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Record W4383053637 · doi:10.5281/zenodo.8111502

2022 roadmap on low temperature electrochemical CO2 reduction

2022· article· en· W4383053637 on OpenAlexfundno aff
Ifan E. L. Stephens

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2022
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsnot available
FundersLawrence Livermore National LaboratoryOffice of Naval ResearchTata Steel NederlandNatural Sciences and Engineering Research Council of CanadaInstitut Universitaire de FranceMinistry of Science and ICT, South KoreaTata SteelOffice of Energy EfficiencyVillum FondenNational Research Foundation of KoreaFondazione Ticino OlonaU.S. Department of EnergyNational Natural Science Foundation of ChinaNational Aeronautics and Space AdministrationCalifornia Energy CommissionDanmarks GrundforskningsfondSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungSeoul National UniversityEuropean CommissionOffice of Fossil EnergyU.S. Air ForceDeutsche ForschungsgemeinschaftNational Research FoundationNational Research Council CanadaSmall Business Technology TransferInstitute for Basic ScienceNederlandse Organisatie voor Wetenschappelijk OnderzoekTomKat Center for Sustainable Energy, Stanford UniversityAgence Nationale de la RechercheInnovationsfondenBeijing National Laboratory for Molecular SciencesOffice of Energy Efficiency and Renewable EnergyNational Science Foundation
KeywordsReduction (mathematics)ElectrochemistryEnvironmental scienceMaterials scienceChemistryMathematicsElectrodePhysical chemistry

Abstract

fetched live from OpenAlex

Electrochemical CO<sub>2</sub> reduction (CO<sub>2</sub>R) is an attractive option for storing renewable electricity and for the sustainable production of valuable chemicals and fuels. In this roadmap, we review recent progress in fundamental understanding, catalyst development, and in engineering and scale-up. We discuss the outstanding challenges towards commercialization of electrochemical CO<sub>2</sub>R technology: energy efficiencies, selectivities, low current densities, and stability. We highlight the opportunities in establishing rigorous standards for benchmarking performance, advances in <em>in operando</em> characterization, the discovery of new materials towards high value products, the investigation of phenomena across multiple-length scales and the application of data science towards doing so. We hope that this collective perspective sparks new research activities that ultimately bring us a step closer towards establishing a low- or zero-emission carbon cycle.

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)0.006
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.047
Threshold uncertainty score0.159

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0040.006
Open science0.0020.005
Research integrity0.0070.004
Insufficient payload (model declined to judge)0.0470.024

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 head0.012
GPT teacher head0.224
Teacher spread0.212 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations35
Published2022
Admission routes1
Has abstractyes

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