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Record W4412403640 · doi:10.1021/acscatal.5c03710

Atomically Dispersed Ni–Cu Dual Sites: Efficient Electrocatalytic Conversion of 4-Nitrophenol to <i>p</i>-Aminophenol in a Hybrid Acid/Alkali Flow Electrolyzer

2025· article· en· W4412403640 on OpenAlexaff
Hongzhong Wang, Kai Chen, Junheng Huang, Chengchao He, Qinlong Zhang, Junwei Li, Zhifang Zhang, Zhenhai Wen

Bibliographic record

VenueACS Catalysis · 2025
Typearticle
Languageen
FieldChemistry
TopicNanomaterials for catalytic reactions
Canadian institutionsCarbon Engineering (Canada)
FundersShaanxi Science and Technology AssociationYulin UniversityNatural Science Foundation of Fujian ProvinceChinese Academy of SciencesNational Natural Science Foundation of ChinaYulin Science and Technology BureauNational Key Research and Development Program of ChinaFuzhou Science and Technology Bureau
KeywordsElectrolysisAlkali metalElectrocatalystCatalysisDual (grammatical number)ChemistryInorganic chemistryMaterials scienceChemical engineeringCombinatorial chemistryElectrochemistryElectrodeOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

4-Nitrophenol (4-NP), a toxic and recalcitrant pollutant, necessitates effective remediation strategies. Here, a noble metal-free NiCu-NC electrocatalyst is developed, where dual atomic Ni–Cu sites are elegantly anchored on a rhombic dodecahedral nitrogen-doped carbon matrix, enabling selective electrochemical reduction of 4-NP into value-added p -aminophenol (4-AP). This catalyst achieves a low overpotential and a high Faradaic efficiency of 95.7% for 4-AP production. Density functional theory (DFT) calculations reveal that the NiCu dual-atom configuration minimizes the energy barrier for the rate-determining step, thereby enhancing catalytic performance. Furthermore, a hybrid acid/alkali flow electrolysis cell is designed to couple the cathodic 4-NP reduction with the anodic oxygen evolution. This system maintains stable operation for over 400 h while maintaining a high Faradaic efficiency (>91%) and near-complete conversion (99%) for valorization of 4-NP. This work demonstrates a practical electrocatalytic approach for pollutant transformation and chemical production with potential implications for wastewater treatment and resource utilization.

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 categoriesMeta-epidemiology (narrow)
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 score0.002
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.220
Teacher spread0.215 · 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.

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

Citations10
Published2025
Admission routes1
Has abstractyes

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