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Record W7116781695 · doi:10.1021/acs.jpcc.5c06254

Concentration Effects of Co and Cu Dopants in β-Ni(OH) <sub>2</sub> on Ammonia Oxidation Activity and Selectivity

2025· article· en· W7116781695 on OpenAlexafffund
Shayne Johnston, Brendan D. Paget, Jesper A. Biesenthal, Daniel J. Quintal, Leanne D. Chen

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2025
Typearticle
Languageen
FieldChemical Engineering
TopicAmmonia Synthesis and Nitrogen Reduction
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of CanadaAlliance de recherche numérique du Canada
KeywordsSelectivityAmmoniaDopantElectrochemistryDopingRedoxAmmonia production

Abstract

fetched live from OpenAlex

The electrochemical ammonia oxidation reaction offers a potential approach for energy generation or remediation of aqueous nitrogenous waste. The development of affordable and stable electrocatalysts is needed for the widespread use of the AOR. Ni-based electrodes offer a cheaper alternative to other electrocatalysts while having moderate activity and long-term stability. This work uses density functional theory to investigate the effect of Co and Cu surface dopants at varying concentrations in β-Ni(OH) 2 and their impact on ammonia oxidation activity and selectivity. The introduction of Co doping to β-Ni(OH) 2 reduced the *NH 2 to *NH free energy, leading to lowered limiting potentials for N 2 ( g ) formation. Cu doping led to the reduction in energy required for the hydroxylation of *NO to *NO 2 H. This hydroxylation step is the typical limiting potential step for the production of NO 2 – ( aq ) and suggests that Cu doping may impact the selectivity toward NO 2 – ( aq ) production. This work begins to understand the effect of surface doping β-Ni(OH) 2 in differing ratios in an attempt to improve the performance of Ni-based catalysts.

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 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)0.000
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 score0.009
Threshold uncertainty score0.298

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.004
GPT teacher head0.225
Teacher spread0.221 · 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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueThe Journal of Physical Chemistry CSame topicAmmonia Synthesis and Nitrogen ReductionFrench-language works237,207