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Record W2732119785 · doi:10.1021/acsenergylett.7b00392

Alternative Fuel Cell Technologies for Cogenerating Electrical Power and Syngas from Greenhouse Gases

2017· article· en· W2732119785 on OpenAlexafffund
Meng Li, Bin Hua, Jing‐Li Luo

Bibliographic record

VenueACS Energy Letters · 2017
Typearticle
Languageen
FieldMaterials Science
TopicAdvancements in Solid Oxide Fuel Cells
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaClimate Change and Emissions Management Corporation
KeywordsSyngasFlexibility (engineering)Greenhouse gasProcess engineeringSolid oxide fuel cellElectricityChemical energyProcess (computing)Efficient energy useEnergy transformationFuel cellsElectricity generationEnvironmental scienceAnodePower (physics)Computer scienceEngineeringChemistryElectrical engineeringChemical engineering

Abstract

fetched live from OpenAlex

Increasing environment awareness and energy demands are the reasons for emerging energy technologies with ecofriendliness and high efficiency. Of the various candidates, the solid oxide fuel cell (SOFC) is very appealing because of its high efficiency and fuel flexibility. Traditionally, SOFCs directly convert the chemical energies of the readily available fuels into electricity with H 2 O and CO 2 as the products, which is very promising in terms of the energy efficiency yet leads to CO 2 emission in practice. In fact, SOFCs are able to allow in situ CO 2 –CH 4 reforming and H 2 selective electro-oxidation in their anodes. Such a process enables a sustainable path to produce electrical power and syngas from CO 2 but is hindered by several issues. This Perspective discusses the main technical challenges of this process and available approaches achieved so far. The potential future directions for advancing this technology are also pointed out.

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: none
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.015
GPT teacher head0.256
Teacher spread0.241 · 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

Citations45
Published2017
Admission routes2
Has abstractyes

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