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Record W2995145484 · doi:10.1073/pnas.1821686116

Renewable electricity storage using electrolysis

2019· article· en· W2995145484 on OpenAlexfundno aff
Zhifei Yan, Jeremy L. Hitt, John A. Turner, Thomas E. Mallouk

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2019
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsnot available
FundersCanadian Institute for Advanced ResearchU.S. Department of Energy
KeywordsElectrolysisRenewable energyElectrolysis of waterEnergy storageChemical energyProcess engineeringElectricityPower to gasPolymer electrolyte membrane electrolysisEnvironmental scienceHigh-temperature electrolysisWaste managementChemistryElectrolyteEngineeringElectrodeElectrical engineeringPower (physics)Physics

Abstract

fetched live from OpenAlex

Electrolysis converts electrical energy into chemical energy by storing electrons in the form of stable chemical bonds. The chemical energy can be used as a fuel or converted back to electricity when needed. Water electrolysis to hydrogen and oxygen is a well-established technology, whereas fundamental advances in CO 2 electrolysis are still needed to enable short-term and seasonal energy storage in the form of liquid fuels. This paper discusses the electrolytic reactions that can potentially enable renewable energy storage, including water, CO 2 and N 2 electrolysis. Recent progress and major obstacles associated with electrocatalysis and mass transfer management at a system level are reviewed. We conclude that knowledge and strategies are transferable between these different electrochemical technologies, although there are also unique complications that arise from the specifics of the reactions involved.

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: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.028
GPT teacher head0.289
Teacher spread0.261 · 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

Citations245
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the National Academy of SciencesSame topicCO2 Reduction Techniques and CatalystsFrench-language works237,207