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Environmental implications of solid oxide fuel cell system for hydrogen sustainability

2025· article· en· W4406902670 on OpenAlexaff
Xinmiao Wei, Shivom Sharma, Arthur Waeber, Du Wen, Manuele Margni, François Maréchal, Jan Van herle

Bibliographic record

VenueResources Conservation and Recycling · 2025
Typearticle
Languageen
FieldMaterials Science
TopicAdvancements in Solid Oxide Fuel Cells
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsSustainabilitySolid oxide fuel cellWaste managementFuel cellsEnvironmental scienceHydrogenBusinessNatural resource economicsEngineeringChemistryChemical engineeringEconomicsEcology

Abstract

fetched live from OpenAlex

Hydrogen, known for its high energy content and clean combustion, is promising in the energy transition. This study explores the environmental impact of a solid oxide fuel cell (SOFC) system. 1 kg of hydrogen production at 1 bar serves as the functional unit. The SOFC system generates hydrogen, electricity, and heat across five modes. Results indicate that the SOFC system achieves a global warming potential of 0.17–9.50 kg CO 2 -eq/FU using the system expansion method. Regional analysis shows that areas with high renewable electricity shares experience increased CO 2 emissions due to functional unit decision. The exergy allocation method is less sensitive to electricity sources and seasonal emission profiles than system expansion. Comparing eight production routes, the SOFC system using biomethane (−5.46 kg CO 2 -eq/FU) outperforms steam methane reforming (11 kg CO 2 -eq/FU) and biomass gasification (1.49 kg CO 2 -eq/FU). These insights are valuable for advancing renewable energy initiatives and effectively mitigating climate change. • Study a novel SOFC system design that generates electricity, hydrogen, and heat. • Details LCA results for climate change, ecosystem quality and human health impacts. • Evaluates emissions using different electricity sources, fuel types, and profiles. • Highlights benefits of system expansion and exergy allocation in LCA multifunctionality. • Provides new insight on hydrogen categorization against conventional methods.

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)0.000
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 score0.056
Threshold uncertainty score0.414

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 head0.011
GPT teacher head0.271
Teacher spread0.260 · 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

Citations13
Published2025
Admission routes1
Has abstractyes

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