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Record W4230117171 · doi:10.1149/ma2018-01/29/1690

Effect of Co Addition in Amorphous Ni-Based Alloys for the Alkaline Oxygen Evolution Reaction

2018· article· en· W4230117171 on OpenAlexaff
Kevin M. Cole, Donald W. Kirk, Steven J. Thorpe

Bibliographic record

VenueECS Meeting Abstracts · 2018
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsOverpotentialOxygen evolutionAlkaline water electrolysisAmorphous solidElectrolysisMaterials scienceElectrolyteElectrolysis of waterMicrostructureChemical engineeringFaraday efficiencyMetallurgyNickelWater splittingAqueous solutionCorrosionInorganic chemistryCatalysisElectrochemistryChemistryElectrodePhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Traditional alkaline water electrolysis has been successful industrially because of the use of low cost, stable, nickel based materials as electrocatalysts. The major drawback to these systems is that they use aqueous KOH as the electrolyte which is not desirable from a corrosion and packaging perspective. The use of novel anion exchange membranes (AEM) in alkaline water electrolysers is now of particular interest since it eliminates the aqueous KOH electrolyte and can provide form factors and much higher current densities that rival PEM-based electrolysers. Conventional Ni electrocatalysts for the oxygen evolution reaction (OER) still display large overpotentials and slower reaction kinetics. The addition of Co to Ni-based alloys has been noted to increase the activity by stabilizing the β-NiOOH phase over γ-NiOOH, but this often leads to an increase in the oxygen overpotential [1]. The issue of increased overpotential can be mitigated with the use of amorphous structures to reduce oxygen overpotentials with the addition of Co [2]. This previous research has been limited to thin films which are not ideal for catalysts in AEM water electrolysis [2]. In this work, amorphous Ni79.2-xCoxNb12.5Y8.3 (x = 0, 5, 10 at.% Co) are synthesized using mechanical alloying as a means to produce high surface area powders. The effect of milling temperature and time on the resulting powder microstructure was characterized using a combination of x-ray diffraction and electron microscopy. Performing mechanical alloying at cryogenic temperatures resulted in an amorphous microstructure for alloys containing 0, 5, and 10 at.% Co upon 6 hours of milling. The role of Co on the alkaline oxygen evolution reaction was investigated by cyclic voltammetry and Tafel measurements. Tafel plots for the amorphous alloys, along with crystalline Ni can be seen in Fig. 1 with the corresponding kinetic properties being summarized in Table 1. These results show that all of the amorphous alloys possessed a lower Tafel slope compared to crystalline Ni while the amount of Co within these amorphous alloys had little effect on Tafel values. The kinetic behaviour of the amorphous alloys towards the OER was a function of the amount of Co. When 5 at.% Co was added to Ni79.2-xCoxNb12.5Y8.3, the overpotential decreased by 30 mV with a three-fold increase in exchange current density. In contrast, when 10 at.% Co was added, only a minor decrease in overpotential was observed accompanied by a nearly six-fold increase in exchange current density. These features, along with the high surface area achieved during mechanical alloying, provide a cost-effective and simple method for producing stable and active electrocatalysts for the oxygen evolution reaction in AEM water electrolysis. [1] T. N. Lambert, J. A. Vigil, S. E. White, D. J. Davis, S. J. Limmer, P. D. Burton, E. N. Coker, T. E. Beechem and M. T. Brumbach, Chem. Commun., 51(46), 9511 (2015) [2] R. D. L. Smith, M. S. Prévot, R. D. Fagan, S. Trudel and C. P. Berllinguette, J. Am. Chem. Soc., 135(31), 11580 (2013) Figure 1

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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 score0.002
Threshold uncertainty score0.004

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 head0.013
GPT teacher head0.278
Teacher spread0.266 · 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
Published2018
Admission routes1
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