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Record W4391914613 · doi:10.1002/anie.202401924

Two‐dimensional Cu Plates with Steady Fluid Fields for High‐rate Nitrate Electroreduction to Ammonia and Efficient Zn‐Nitrate Batteries

2024· article· en· W4391914613 on OpenAlexaff
Limin Zhou, Xueqiu Chen, Shaojun Zhu, Kun You, Zhengjun Wang, Ru Fan, Jun Li, Yifei Yuan, Xin Wang, Jichang Wang, Yihuang Chen, Huile Jin, Shun Wang, Jingjing Lv

Bibliographic record

VenueAngewandte Chemie International Edition · 2024
Typearticle
Languageen
FieldChemical Engineering
TopicAmmonia Synthesis and Nitrogen Reduction
Canadian institutionsUniversity of Windsor
FundersNational Natural Science Foundation of China
KeywordsNitrateAmmoniaAmmonia productionInorganic chemistryChemistryMaterials scienceThermodynamicsPhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Nitrate electroreduction reaction (eNO 3 − RR) to ammonia (NH 3 ) provides a promising strategy for nitrogen utilization, while achieving high selectivity and durability at an industrial scale has remained challenging. Herein, we demonstrated that the performance of eNO 3 − RR could be significantly boosted by introducing two‐dimensional Cu plates as electrocatalysts and eliminating the general carrier gas to construct a steady fluid field. The developed eNO 3 − RR setup provided superior NH 3 Faradaic efficiency (FE) of 99 %, exceptional long‐term electrolysis for 120 h at 200 mA cm −2 , and a record‐high yield rate of 3.14 mmol cm −2 h −1 . Furthermore, the proposed strategy was successfully extended to the Zn‐nitrate battery system, providing a power density of 12.09 mW cm −2 and NH 3 FE of 85.4 %, outperforming the state‐of‐the‐art eNO 3 − RR catalysts. Coupled with the COMSOL multiphysics simulations and in situ infrared spectroscopy, the main contributor for the high‐efficiency NH 3 production could be the steady fluid field to timely rejuvenate the electrocatalyst surface during the electrocatalysis.

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 score0.017
Threshold uncertainty score0.823

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 head0.009
GPT teacher head0.236
Teacher spread0.227 · 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

Citations33
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueAngewandte Chemie International EditionSame topicAmmonia Synthesis and Nitrogen ReductionFrench-language works237,207