Electrodeposition and Characterization of Pt(100) Nanostructures
Bibliographic record
Abstract
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
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
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".