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Record W2945075429 · doi:10.1021/acsenergylett.9b00975

CO <sub>2</sub> Electroreduction from Carbonate Electrolyte

2019· article· en· W2945075429 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueACS Energy Letters · 2019
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institute for Advanced Research
KeywordsSyngasElectrolysisElectrolyteCarbonateElectrochemistryChemical engineeringCatalysisUpgradeChemistryMaterials scienceWaste managementElectrodeOrganic chemistryComputer scienceEngineering

Abstract

fetched live from OpenAlex

The process of CO2 valorization—from capture of CO2 to its electrochemical upgrade—requires significant inputs in each of the capture, upgrade, and separation steps. Here we report an electrolyzer that upgrades carbonate electrolyte from CO2 capture solution to syngas, achieving 100% carbon utilization across the system. A bipolar membrane is used to produce proton in situ to facilitate CO2 release at the membrane:catalyst interface from the carbonate solution. Using a Ag catalyst, we generate syngas at a 3:1 H2:CO ratio, and the product is not diluted by CO2 at the gas outlet; we generate this pure syngas product stream at a current density of 150 mA/cm2 and an energy efficiency of 35%. The carbonate-to-syngas system is stable under a continuous 145 h of catalytic operation. The work demonstrates the benefits of coupling CO2 electrolysis with a CO2 capture electrolyte on the path to practicable CO2 conversion technologies.

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 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 score0.076
Threshold uncertainty score1.000

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 head0.004
GPT teacher head0.193
Teacher spread0.189 · 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