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Record W4241306013 · doi:10.1002/ange.201805514

Ambient Electrosynthesis of Ammonia: Electrode Porosity and Composition Engineering

2018· article· en· W4241306013 on OpenAlexaff
Hong Wang, Lu Wang, Qiang Wang, Shuyang Ye, Wei Sun, Yue Shao, Zhiping Jiang, Qiao Qiao, Yimei Zhu, Pengfei Song, Debao Li, Le He, Xiaohong Zhang, Jiayin Yuan, Tom Wu, Geoffrey A. Ozin

Bibliographic record

VenueAngewandte Chemie · 2018
Typearticle
Languageen
FieldChemical Engineering
TopicAmmonia Synthesis and Nitrogen Reduction
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsElectrosynthesisAqueous solutionCatalysisAmmonia productionNanoporousChemical engineeringAmmoniaElectrochemistryInorganic chemistryReversible hydrogen electrodeCarbon fibersPorosityChemistryFaraday efficiencyMaterials scienceNanotechnologyElectrodeWorking electrodeOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Ammonia, a key precursor for fertilizer production, convenient hydrogen carrier, and emerging clean fuel, plays a pivotal role in sustaining life on Earth. Currently, the main route for NH 3 synthesis is by the heterogeneous catalytic Haber–Bosch process (N 2 +3 H 2 →2 NH 3 ), which proceeds under extreme conditions of temperature and pressure with a very large carbon footprint. Herein we report that a pristine nitrogen‐doped nanoporous graphitic carbon membrane (NCM) can electrochemically convert N 2 into NH 3 in an acidic aqueous solution under ambient conditions. The Faradaic efficiency and rate of production of NH 3 on the NCM electrode reach 5.2 % and 0.08 g m −2 h −1 , respectively. Functionalization of the NCM with Au nanoparticles dramatically enhances these performance metrics to 22 % and 0.36 g m −2 h −1 , respectively. As this system offers the potential to be scaled to industrial levels it is highly likely that it might displace the century‐old Haber–Bosch process.

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 score0.001
Threshold uncertainty score0.648

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 head0.008
GPT teacher head0.207
Teacher spread0.199 · 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

Citations17
Published2018
Admission routes1
Has abstractyes

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