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Record W4285400078 · doi:10.1149/ma2022-01391787mtgabs

Determining the Influence of Catalyst Layer Architecture and Reactant Flow in an MEA for the Electrochemical Nitrogen Reduction Reaction Under Ambient Conditions

2022· article· en· W4285400078 on OpenAlexaff
Wei Bi, Előd Gyenge, David P. Wilkinson, Nima Shaigan, Ali Malek, Khalid Fatih

Bibliographic record

VenueECS Meeting Abstracts · 2022
Typearticle
Languageen
FieldChemical Engineering
TopicAmmonia Synthesis and Nitrogen Reduction
Canadian institutionsNational Research Council CanadaUniversity of British Columbia
Fundersnot available
KeywordsCatalysisElectrosynthesisFaraday efficiencyCathodeAnodeChemical engineeringMaterials scienceElectrochemistryNoble metalChemistryNanotechnologyInorganic chemistryElectrodeOrganic chemistry

Abstract

fetched live from OpenAlex

Ammonia (NH 3 ) has been increasingly recognized as an advantageous energy/hydrogen carrier beyond agriculture use to assist in the achievement of net-zero and decarbonization goals. For over a century, NH 3 production has been dominated by the thermochemical Haber-Bosch process which consumes significant fossil fuel-related energy and feedstocks with a heavy carbon footprint. By contrast, the electrochemical nitrogen reduction reaction (ENRR) presents a sustainable and modular approach to fit remote and small-scale NH 3 needs, which has attracted growing interest in recent years. Rational design of electrocatalysts (e.g., introducing defects, size reduction, and heteroatom doping) has been the mainstream approach to address the bottlenecks in the ENRR, especially the low specific activity and poor faradaic efficiency. However, attempts to vary the ENRR electrolytic cell layout/design or the operating conditions have been scarce, and therefore present an opportunity to improve the NH 3 electrosynthesis performance by well-thought-out reactor designs coupled with operating conditions. Our study aims to understand the influence of catalyst layer/electrode architectures and reactant mass transfer on the ENRR performance of model catalysts in a membrane electrode assembly (MEA) cell under different flow configurations. On the anode side, a platinum-coated membrane is used to oxidize the humidified H 2 , which provides sufficient protons for the cathode reactions. On the cathode side, electrocatalysts with commercial availability and reported ENRR activities are applied as the catalyst (e.g., noble metal, transition metal nitrides or dichalcogenides). The catalyst layer is deposited on the gas-diffusion layer and/or the proton-exchange membrane to construct a gas-diffusion electrode (GDE) and/or a catalyst-coated membrane (CCM). Subsequently, the NH 3 electrosynthesis performances are evaluated under different cathode flow conditions for the varied catalyst layer architectures. Initially, the N 2 -saturated aqueous electrolyte (Na 2 SO 4 ) is recirculated on the cathode side to flood the cathode surface constantly, which resembles the operating condition in the liquid-filled H-cell present in previous ENRR studies. In these cases, the catalytic sites are predominantly covered by proton donors (e.g., hydronium ions or water molecules) without accessing N 2 molecules that only dissolve marginally (0.06 mmol/L) in aqueous media. Hence, in contrast with the “liquid-only” flow configuration, humidified N 2 gas is supplied alone (“gas-only”) or in combination with the recirculated electrolyte solution (“gas-liquid”) to increase the presence of N 2 near catalytic sites. When gaseous N 2 is present, the gas outlet pressure (cell back pressure), as well as membrane drying, were observed to affect the current density and operational stability. Additionally, false positives are diligently recognized which arise from residual N-containing species in the MEA and environmental NH 3 contamination. Linear scan voltammetry, the routine electrochemical characterization to distinguish potentially ENRR-active electrocatalysts, was found to be particularly susceptible and contribute significantly to erroneous ENRR performance. In short, This work demonstrates the challenge of getting authentic ammonia production in aqueous systems with improved electrolytic cell designs and operating conditions. References: Z. Andersen, V. Čolić, S. Yang, J. A. Schwalbe, A. C. Nielander, J. M. McEnaney, K. Enemark-Rasmussen, J. G. Baker, A. R. Singh, B. A. Rohr, M. J. Statt, S. J. Blair, S. Mezzavilla, J. Kibsgaard, P. C. K. Vesborg, M. Cargnello, S. F. Bent, T. F. Jaramillo, I. E. L. Stephens, J. K. Nørskov and I. Chorkendorff, Nature , 2019, 570 , 504–508. Wei, M. Pu, Y. Jin and M. Wessling, ACS Appl. Mater. Interfaces , 2021, 13 , 21411–21425. Figure captions: the top two figures illustrate the liquid- and gas-phase flow configurations in this study; and the bottom two figures demonstrate the negligible production of NH 3 from the gas-phase reaction condition where significant amounts of erroneous NH 3 production was observed from linear scan voltammetry (LSV). Figure 1

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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 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 score0.160
Threshold uncertainty score0.370

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 science0.0000.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.244
Teacher spread0.230 · 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.

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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Citations1
Published2022
Admission routes1
Has abstractyes

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