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Record W4361236094 · doi:10.1002/cssc.202202376

Performance and Stability of Aemion and Aemion+ Membranes in Zero‐Gap CO<sub>2</sub> Electrolyzers with Mild Anolyte Solutions

2023· article· en· W4361236094 on OpenAlexaff
Peter Mardle, Apurva Shantilal Gangrade, Torben Saatkamp, Zhengming Jiang, Simon Cassegrain, Nana Zhao, Zhiqing Shi, Steven Holdcroft

Bibliographic record

VenueChemSusChem · 2023
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsNational Research Council CanadaSimon Fraser University
Fundersnot available
KeywordsElectrolysisMembraneCathodePermeationFaraday efficiencyPolymer electrolyte membrane electrolysisChemistryElectrolysis of waterChemical engineeringWork (physics)Analytical Chemistry (journal)Proton exchange membrane fuel cellInorganic chemistryElectrochemistryElectrolyteElectrodeThermodynamicsChromatographyPhysical chemistry

Abstract

fetched live from OpenAlex

Abstract The dependence of performance and stability of a zero‐gap CO 2 electrolyzer on the properties of the anion exchange membrane (AEM) is examined. This work firstly assesses the influence of the anolyte when using an Aemion membrane and then shows that when using 10 mM KHCO 3 , a CO 2 electrolyzer using a next‐generation Aemion+ membrane can achieve lower cell voltages and longer lifetimes due to increased water permeation. The impact of lower permselectivity of Aemion+ on water transport is also discussed. Using Aemion+, a cell voltage of 3.17 V at 200 mA cm −2 is achieved at room temperature, with a faradaic efficiency of &gt;90 %. Stable CO 2 electrolysis at 100 mA cm −2 is demonstrated for 100 h, but with reduced lifetime at 300 mA cm −2 . However, the lifetime of the cell at high current densities is shown to be increased by improving water transport characteristics of the AEM and reducing dimensional swelling, as well as by improving cathode design to reduce localized dehydration of the membrane.

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 score0.010
Threshold uncertainty score0.604

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.022
GPT teacher head0.235
Teacher spread0.213 · 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

Citations24
Published2023
Admission routes1
Has abstractyes

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