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Record W2765262905 · doi:10.3390/catal7110324

Hydrogen Production via Water Dissociation Using Pt–TiO2 Photocatalysts: An Oxidation–Reduction Network

2017· article· en· W2765262905 on OpenAlexafffund
Jesus Fabricio Guayaquil‐Sosa, Alan Calzada, Benito Serrano, Salvador Escobedo, Hugo de Lasa

Bibliographic record

VenueCatalysts · 2017
Typearticle
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsWestern University
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaConsejo Nacional de Ciencia y TecnologíaSecretaría de Educación Pública
KeywordsHydrogenAcetaldehydeDissociation (chemistry)ChemistryHydrogen productionInert gasPhotocatalysisHydrogen peroxideMethanolInorganic chemistryPhotochemistryScavengerEthanolCatalysisRadicalOrganic chemistry

Abstract

fetched live from OpenAlex

Several TiO2 based semiconductors with different Pt loadings are prepared using incipient impregnation, wet impregnation and the sol-gel method. These photocatalysts are evaluated in the Photo-CREC-Water II Photoreactor for hydrogen production via water dissociation, using an organic renewable scavenger (ethanol). Results obtained show the influence of the photocatalyst preparation in the production of hydrogen and in the observed quantum yields. Furthermore, it is established that the reaction networks leading to hydrogen production, using various photocatalysts, share common features. This analysis is developed by both identifying and quantifying different chemical species and their changes with irradiation time. Key species in this oxidation–reduction network are hydrogen, hydrogen peroxide, ethanol, methane, ethane, acetaldehyde and carbon dioxide. On this basis, it is shown that under an inert gas atmosphere, ethanol consumption is sub-stoichiometric. This points towards simultaneous ethanol consumption and the formation of the ethanol scavenger.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
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 score0.007
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.003
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 head0.025
GPT teacher head0.301
Teacher spread0.275 · 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.

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

Citations31
Published2017
Admission routes2
Has abstractyes

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