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Record W2609385175 · doi:10.1149/ma2017-01/33/1612

Electrochemical Promotion of Propene Combustion on Ag Catalytic Coatings

2017· article· en· W2609385175 on OpenAlexaff
I. Kalaitzidou, Thomas Cavoué, A. Boréave, Elena A. Baranova, Mathilde Rieu, J.P. Viricelle, David Horwat, P. Vernoux

Bibliographic record

VenueECS Meeting Abstracts · 2017
Typearticle
Languageen
FieldEngineering
TopicGas Sensing Nanomaterials and Sensors
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsCatalysisPropeneMaterials scienceElectrochemistryElectrolyteChemical engineeringInorganic chemistryElectrodeChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The non-faradaic electrochemical modification of catalytic activity (NEMCA effect) or the electrochemical promotion of catalysis (EPOC) has been investigated thoroughly for more than 120 catalytic reaction systems [1,2]. In electrochemical promotion studies, the conductive catalyst-electrode is in contact with an ionic conductor and the catalyst is electrochemically promoted by applying a current or potential between the catalyst film and a reference electrode. Numerous surface science and electrochemical techniques have shown that EPOC is due to the electrochemically controlled migration of promoting or poisoning ionic species (O2-in case of YSZ) between the ionic conductor and the gas exposed catalytic surface [1]. Propene is one of the major unburnt hydrocarbons containing in diesel cars exhausts. The most effective materials for propene combustion are Platinum-Group Metals. In spite of their high efficiency, these catalysts cannot be considered in the medium-term due to their excessive cost, which makes necessary stages of recovery and recycling [1]. Ag-based catalysts represent a possible alternative. This study reports the electrochemical promotion of the propene combustion on Ag films deposited on 8 mol% Y2O3 stabilized ZrO2 solid electrolyte, an O2-ionic conductor. Nanostructured electrochemical catalysts were prepared by screen-printing and reactive Physical Vapor Deposition (PVD) method. Screen-printing technique is a flexible tool to prepare few µm thick porous films at low cost whereas extremely thin coatings of Ag can be produced by PVD. Thickness and porosity of Ag coatings were modified by changing the deposition parameters (duration and pressure for PVD, nature of the ink and calcination temperature for screen-printing) to optimize the catalytic properties. Catalytic and electrocatalytic tests have been carried out in a quartz reactor [3] which operated under continuous flowing conditions at atmospheric pressure. The catalytic activity was monitored in a temperature range of 100 to 400oC under lean-burn conditions, as encountered in Diesel exhausts. The most active Ag films were also evaluated under closed circuit conditions (± 2V) in order to measure the effect of polarisation between the silver working electrode and an Au reference electrode. Both electrodes were exposed to the same atmosphere in a single chamber configuration. The catalytic activity of samples is depicted in Figure 1. All Ag coatings are effective from around 200°C. The catalytic performances were correlated with the microstructure of the films. Furthermore, the propene combustion was found to be electropromoted on the most active Ag films at low temperature with Faradaic efficiencies larger than 100. References: [1] C.G. Vayenas, S. Bebelis, C. Pliangos, S. Brosda and D. Tsiplakides, Electrochemical Activation of Catalysis: Promotion, Electrochemical Promotion, and Metal-Support Interactions, Kluver Academic / Plenum Plublishers, New York, 2001. [2] A. Katsaounis, J. Applied Electrochemistry 40 (2009) 885-902. [3] P. Vernoux, F. Gaillard, L. Bultel, E. Siebert and M. Primet, Journal of Catalysis 208 (2002) 412-421. Figure 1

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.017
GPT teacher head0.227
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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