MétaCan
Menu
Back to cohort
Record W3120935163 · doi:10.1002/smm2.1018

Suppressing the liquid product crossover in electrochemical CO<sub>2</sub> reduction

2021· article· en· W3120935163 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSmartMat · 2021
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsUniversity of Toronto
FundersNational Natural Science Foundation of China
KeywordsElectrochemistryElectrolyteFormateFaraday efficiencyCommodity chemicalsMembraneLiquid fuelChemical engineeringChemistryMaterials scienceProcess engineeringElectrodeOrganic chemistryCatalysisEngineering

Abstract

fetched live from OpenAlex

Abstract Coupling electrochemical CO 2 reduction (CO 2 R) with a renewable energy source to create high‐value fuels and chemicals is a promising strategy in moving toward a sustainable global energy economy. CO 2 R liquid products, such as formate, acetate, ethanol, and propanol, offer high volumetric energy density and are more easily stored and transported than their gaseous counterparts. However, a significant amount (~30%) of liquid products from electrochemical CO 2 R in a flow cell reactor cross the ion exchange membrane, leading to the substantial loss of system‐level Faradaic efficiency. This severe crossover of the liquid product has—until now—received limited attention. Here, we review promising methods to suppress liquid product crossover, including the use of bipolar membranes, solid‐state electrolytes, and cation‐exchange membranes‐based acidic CO 2 R systems. We then outline the remaining challenges and future prospects for the production of concentrated liquid products from CO 2 .

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.

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 score0.033
Threshold uncertainty score0.479

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 head0.248
Teacher spread0.239 · 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