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Record W3090282214 · doi:10.1016/j.nanoen.2020.105416

Efficient and stable photoelectrochemical hydrogen generation using optimized colloidal heterostructured quantum dots

2020· article· en· W3090282214 on OpenAlexafffund
Hui Zhang, Lucas V. Besteiro, Jiabin Liu, Chao Wang, Gurpreet Singh Selopal, Zhangsen Chen, David Barba, Zhiming M. Wang, Haiguang Zhao, Gregory P. Lopinski, Shuhui Sun, Federico Rosei

Bibliographic record

VenueNano Energy · 2020
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsNational Research Council CanadaInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Electronic Science and Technology of ChinaCanada Research ChairsNatural Science Foundation of Shandong ProvinceFonds de recherche du Québec – Nature et technologiesUnited Nations Educational, Scientific and Cultural Organization
KeywordsMaterials scienceQuantum dotPhotocurrentOptoelectronicsAbsorption (acoustics)Band gapDelocalized electronSemiconductorNanotechnologyElectronWater splittingComposite materialPhotocatalysis

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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 score0.327
Threshold uncertainty score0.567

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 science0.0000.000
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 head0.029
GPT teacher head0.220
Teacher spread0.191 · 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

Citations65
Published2020
Admission routes2
Has abstractno

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