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Record W2945021488 · doi:10.1021/acsenergylett.9b00699

Electrochemical Nitrogen Reduction Reaction on Ruthenium

2019· article· en· W2945021488 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueACS Energy Letters · 2019
Typearticle
Languageen
FieldChemical Engineering
TopicAmmonia Synthesis and Nitrogen Reduction
Canadian institutionsNational Research Council Canada
FundersDevelopment and Reform Commission of Shenzhen MunicipalityResearch Grants Council, University Grants Committee
KeywordsRutheniumElectrochemistryElectrocatalystChemistryCatalysisAdsorptionNitrogenAmmoniaInorganic chemistryRedoxInfrared spectroscopyAbsorption (acoustics)PhotochemistryAmmonia productionElectrodePhysical chemistryMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Ruthenium is a good catalyst for ammonia synthesis in the Haber–Bosch process and a promising electrocatalyst for electrochemical N2 reduction reaction (NRR). However, the NRR pathway on Ru is unclear because of the lack of information on reaction intermediates. Surface-enhanced infrared absorption spectroscopy combined with electrochemical measurements is employed to study the NRR mechanisms on Ru thin film. During the nitrogen reduction, the *N2Hx (0 ≤ x ≤ 2) was detected with the band of N=N stretching (∼1940 cm–1) at potentials below 0.2 V in an N2-satureated HClO4 solution. The coverage of *N2Hx on the Ru surface was significantly increased with the potential decreasing from 0.2 to −0.4 V. The formed *N2Hx species could be oxidized at potentials higher than −0.1 V. In an N2-satureated KOH solution, no N-related infrared absorption band was observed on Ru surfaces, indicating that the adsorption of nitrogen molecules on Ru surfaces is very weak.

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.

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 score0.007
Threshold uncertainty score0.736

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 head0.005
GPT teacher head0.186
Teacher spread0.180 · 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