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Record W4293124613 · doi:10.1016/j.xcrp.2022.101027

Strategies for decarbonizing natural gas with electrosynthesized methane

2022· article· en· W4293124613 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

VenueCell Reports Physical Science · 2022
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsQueen's University
Fundersnot available
KeywordsNatural gasRenewable natural gasRenewable energyEnvironmental sciencePower to gasGreenhouse gasDownstream (manufacturing)MethaneElectrolysisWork (physics)Process engineeringNatural gas pricesWaste managementEngineeringFuel gasMechanical engineeringChemistryElectrical engineeringElectrodeOperations management

Abstract

fetched live from OpenAlex

Natural gas supplies nearly a quarter of the world’s energy and is growing faster than any other energy source. One pathway to reduce the CO2 emission intensity of natural gas without transitioning end-use infrastructure is to synthesize a natural gas substitute from CO2 and renewable energy via electrochemical CO2 reduction. To improve the economic viability of electrogas, this work examines the possibility of using electrolyzer products without downstream separation. We quantify the electrolyzer performance needed to replicate the key heating value, safety, and emissions characteristics of natural gas. We find that, except in the case of unrealistically high device performance, directly synthesized electrogas is unable to reproduce all necessary properties of natural gas. We discover, however, a range of safe and low-emitting electrogas compositions likely achievable with current technology that can be blended with natural gas to reduce its CO2 intensity while retaining sufficient heating value.

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 score0.017
Threshold uncertainty score0.386

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 head0.009
GPT teacher head0.252
Teacher spread0.243 · 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