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Record W2999203166 · doi:10.1007/s40820-019-0349-y

Single-Atom Catalysts for Electrochemical Hydrogen Evolution Reaction: Recent Advances and Future Perspectives

2020· review· en· W2999203166 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueNano-Micro Letters · 2020
Typereview
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of ChinaInstitut national de la recherche scientifique
KeywordsCatalysisRenewable energyNanotechnologyHydrogen productionElectrochemistryElectrolysis of waterHydrogenElectrolysisFossil fuelWater splittingPower to gasAtom economyChemistryMaterials scienceBiochemical engineeringOrganic chemistryEcologyEngineeringPhysical chemistry

Abstract

fetched live from OpenAlex

Hydrogen, a renewable and outstanding energy carrier with zero carbon dioxide emission, is regarded as the best alternative to fossil fuels. The most preferred route to large-scale production of hydrogen is by water electrolysis from the intermittent sources (e.g., wind, solar, hydro, and tidal energy). However, the efficiency of water electrolysis is very much dependent on the activity of electrocatalysts. Thus, designing high-effective, stable, and cheap materials for hydrogen evolution reaction (HER) could have a substantial impact on renewable energy technologies. Recently, single-atom catalysts (SACs) have emerged as a new frontier in catalysis science, because SACs have maximum atom-utilization efficiency and excellent catalytic reaction activity. Various synthesis methods and analytical techniques have been adopted to prepare and characterize these SACs. In this review, we discuss recent progress on SACs synthesis, characterization methods, and their catalytic applications. Particularly, we highlight their unique electrochemical characteristics toward HER. Finally, the current key challenges in SACs for HER are pointed out and some potential directions are proposed as well.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.959
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.247
Teacher spread0.236 · 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