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Record W4415320818 · doi:10.1016/j.isci.2025.113787

How energy and chemistry converge for a fossil-free future

2025· article· en· W4415320818 on OpenAlexaff
Jan Mertens, Christian Breyer, Ronnie Belmans, Corinne Gendron, Patrice Geoffron, Carolyn Fischer, Elodie Du Fornel, Olivier Ledent, Richard K. Lester, Kimberly A. Nicholas, Laura Megrelis, P. Miranda, Alice Prudhomme, Peter Verwee, Olivier Sala, Michael E. Webber, Koenraad Debackere

Bibliographic record

VenueiScience · 2025
Typearticle
Languageen
FieldChemistry
TopicVarious Chemistry Research Topics
Canadian institutionsUniversité du Québec à Montréal
FundersKU LeuvenUniversiteit UtrechtCovestroUniversiteit GentEuropean Chemical Industry Council
KeywordsRenewable energyProcess (computing)Sustainable energyEnergy (signal processing)Carbon fibersDual (grammatical number)Sustainable developmentGreen chemistry

Abstract

fetched live from OpenAlex

as sustainable carbon sources and explore the enabling potential of electrification, low-carbon hydrogen, and direct air capture. Novel process pathways and infrastructure scenarios are analyzed to highlight strategic opportunities for cross-sectoral collaboration. Our findings underscore the need for coordinated investment, policy support, and alignment with renewable energy geography to achieve a resilient, fossil-free future.

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 score0.353
Threshold uncertainty score0.523

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 science0.0010.000
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 head0.010
GPT teacher head0.253
Teacher spread0.244 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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