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Record W2903522745 · doi:10.6000/1929-6002.2018.07.07

Hydrogen Production by Photoreforming of Organic Compounds

2018· article· en· W2903522745 on OpenAlexvenueno aff
Ilenia Rossetti, Elnaz Bahadori, Alberto Villa, Laura Prati, Gianguido Ramis

Bibliographic record

VenueJournal of Technology Innovations in Renewable Energy · 2018
Typearticle
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsHydrogen productionProduction (economics)HydrogenChemistryBiochemical engineeringEnvironmental scienceOrganic chemistryEngineeringEconomics

Abstract

fetched live from OpenAlex

H2 is gaining attention as energy vector, particularly if produced from renewable sources.It may be produced through photoreforming of organic compounds that act as hole scavengers to improve hydrogen productivity with respect to direct water photosplitting. Methanol is used here as model molecule to investigate the effect of catalyst composition and of substrate concentration on photocatalytic activity. Simple catalysts formulations were selected, in order to propose an easily scalable technology with a poorly expensive material. TiO2 with different structure (anatase, rutile and a mixture of them) was used as semiconductor, doped with a small amount of Au (0.1 wt%) to improve the lifetime of photogenerated charges.A new photoreactor was set up, with external irradiation that improves the scale up feasibility and possible future application with solar energy. Methanol conversion and hydrogen productivity increased with increasing methanol concentration up to 15 wt%. Rutile led to the highest conversion, but TiO2 P25 showed the highest hydrogen productivity.The best result was achieved by treating a 15 wt% methanol solution with 0.1 wt%Au/TiO2 P25, which led to 0.276 mol H2 h-1 kgcat-1.

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.

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 score0.079
Threshold uncertainty score0.636

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.009
GPT teacher head0.261
Teacher spread0.252 · 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

Citations4
Published2018
Admission routes1
Has abstractyes

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