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Record W4412538604 · doi:10.1039/d5ey00217f

How the electric double layer impacts nitrate reduction to ammonia

2025· article· en· W4412538604 on OpenAlexafffund
Sofia Czerny-Holownia, H. Boyer, Matthew Junjie Liu, Justin C. Bui, William A. Tarpeh, Eric W. Lees

Bibliographic record

VenueEES Catalysis · 2025
Typearticle
Languageen
FieldChemical Engineering
TopicAmmonia Synthesis and Nitrogen Reduction
Canadian institutionsUniversity of British Columbia
FundersNational Science Foundation Graduate Research Fellowship ProgramDivision of Emerging Frontiers in Research and InnovationDefense Advanced Research Projects AgencyU.S. Department of DefenseUniversity of British ColumbiaNatural Sciences and Engineering Research Council of CanadaCMC MicrosystemsNational Science FoundationGovernment of CanadaDivision of Emerging FrontiersDefense Sciences Office, DARPASchmidt Family Foundation
KeywordsAmmoniaReduction (mathematics)NitrateEnvironmental scienceLayer (electronics)Double layer (biology)ChemistryEnvironmental chemistryMaterials scienceNanotechnologyMathematicsOrganic chemistry

Abstract

fetched live from OpenAlex

Multiphysics modelling reveals how the electric double layer governs nitrate transport and how a more negative catalyst potential-of-zero-charge promotes ammonia formation.

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 score0.008
Threshold uncertainty score0.476

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.013
GPT teacher head0.237
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 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

Citations10
Published2025
Admission routes2
Has abstractyes

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