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Record W4403216372 · doi:10.1039/d4ta03761h

Boosting solar hydrogen production with polarized MOF-derived ferroelectric In<sub>2</sub>Se<sub>3</sub>/In<sub>2</sub>O<sub>3</sub> nanohybrids

2024· article· en· W4403216372 on OpenAlex
Li Shi, Daniele Benetti, Faying Li, Cătălin Harnagea, Qin Wei, Federico Rosei

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

VenueJournal of Materials Chemistry A · 2024
Typearticle
Languageen
FieldEngineering
TopicChalcogenide Semiconductor Thin Films
Canadian institutionsMcGill UniversityInstitut National de la Recherche Scientifique
FundersFonds de recherche du Québec – Nature et technologiesChina Scholarship CouncilNational Natural Science Foundation of China
KeywordsBoosting (machine learning)Hydrogen productionFerroelectricityMaterials scienceHydrogenNanotechnologyOptoelectronicsChemistryComputer scienceArtificial intelligenceOrganic chemistryDielectric

Abstract

fetched live from OpenAlex

A ferroelectric In 2 Se 3 /In 2 O 3 nanocomposite was fabricated using a MOF template for enhanced photoelectrochemical water splitting. The optimized device achieved 1.91 mA cm −2 , a 2.4× boost, due to improved charge separation from the heterostructure.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesMeta-epidemiology (narrow)
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 score0.005
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
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 head0.010
GPT teacher head0.206
Teacher spread0.195 · 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