Electrochemical Promotion of Bimetallic Nanoparticles with Low-Noble Metal Content Interfaced with Yttria-Stabilized Zirconia Solid-Electrolyte
Bibliographic record
Abstract
Heterogeneous catalysts play a central role in production of a variety of useful chemicals, energy generation and greenhouse gas emissions reduction. A heterogeneous catalyst usually consists of fine metal or metal oxide particles in the nanometer range dispersed on a high surface area supports. The catalytic activity and/or selectivity of the catalyst could be tuned in by using chemical promoters that are added to the catalyst during its fabrication or by choosing a suitable catalyst supports via metal-support interaction (MSI) effect. Another way to efficiently promote catalytic activity and in some instances catalyst selectivity is the use of electrochemistry via Electrochemical Promotion of Catalysis (EPOC), also called Non-faradaic Electrochemical Modification of Catalytic Activity (NEMCA) phenomenon [1, 2]. EPOC or NEMCA is ascribed to significant changes in the catalytic properties of metal or metal oxide catalysts deposited on solid-electrolytes caused by the application of small current (in the range of a few μA) or potential (up to 2V). The application of electric stimuli results in the supply of ionic species from the solid-electrolyte to the active surface, where the catalytic reaction takes place. These ionic species act as promotes and their addition results in significant catalytic rate increase due to changes in the catalyst work function [2-4]. This phenomenon has been applied to over 100 catalytic systems and reactions and continues to make an impact to surface science, catalysis and solid-state electrochemistry. The application of EPOC to nanostructured, highly dispersed catalysts has attracted significant interest in the last decade and several studies have demonstrated dramatic EPOC effect with noble metal (Pt, RuO2, Pd, etc.) nanoparticles (NPs) as low as 1.5 nm in size and metal loading down to 0.1 mg metal/cm2 [5-9]. Despite this tremendous progress, further improvement in metal utilization for practical advancement of EPOC is essential. In the present work, we investigated EPOC of bi-metallic, highly dispersed Ni90Pd10 and Ru45Fe55 (at. %) nanoparticles for two environmentally important reactions: CH4 combustion and CO2 hydrogenation. Both CH4 and CO2 are greenhouse gases that have negative impact on the environment and contribute to climate change, therefore methane conversion to CO2 and CO2 transformed to useful chemicals have several advantages. Ni90Pd10 and Ru45Fe55 NPs were synthesized using polyol reduction method by using ethylene glycol as reducing and stabilizing agent. TEM/SEM and XRD analysis were carried out to determent the particle and crystallite size, as well as alloy formation in bi-metallic NPs. The resulting colloidal solution containing NPs were deposited on yttria-stabilized zirconia (8 mol % Y2O3-ZrO2, (YSZ)) solid-electrolyte disk (D = 19 mm, thickness = 1 mm) with loading of 0.2 - 0.3 mg metal/cm2 and served as a catalyst-working electrode. Inert gold counter and pseudo-reference electrode were deposited on the opposite side of YSZ disk. Gold mesh served a current collector for NPs. The solid-electrolyte cell was placed in the CSTR-type quartz reactor (atm. pressure) [9] for CH4 combustion (T = 400 – 500 oC) and CO2 hydrogenation (T = 275 – 400 oC) reactions. Galvanostatic and potentiostatic EPOC transient experiments were carried out under various gas compositions and temperatures. The EPOC results on bi-metallic nanoparticles will be presented and the role of Ni and Fe on the catalytic activity and EPOC efficiency will be discussed. References Stoukides, C.G. Vayenas, J. Catal., 70 (1981), 137-146 G. Vayenas, S. Bebelis, S. Ladas, Nature 343 (1990) 625–627. G. Vayenas, S. Bebelis, C. Pliangos, S. Brosda, D. TsiplakidesElectrochemical Activation of Catalysis: Promotion, Electrochemical Promotion and Metal-support Interactions, Kluwer Academic/Plenum, New York (2001) Vernoux, L. Lizarraga, M.N. Tsampas, F.M. Sapountzi, A. De Lucas-Consuegra, J.L. Valverde, S. Souentie, C.G. Vayenas, D. Tsiplakides, S. Balomenou, E.A. Baranova, Chem. Rev. 113 (2013) 8192–8260. Kambolis, L. Lizarraga, M.N. Tsampas, L. Burel, M. Rieu, J.P. Viricelle, Vernoux Electrochem. Commun., 19 (2012), pp. 5-8. A.E. Dole, E.A. Baranova, Implementation of Nanostructured Catalysts in the Electrochemical Promotion of Catalysis in Handb. Nanoelectrochemistry M. Aliofkhazraei, H.A.S. Makhlouf (Eds.), Springer International Publishing, Cham (2015), 1-27 A.E. Dole, A. Costa, M. Couillard, E.A. Baranova, J. Catal. 333 (2016) 40–50. M. Hajar, K.D. Patel, U. Tariq, E.A. Baranova, J. Catal. 352 (2017) 42–51.
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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".