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Record W4309813204 · doi:10.1149/ma2022-02441632mtgabs

High Performance Anion Exchange Membrane Water Electrolyzer Using Monolayer Nickel-Iron Layered Double Hydroxide As Anode Catalysts

2022· article· en· W4309813204 on OpenAlexaff
Sun Seo Jeon, Hyunjoo Lee

Bibliographic record

VenueECS Meeting Abstracts · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsHydroxideElectrolysisMaterials scienceInorganic chemistryCatalysisElectrocatalystMonolayerAnodeChemical engineeringNickelConductivityIon exchangeOxygen evolutionChemistryElectrochemistryElectrodeElectrolyteMetallurgyNanotechnologyIonOrganic chemistry

Abstract

fetched live from OpenAlex

Much efforts have been devoted to developing electrocatalysts applicable to anion exchange membrane water electrolyzer (AEMWE). AEMWE operates in basic condition, which allows non-noble metal-based catalysts to be used, while its membrane electrode assembly (MEA) design allows higher current density compared to conventional alkaline water electrolyzer (AWE). Among many candidates for oxygen evolution reaction (OER), NiFe layered double hydroxide (LDH)-based electrocatalysts show the highest activity in an alkaline medium. Unfortunately, the poor electrical conductivity of NiFe-LDH limits its potential as an electrocatalyst, which was often solved by hybridization with conductive carbonaceous materials. However, we find that using carbonaceous materials for anode has detrimental effects on the stability of AEMWE at industrially relevant current densities. In this work, a facile monolayer structuring is suggested to overcome low electrical conductivity and improve mass transport without using carbonaceous materials. Bulk NiFe-LDH (B-NiFe-LDH) with multiple cationic layers was synthesized by following a conventional co-precipitation method, while monolayer NiFe-LDH (M-NiFe-LDH) was prepared using a similar method, but with formamide present in solvent. While Fe 2p and O 1s did not show noticeable difference, M-NiFe-LDH had larger Ni 3+ peak than B-NiFe-LDH, indicating that M-NiFe-LDH has more Ni species in NiOOH environment rather than Ni(OH) 2 environment. Here, more NiOOH phase in M-NiFe-LDH caused higher conductivity, leading to higher specific activity. The effect of electrical conductivity was further investigated by mixing the catalysts with various amounts of carbon materials. When carbon black (Vulcan XC-72) was loaded together with B-NiFe-LDH, the OER activity increased from the absence of carbon up to carbon-to-catalyst weight ratio of 0.1. Further addition of carbon did not increase the current density. On the other hand, the OER activity barely changed upon carbon addition for M-NiFe-LDH. As only Ni 3+/4+ species are known to have OER activity, facile electron transfer from Ni 2+ sites to Ni 3+/4+ is important. Mixing carbon materials with B-NiFe-LDH provided conductive networks into previously “electron-unreachable” regions, which was also confirmed by the increase in the size of Ni oxidation peaks upon carbon addition. On the other hand, the contact between glassy carbon and M-NiFe-LDH catalyst was sufficient to have efficient electron transfer without carbon. The M-NiFe-LDH deposited on Ni foam (NF) showed much better AEMWE performance than B-NiFe-LDH, due to better electrical conductivity and higher hydrophilicity. When the M-NiFe-LDH was loaded on carbon paper (CP) instead of Ni foam, water electrolysis performance was enhanced, but stability was greatly lowered. Similar to the M-NiFe-LDH, the CP substrate showed the higher initial performance than the NF substrate for B-NiFe-LDH, but the B-NiFe-LDH/CP stopped operating eventually even at milder conditions. Overall, using M-NiFe-LDH/NF electrode, the high energy conversion efficiency of 72.6% and an outstanding stability at a current density of 1 A cm -2 over 50 h could be achieved without carbonaceous material. This work highlights electrical conductivity and hydrophilicity of catalysts in membrane-electrode-assembly (MEA) as key factors for high performance AEMWE.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.443
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.243
Teacher spread0.226 · 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 teacher head, not a consensus.

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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