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Record W4251856700 · doi:10.1149/10301.0663ecst

Production of Carbon Neutral Methanol Using Co-Electrolysis of CO<sub>2</sub> and Steam in Solid Oxide Electrolysis Cell in Tandem with Direct Air Capture

2021· article· en· W4251856700 on OpenAlexaff
Katelyn M Ferguson, Hussein Saafan, Emma K Wildeboer, Troy Reeves, Eric Croiset

Bibliographic record

VenueECS Transactions · 2021
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsElectrolysisOxideEnvironmental scienceGreenhouse gasCapital costCarbon dioxideAtmosphere (unit)MethanolElectrolysis of waterCarbon fibersWaste managementChemistryMaterials scienceElectrodeEngineeringThermodynamicsMetallurgy

Abstract

fetched live from OpenAlex

The increasing concentration of carbon dioxide (CO2) in the atmosphere since the industrial revolution is a major contributor to climate change. Among the several options to tackle this issue, the removal of CO2 from the atmosphere and its subsequent use is becoming increasingly attractive. This paper presents a techno-economic feasibility study and quantification of the environmental benefits of combining direct air capture (i.e. capturing CO2 from the atmosphere) with co-electrolysis of water and the captured CO2 in a solid oxide electrolyser cell (SOEC). It was found that the fuel methanol could be modelled to be produced in a carbon negative manner but is not profitable due to current SOEC capital cost. The capital cost of the SOEC is expected to be cut in half by 2030. This would allow for profitable production of methanol.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.008
GPT teacher head0.231
Teacher spread0.224 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2021
Admission routes1
Has abstractyes

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