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Record W2802784444 · doi:10.1149/ma2018-01/37/2172

(Invited) Electrochemical Methods for Surface Composition Determination of Alloy and Core/Shell Nanoparticles

2018· article· en· W2802784444 on OpenAlexaffabout
Ehab N. El Sawy, Annie Hoang, Jachym Slaby, Viola Birss

Bibliographic record

VenueECS Meeting Abstracts · 2018
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsNanoparticleAlloyMaterials scienceCatalysisChemical engineeringElectrochemistryShell (structure)MetalNanotechnologyChemistryElectrodePhysical chemistryComposite materialMetallurgyOrganic chemistry

Abstract

fetched live from OpenAlex

The surface characteristics of metallic nanoparticles (NPs), especially the atomic percent and distribution of each component, is critical to their catalytic and electrocatalytic behavior. Therefore, efforts are underway to design NP catalysts with full control of their composition, both in the bulk and at the surface. In our work, Pt-Ru and Pt-Ir NPs, either in alloy or core@shell forms, are of great interest, due to their suitability for a wide range of electrocatalytic and sensing applications, such as for the oxidation of formic acid, alcohols, and ammonia, for oxygen evolution and reduction in regenerative fuel cells, in the reduction of hydrogen peroxide, and as an electron mediating matrix in glucose biosensors 1–8. In the present work, PtxIry alloys, Ircore@Ptshell, Rucore@Ptshell, and Rucore@Pt-Irshell NPs were synthesized using the simple and controllable polyol method 9. In the case of PtxIry alloy NPs, two sequential heating steps were employed to minimize the possibility of surface enrichment of Pt or Ir during NP formation. In the case of Ircore@Ptshell and Rucore@Ptshell NPs, the Ptshell, with a coverage of between 0.1 and 2 monolayers (MLs), was controllably deposited on the surface of the Ircore and Rucore NPs, while for the Rucore@Pt-Irshell NPs, one ML of the PtxIry alloy shell, containing different Pt:Ir ratios, was deposited on the Rucore. Wavelength dispersive X-ray spectroscopy (WDS) and energy dispersive X-ray spectroscopy, coupled with high-resolution transmission electron microscopy (EDS/HRTEM) and powder X-ray diffraction (PXRD), were then used to determine the bulk composition and homogeneity of the NPs. To determine the outer surface composition of these NPs, the underpotential deposition/stripping of Cu and oxalic acid oxidation have been used previously, e.g., for Pt-Ru NPs 10–12. However, the effect of NP size, Pt-Ru interactions at the surface, and the degree of Ru oxidation on the catalytic activity have not been determined. In other prior work 13, CO stripping was used to establish the stability of PtRu NPs by tracking the changes in the surface composition. In the present work, several electrochemical fingerprinting methods were developed (underpotential deposition/removal of H atoms, CO stripping, and surface oxide reduction) to determine the precise coverage and thickness (fraction of MLs) of the Ptshell on the Ircore@Ptshell and Rucore@Ptshell, NPs and the Pt:Ir ratio at the surface of the PtxIry alloy and Rucore/Pt-Irshell NPs, as well as the real surface area of the exposed metals. Figure 1 1,14,15 shows the CO stripping voltammetry of the NPs under study here as an example of how the peak potential and splitting correlate with the NP surface composition. A comparison will be given between the surface areas and compositions obtained by each of the electrochemical methods used here, as well as with what can be inferred from TEM imaging methods. References: E. N. El Sawy, H. T. Handal, V. Thangadurai, and V. I. Birss, J. Mater. Chem. A, 4, 15400–15410 (2016) E. N. El Sawy and P. G. Pickup, Electrocatalysis, 7, 1–9 (2016). E. N. E. N. El Sawy, H. A. H. A. El-Sayed, and V. I. V. I. Birss, Phys. Chem. Chem. Phys., 17, 27509–27519 (2015) A. Allagui et al., Int. J. Hydrogen Energy, 38, 2455–2463 (2013). M. Zeng, X. X. Wang, Z. H. Tan, X. X. Huang, and J. N. Wang, J. Power Sources, 264, 272–281 (2014) E. Antolini, Acs Catal., 4, 1426–1440 (2014) P. Holt-Hindle, S. Nigro, M. Asmussen, and A. Chen, Electrochem. commun., 10, 1438–1441 (2008) A. S. Jhas, H. Elzanowska, B. Sebastian, and V. Birss, Electrochim. Acta, 55, 7683–7689 (2010) H. Bonnemann and K. S. Nagabhushana, in Metal Nanoclusters in Catalysis and Materials Science,, p. 21–48, Elsevier, Amsterdam (2008) C. L. Green and A. Kucernak, J. Phys. Chem. B, 106, 1036–1047 (2002) C. N. Van Huong and M. J. Gonzalez-Tejera, J. Electroanal. Chem. Interfacial Electrochem., 244, 249–259 (1988) C. Bock and B. MacDougall, J. Electrochem. Soc., 150, E377–E383 (2003) P. Ochal et al., J. Electroanal. Chem., 655, 140–146 (2011) E. N. El Sawy, H. a El-Sayed, and V. I. Birss, Chem. Commun., 50, 11558–11561 (2014) E. N. El Sawy, thesis, University of Calgary, Canada (2013). 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)0.001
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: Methods · Consensus signal: Methods
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.009

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 head0.018
GPT teacher head0.296
Teacher spread0.278 · 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
GenreMethods

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
Published2018
Admission routes2
Has abstractyes

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