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Record W2127600166 · doi:10.1002/ceat.201500305

Efficiency Factors in Photocatalytic Reactors: Quantum Yield and Photochemical Thermodynamic Efficiency Factor

2015· article· en· W2127600166 on OpenAlexaffabout
Hugo de Lasa, Benito Serrano, Jesús Moreira, Patricio J. Valadés-Pelayo

Bibliographic record

VenueChemical Engineering & Technology · 2015
Typearticle
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsWestern University
Fundersnot available
KeywordsChemistryPhotocatalysisHuman decontaminationYield (engineering)EnthalpyQuantum yieldPhotonKineticsPhotochemistryThermodynamicsWaste managementOrganic chemistryOpticsCatalysis

Abstract

fetched live from OpenAlex

Abstract Photocatalytic efficiency is evaluated using quantum yields (QYs) and the photochemical thermodynamic efficiency factor (PTEF). The PTEF allows establishing reactor efficiency as the ratio of utilized enthalpy for the formation of consumed OH• free radicals over the absorbed photon energy. A key consideration for the evaluation of efficiency factors is the establishment of macroscopic energy balances together with an accurate assessment of evolved and absorbed photons. Of considerable help are the experimental devices developed at the Chemical Reactor Engineering Centre (CREC)/University of Western Ontario (UWO) laboratories. Photoconversion kinetics is required for calculation of the OH• consumption rates and establishment of the related kinetic parameters. PTEFs and QYs have been applied by CREC‐UWO researchers for efficiency calculations in photocatalytic reactors for the decontamination of air, water, and hydrogen production.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.014
GPT teacher head0.239
Teacher spread0.225 · 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 source (direct Gemma or distilled Codex), 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

Citations24
Published2015
Admission routes2
Has abstractyes

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