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Record W2306109469 · doi:10.1149/ma2014-01/20/865

Probing the Surface Area of Nickel Catalyst Materials: The Effect of Oxalate Adsorption on the Electrochemistry of Nickel in Basic Media

2014· article· en· W2306109469 on OpenAlexaffabout
David S. Hall, Christina Bock, B. MacDougall

Bibliographic record

VenueECS Meeting Abstracts · 2014
Typearticle
Languageen
FieldChemistry
TopicElectrochemical Analysis and Applications
Canadian institutionsNational Research Council CanadaUniversity of Ottawa
Fundersnot available
KeywordsNickelCyclic voltammetryCatalysisMaterials scienceInorganic chemistryElectrolyteElectrochemistryElectrodeChemical engineeringOxalateAdsorptionChemistryMetallurgyOrganic chemistryPhysical chemistry

Abstract

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INTRODUCTION Nickel and nickel-based materials find widespread applications as secondary battery anodes, supercapacitors, electro-catalysts, photocatalysts, and photovoltaic cell components (e.g., [1]-[3]). In alkaline media, nickel-based electrodes are effective catalysts for the hydrogen and oxygen evolution reactions (HER, OER), by which hydrogen may be renewably produced as fuel or industrial feedstock. However, accurate methods to measure the electrochemically active surface area (ECSA) for nickel-based electrodes have not yet been established [4]. The ECSA directly affects the catalytic activity and electrode capacitance, hence, a reliable method for the measurement of the ESCA is desirable. In this work, a new method to probe the surface area of metallic nickel electrodes utilizing voltammetry will be presented. The results are compared to alternative ECSA measurement techniques in terms of accuracy, precision and practical applications for nickel-based catalyst materials. RESULTS AND DISCUSSION Ni electrodes were mechanically polished using 320 grit SiC paper, 9 and 3 μm polycrystalline diamond pastes and a 50 nm Al2O3 suspension. Electrochemical measurements were collected in 0.1 or 1 M KOH with and without the addition of 0.001 M – 0.1 M K2C2O4. In comparison with a forward voltammetric sweep in an electrolyte containing only KOH, the addition of oxalate anions to the solution has no observed effect on the voltammetry of metallic nickel in the hydrogen evolution reaction (HER) or the Ni/Ni(II) oxidation potential regions (< 1.3 VRHE). This result is similar to those reported for several other organic additives [5]. However, whereas literature evidence shows that at higher potentials many organic additives oxidize at a diffusion-limited rate, oxalate anions do not show the expected oxidation current. Instead, we observe that the Ni(II)/Ni(III) oxidation peak, normally at ~1.45 VRHE, occurs at lower potentials (1.35 – 1.40 VRHE). Further, in the presence of sufficiently high concentrations of oxalate anions, the Ni(II)/Ni(III) oxidation peak is very sharp, with full widths at half maxima (FWHMs) less than 20 mV. The voltammetric peak characteristics, i.e., the peak position, width and area, have been measured at various concentrations of oxalate and at various potential sweep rates. The peak position is very sensitive to the electrode’s surface state. However, the peak width and area are very reproducible. The practical result of the present work is that we now have a sharp voltammetric peak that is well-separated from concurrent electrochemical processes, in particular the OER, which typically onsets near 1.45 - 1.5 VRHE. The electrical charge associated with this oxidation peak is consistent with a process involving a monolayer. The implementation of these results for measuring the ECSA of metallic nickel electrodes has been tested by conducting voltammetry on different electrode preparation regimes, which ranged from a rough polish to a mirror finish. The validity of this characterization method will be discussed and the results will be compared with alternative methods [6]. The extension of this work to characterizing nickel-based catalyst materials will be discussed. ACKNOWLEDGEMENTS This work was supported by National Research Council Canada (NRC), the Natural Sciences and Engineering Research Council of Canada (NSERC) and the University of Ottawa. REFERENCES 1. D. Chade, L. Berlouis, D. Infield, A. Cruden, P. T. Nielsen and T. Mathiesen, Int. J. Hydrogen Energy, 38, 14380 (2013). 2. Y. Miao, L. Ouyang, S. Zhou, L. Xu, Z. Yang, M. Xiao and R. Ouyang, Biosens. Bioelectron., 53, 428 (2014). 3. Y. Wang, S. Gai, N. Niu, F. He and P. Yang, J. Mater. Chem. A, 1, 9083 (2013). 4. D. S. Hall, C. Bock and B. R. MacDougall, J. Electrochem. Soc., 160, F235 (2013). 5. M. Fleischmann, K. Korinek and D. Pletcher, J. Electroanal. Chem. Interfacial Electrochem., 31, 39 (1971). 6. S. Trasatti and O. A. Petrii, J. Electroanal. Chem., 327, 353 (1992).

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 science0.0010.000
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 head0.008
GPT teacher head0.218
Teacher spread0.209 · 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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Published2014
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