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Record W4320919762 · doi:10.1002/aenm.202204231

Electrocatalytic Disproportionation of Nitric Oxide Toward Efficient Nitrogen Fixation

2023· article· en· W4320919762 on OpenAlexaff
Angjian Wu, Jiabao Lv, Xiaoxu Xuan, Jinhe Zhang, Ang Cao, Mengjie Wang, Xiaoyu Wu, Qian Liu, Yunhao Zhong, Wei Sun, Qiulin Ye, Yaqi Peng, Xiaoqing Lin, Zhifu Qi, Songqiang Zhu, Qunxing Huang, Xiaodong Li, Hao Bin Wu, Jianhua Yan

Bibliographic record

VenueAdvanced Energy Materials · 2023
Typearticle
Languageen
FieldChemical Engineering
TopicAmmonia Synthesis and Nitrogen Reduction
Canadian institutionsUniversity of Waterloo
FundersFundamental Research Funds for the Central UniversitiesZhejiang UniversityNational Natural Science Foundation of China
KeywordsDisproportionationMaterials scienceNitric oxideNitrogenOxideInorganic chemistryCatalysisMetallurgyOrganic chemistryChemistry

Abstract

fetched live from OpenAlex

Abstract Electrocatalytic conversion of waste nitric oxide into ammonia is a promising approach to achieve sustainable nitrogen fixation. Herein, a CoNi co‐oxides catalyst is designed for NH 3 electrosynthesis with the merits of facilitating NO adsorption and reducing the reaction energy barrier. By synergistic coupling with anodic NO oxidation, electrocatalytic disproportionation of NO is first realized to simultaneously synthesize value‐added double nitrogen products (NH 3 and nitrate) with increased total energy efficiency. Furthermore, decoupled acid–base asymmetric electrolyte design is proposed in a united assembled electrolyzer, enabling a high NH 3 production rate (26.27 mg h −1 cm −2 ) with unit faradaic efficiency and a remarkable nitrate production rate of 68.41 mg h −1 cm −2 at the anode. A low cell voltage of 3.58 V is obtained by optimizing ion agglomeration within the membrane to promote the charge‐ion exchange and electrode kinetics. Technoeconomic analysis demonstrates the economic feasibility of recycling waste NO by the electrocatalytic disproportionation strategy.

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 score0.034
Threshold uncertainty score0.521

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 head0.010
GPT teacher head0.229
Teacher spread0.219 · 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

Citations39
Published2023
Admission routes1
Has abstractyes

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