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Seven ways to decouple low-temperature water electrolysis - A comparative review for alternative green hydrogen production

2024· review· en· W4407847813 on OpenAlexfundno aff
Eva Wallnöfer‐Ogris, Ilena Grimmer, Bernd Loder, Marie Macherhammer, Alexander Trattner

Bibliographic record

VenueInternational Journal of Hydrogen Energy · 2024
Typereview
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsnot available
FundersBundesministerium für Digitalisierung und WirtschaftsstandortÖsterreichische ForschungsförderungsgesellschaftAustrian Institute of TechnologyBundesministerium für Klimaschutz, Umwelt, Energie, Mobilität, Innovation und TechnologieTechnische Universitat WienSimon Fraser UniversityBundesministerium für Arbeit und Wirtschaft
KeywordsHydrogen productionElectrolysisElectrolysis of waterProduction (economics)High-temperature electrolysisHydrogenProcess engineeringWater splittingEnvironmental scienceMaterials scienceChemistryCatalysisPhysical chemistryEngineeringEconomicsElectrodeOrganic chemistry

Abstract

fetched live from OpenAlex

This review provides an overview of alternative, decoupled low-temperature water electrolysis systems for the production of green hydrogen that are currently being researched and developed. The continuous or sequential processes utilise a redox mediator enabling the decoupling of the hydrogen and oxygen evolution reactions spatially and/or in time. The different requirements for the redox mediators and the system characteristics are compared, highlighting the physical and chemical limitations of the individual systems, as well as their respective strengths and potential for further development. This indicates that the advantages of decoupled electrolysis compared to direct electrolysis can go far beyond their impressive high faradaic efficiency and operational safety, even at high hydrogen pressure and operation at low current densities . Consequently, it can be presumed that these systems could become a cost- and energy-efficient technology, thereby establishing themselves as a competitive future technology in a multitude of application scenarios.

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)0.001
metaresearch head score (Gemma)0.001
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: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
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 head0.036
GPT teacher head0.325
Teacher spread0.289 · 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
GenreReview

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

Citations3
Published2024
Admission routes1
Has abstractyes

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