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Record W2507412263 · doi:10.1149/ma2016-02/21/1609

Electrodeposition and Characterization of Pt(100) Nanostructures

2016· article· en· W2507412263 on OpenAlexaff
Erwan Bertin, Sébastien Garbarino, Brunet Magali, David Pech, Daniel Guay

Bibliographic record

VenueECS Meeting Abstracts · 2016
Typearticle
Languageen
FieldChemistry
TopicElectrochemical Analysis and Applications
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsCatalysisElectrochemistryMaterials sciencePlatinumNanoparticleNanotechnologyPlatinum nanoparticlesChemical engineeringDeposition (geology)NanostructureElectrocatalystColloidCharacterization (materials science)ChemistryElectrodeOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

In the last decade, approaches to increase the electrocatalytic activity of platinum based catalysts have focus on decreasing the particle size or using nanostructures to increase the surface to mass ratio. Significant improvements were also obtained by mixing Pt with various elements to form alloys and improve the poisoning tolerance to CO and other oxygenated compounds.[1, 2] However, an increasingly promising approach has focus on taking advantages of the surface sensitivity of electrochemical reactions to use catalysts with a specific surface structure, such as single crystals, shape-controlled nanoparticles or preferentially oriented films to increase the activity of a given catalyst.[3, 4] Unfortunately, the preparation and use of these catalysts is not trivial. Single crystals have a very well defined surface structure, but their low electrochemical surface area limits the interest of such structure to fundamental studies. Nanoparticles offer a promising surface to mass ratio, but are often prepared by colloidal methods. The ligand shell employed in the synthesis can therefore be challenging to remove without altering the surface structure. However, it was recently shown that platinum films could be prepared by potentiostatic deposition in the absence of any organic surfactants. The blank voltammogram of such film (Fig.1) clearly displays features associated with a preferential (100) orientation, namely a h2 peak higher then h1, as well as the presence of h3, associated with (100) terraces. In this study, we will focus on influence of some of the parameters influencing the electrodeposition of such preferentially (100) oriented films. The influence of parameters such as the deposition potential, charge and the nature of the Pt salt used have a critical influence on the fraction of (100) and (111) sites that can be obtained. The analysis of this films will be discussed in light of the results obtained by bismuth irreversible adsorption and deconvolution of the hydrogen desorption region, according to the method described by Solla-Gullón et al.[5] A fine tuning of the deposition parameters can allow one to control the total fraction of (100) sites (up to 47%) and the fraction of Pt atoms in (100) terraces. The fraction of (111) sites can also be adjusted and reached a maximum value of 23 %.[5, 6] The advantages of these structures for electrocatalytic reactions (ammonia, formic acid oxidation) will also be discussed. Finally, in the last part of our work, we have studied the electrodeposition of such kind of preferentially oriented nanostructures through a homemade, porous AAO membrane. The parameters influencing the membrane preparation will be briefly discussed, before introducing the new results we obtained in the preparation of nanowires and nanotubes with a preferential (100) orientation. References [1] H.A. Gasteiger, N. Markovic, P.N. Ross, E.J. Cairns, J. Phys. Chem., 97 (1993) 12020-12029. [2] E. Antolini, J.R.C. Salgado, E.R. Gonzalez, Appl. Catal., B, 63 (2006) 137-149. [3] J. Solla-Gullón, F.J. Vidal-Iglesias, J.M. Feliu, Annu Rep Prog Chem Sect C, 107 (2011) 263-297. [4] S. Garbarino, A. Ponrouch, S. Pronovost, J. Gaudet, D. Guay, Electrochem. Commun., 11 (2009) 1924-1927. [5] J. Solla-Gullon, P. Rodriguez, E. Herrero, A. Aldaz, J.M. Feliu, Phys Chem Chem Phys, 10 (2008) 1359-1373. [6] E. Bertin, S. Garbarino, D. Guay, J. Solla-Gullón, F.J. Vidal-Iglesias, J.M. Feliu, J. Power Sources, 225 (2013) 323-329. [7] E. Bertin, C. Roy, S. Garbarino, D. Guay, J. Solla-Gullón, F.J. Vidal-Iglesias, J.M. Feliu, Electrochem. Commun., 22 (2012) 197-199. 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)0.001
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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.005
GPT teacher head0.211
Teacher spread0.205 · 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".

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Citations0
Published2016
Admission routes1
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