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Electrochemically Driven C-N Bond Formation from CO2 and Ammonia at the Triple-Phase Boundary

2021· preprint· en· W4226279195 on OpenAlexafffund
Junnan Li, Nikolay Kornienko

Bibliographic record

VenueChemRxiv · 2021
Typepreprint
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsUniversité de Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectrocatalystTriple bondElectrolysisChemistryPhase (matter)Phase boundaryNanotechnologyTriple junctionWork (physics)AmmoniaElectrochemistryMaterials scienceThermodynamicsPhysical chemistryOrganic chemistryPhysicsElectrode

Abstract

fetched live from OpenAlex

Electrosynthetic techniques are gaining prominence across the fields of chemistry, engineering and energy science. However, most works within the direction of synthetic heterogeneous electrocatalysis focus on water electrolysis and CO 2 reduction. In this work, we moved to expand the scope of this technology by developing a synthetic scheme which couples CO 2 and NH 3 at a gas-liquid-solid triple-phase boundary to produce species with C-N bonds. Specifically, by bringing in CO 2 from the gas phase and NH 3 from the liquid phase together over solid copper catalysts, we have succeeded in forming formamide and acetamide products for the first time. In a subsequent complementary step, we have combined electrochemical analysis and a newly developed operando spectroelectrochemical method, capable of probing the aforementioned triple phase boundary, to extract an initial level of mechanistic analysis regarding the reaction pathways of these reactions and the current system’s limitations. We believe that the development and understanding of this set of reaction pathways will play an exceptionally significant role in expanding the community’s understanding of on-surface electrosynthetic reactions as well as push this set of inherently sustainable technologies towards widespread applicability.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.254
Teacher spread0.243 · 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 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

Citations7
Published2021
Admission routes2
Has abstractyes

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