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Record W3127399365 · doi:10.31031/pps.2020.03.000572

"Carbon Dioxide Recycling for Fuels and Chemical Products"

2020· article· en· W3127399365 on OpenAlexaff
V. Beschkov

Bibliographic record

VenueProgress in Petrochemical Science · 2020
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsCarbon-neutral fuelCarbon dioxideMethaneFossil fuelNegative carbon dioxide emissionGreenhouse gasGreenhouse gas removalEnvironmental scienceRenewable energyCarbon dioxide removalBio-energy with carbon capture and storageRenewable fuelsWaste managementElectrochemical reduction of carbon dioxideSynthetic fuelAtmospheric carbon cycleCarbon fibersBiomass (ecology)Carbon sequestrationSyngasChemistryCarbon monoxideMaterials scienceChemical reactionEngineeringOrganic chemistryEcology

Abstract

fetched live from OpenAlex

The problem of the adverse effect of greenhouse gases released in the atmosphere became a global one in the recent years due to the caused probable climate changes. The mostly spread greenhouse gases are methane and carbon dioxide emitted by agriculture (methane), transport, industry and households (carbon dioxide). Carbon dioxide is considered as a big threat for climate changes because of its very powerful emissions all over the world. There are different ways for remedy of this global threat. First, it is to increase the energy efficiency to spend less carbon containing fuels in transport and industry. Another way is to replace, at least partially, the carbon containing fossil fuels by renewable ones, like wind, solar energy and waterpower, or by recyclable biomass. The third one is to recycle the emitted carbon dioxide to fuels (e.g. methane, synthesis gas, light hydrocarbons) and/or useful chemical products (methanol, formic acid, etc.). Mostly the carbon dioxide recycling is based on endothermic processes requiring input of energy thus polluting atmosphere with carbon dioxide in the general case. That is why a carbon-free sources of energy must be applied. Fuel cell applications seem promising to such a purpose.This minireview presents a comparison of the available data for reverse carbon dioxide conversion to methane and organic compounds.

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)0.001
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 score0.088
Threshold uncertainty score0.443

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.021
GPT teacher head0.285
Teacher spread0.265 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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