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Record W2167340934 · doi:10.1039/c4nr07224c

Nanocomposite heterojunctions as sunlight-driven photocatalysts for hydrogen production from water splitting

2015· article· en· W2167340934 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.

Bibliographic record

VenueNanoscale · 2015
Typearticle
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsSiliCycle (Canada)Université Laval
Fundersnot available
KeywordsNanocompositeSunlightWater splittingHeterojunctionHydrogen productionMaterials sciencePhotocatalysisHydrogenChemical engineeringPhotochemistryNanotechnologyChemistryOptoelectronicsCatalysisOpticsOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

Hydrogen production via photocatalytic water splitting using sunlight has enormous potential in solving the worldwide energy and environmental crisis. The key challenge in this process is to develop efficient photocatalysts which must satisfy several criteria such as high chemical and photochemical stability, effective charge separation and strong sunlight absorption. The combination of different semiconductors to create composite materials offers a promising way to achieve efficient photocatalysts because doing so can improve the charge separation, light absorption and stability of the photocatalysts. In this review article, we summarized the most recent studies on semiconductor composites for hydrogen production under visible light irradiation. After a general introduction about the photocatalysis phenomenon, typical heterojunctions of widely studied heterogeneous semiconductors, including titanium dioxide, cadmium sulfide and graphitic carbon nitride are discussed in detail.

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 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 score0.140
Threshold uncertainty score0.966

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 head0.023
GPT teacher head0.278
Teacher spread0.255 · 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