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Record W3209215202 · doi:10.1002/elsa.202100142

Mechanism of ammonia oxidation to dinitrogen, nitrite, and nitrate on β‐Ni(OH) <sub>2</sub> from first‐principles simulations

2021· article· en· W3209215202 on OpenAlexafffund
Rachelle M. Choueiri, Stephen W. Tatarchuk, Anna Klinkova, Leanne D. Chen

Bibliographic record

VenueElectrochemical Science Advances · 2021
Typearticle
Languageen
FieldChemical Engineering
TopicAmmonia Synthesis and Nitrogen Reduction
Canadian institutionsUniversity of WaterlooUniversity of Guelph
FundersWaterloo Institute for Nanotechnology, University of WaterlooNatural Sciences and Engineering Research Council of CanadaCanada Research Coordinating CommitteeUniversity of WaterlooCanada Foundation for InnovationOntario Research Foundation
KeywordsChemistryNitriteNitrateAmmoniaElectrocatalystInorganic chemistryCatalysisDeprotonationAmmonia productionReaction mechanismOrganic chemistryElectrochemistryElectrodePhysical chemistryIon

Abstract

fetched live from OpenAlex

Abstract The electrocatalyzed ammonia oxidation reaction (AOR) is a potential pathway toward waste ammonia remediation, energy generation, and the synthesis of value‐added products. To date, mechanistic studies have focused on elucidating the progress of AOR on Pt‐based catalysts with an established pathway for only. In this work, density functional theory was applied to determine the lowest energy intermediates toward nitrogen gas, nitrite, and nitrate formation on ‐, a promising electrocatalyst material for AOR. It was found that dinitrogen formation progresses via NH‐NH coupling, whereas nitrite and nitrate formation occurs via deprotonation of ammonia to form adsorbed N and subsequent hydroxylation to form oxygenated intermediates. This work is the first to report a mechanism for nitrite and nitrate formation and will also serve as a benchmark for future studies on Ni‐based materials.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.011
GPT teacher head0.235
Teacher spread0.224 · 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 designSimulation or modeling
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

Citations70
Published2021
Admission routes2
Has abstractyes

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