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Record W4213352468 · doi:10.1002/celc.202101632

Linker‐Modulated Peroxide Electrosynthesis Using Metal‐Organic Nanosheets**

2022· article· en· W4213352468 on OpenAlexaff
Kiran Kuruvinashetti, Nikolay Kornienko

Bibliographic record

VenueChemElectroChem · 2022
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversité de Montréal
FundersAmerican Chemical Society Petroleum Research Fund
KeywordsElectrosynthesisOverpotentialCatalysisFaraday efficiencyHydrogen peroxideAmine gas treatingTerephthalic acidInorganic chemistryChemistryElectrochemistryElectrolyteNanosheetMaterials scienceOrganic chemistryElectrodePhysical chemistry

Abstract

fetched live from OpenAlex

Abstract The electrochemical synthesis of hydrogen peroxide (H 2 O 2 ), a widely used oxidant, is emerging as a green alternative to the conventional anthraquinone method. In this work, Ni‐based metal‐organic nanosheet (Ni−MON) catalysts constructed using a variety of linkers were studied as oxygen reduction catalysts. Using a host of analytical techniques, we reveal how modulating the terephthalic acid linker with hydroxy, amine, and fluorine groups impacts the resulting physical and electronic structure of the Ni catalytic sites. These changes further impact the catalysts’ Faradaic Efficiency for H 2 O 2 , with the Ni−Amine−MON reaching near 100 % FE at minimal overpotential for the 2 e − H 2 O 2 pathway in alkaline electrolyte. Finally, we translate the Ni−Amine−MON catalyst to a gas‐diffusion reaction geometry and demonstrate a H 2 O 2 partial current density of 200 mA/cm 2 while maintaining 85 % Faradaic efficiency. In all, this study puts forth a simple route to catalyst modulation for highly effective H 2 O 2 electrosynthesis.

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), Insufficient payload (model declined to judge)
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 score0.025
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.012
GPT teacher head0.213
Teacher spread0.202 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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