.55 V) and temperatures (25 and 50 oC). The correlation of electrocatalytic activity and selectivity with nanoparticle will be discussed in terms of mass activities and product distribution during GEOR. References [1] M. S. E. Houache, K. Hughes, E. Baranova, Sustainable Energy & Fuels, 3 (2019) 1892-1915. [2] M. S. E. Houache, K. Hughes, A. Ahmed, R. Safari, H. Liu, G. A. Botton, E. A. Baranova, ACS Sustainable Chemistry & Engineering, 7 (2019) 14425-14434. [3] M. S. E. Houache, E. Cosar, S. Ntais, E. A. Baranova, J. Power Sources, 375 (2018) 310-319.
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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".