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Record W3013568791 · doi:10.1016/j.isci.2020.101012

Synergizing Photo-Thermal H2 and Photovoltaics into a Concentrated Sunlight Use

2020· article· en· W3013568791 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

VenueiScience · 2020
Typearticle
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsUniversity of Toronto
FundersChinese Academy of Agricultural SciencesChinese Academy of SciencesNational Natural Science Foundation of China
KeywordsPhotovoltaicsSunlightPhotovoltaic systemSolar energyMaterials scienceRenewable energyOptoelectronicsOpticsPhysicsElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

in water/methanol serves as a spectrum selector, absorbing ultraviolet-visible and infrared energy for rapid photo-thermochemical hydrogen production. The transmitted visible and near-infrared energy fits the photovoltaic bandgap and retains the high efficiency of a commercial photovoltaic cell under different solar concentration values. The experimental design achieved an overall efficiency of 4.2% under 12 suns solar concentration. Furthermore, the results demonstrated a reduced energy loss in full-spectrum energy conversion into hydrogen and electricity. Such simple integration of photo-thermochemical hydrogen and photovoltaics would create a pathway toward cascading use of sunlight energy.

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 score0.048
Threshold uncertainty score0.691

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.024
GPT teacher head0.266
Teacher spread0.242 · 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