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Record W4388288062 · doi:10.1002/cssc.202301425

Modulating Cation and Water Transports for Enhanced CO Electrolysis via Ionomer Coating

2023· article· en· W4388288062 on OpenAlexafffund
Muflih A. Adnan, Shariful Kibria Nabil, Md Golam Kibria

Bibliographic record

VenueChemSusChem · 2023
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of Calgary
FundersGovernment of CanadaCanada First Research Excellence FundUniversity of Calgary
KeywordsIonomerCoatingElectrolysisElectrolysis of waterChemical engineeringMaterials scienceChemistryNanotechnologyElectrolyteElectrodeComposite materialPolymer

Abstract

fetched live from OpenAlex

Abstract Electrification of the chemical industry has been considered an enabler for energy transition on a massive scale. In this context, carbon monoxide electroreduction (COR) to produce multi‐carbon (C 2+ ) products is considered one of the forefront emerging technologies. The key challenge in COR comes from the excessive cation crossover to the cathode via electromigration and water diffusion, which limits CO availability and impedes product selectivity. Commercial anion exchange membrane (AEM) suppresses the electromigration of cations, however, suffers from water diffusion which facilitates cation crossover. Here, we tackled these problems emerging from cation crossover and water diffusion by directly depositing an ultrathin Nafion ionomer on the cathode (sputtered Cu) surface. Our approach enables full‐cell energy efficiency of 21 % for converting CO into ethylene (C 2 H 4 ) at 100 mA/cm 2 with over 200 hours of stable operation. We also exhibited record high energy efficiency for ethanol (C 2 H 5 OH) production with CO‐to‐C 2 H 5 OH electrolysis efficiency of 17 %. This approach to directly depositing ultrathin ionomer on the cathode to enhance system performance may benefit other electrochemical systems to overcome challenges associated with scalability, stability, and efficiency to produce high‐value chemicals.

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 score0.048
Threshold uncertainty score0.366

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 head0.007
GPT teacher head0.204
Teacher spread0.197 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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