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Record W3216196929 · doi:10.3389/fceng.2021.752364

Performances of Homogeneous and Heterogenized Methylene Blue on Silica Under Red Light in Batch and Continuous Flow Photochemical Reactors

2021· article· en· W3216196929 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueFrontiers in Chemical Engineering · 2021
Typearticle
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsnot available
FundersMcGill University
KeywordsMethylene bluePhotocatalysisBatch reactorHomogeneousPhotochemistryChemistryCatalysisReactivity (psychology)Continuous reactorChemical engineeringOrganic chemistryThermodynamics

Abstract

fetched live from OpenAlex

Methylene blue was efficiently immobilized on silica micro- and nanoparticles by electrostatic interactions and the performances of the heterogenized photocatalysts were compared against the homogeneous conditions using the photooxidation of citronellol as a model reaction under red light, in a batch and a continuous flow photochemical reactor. In batch, the heterogeneous photocatalyst outperforms the homogeneous one, presumably due to kinetic and stability effects. The two catalytic systems are also compared in a flow reactor displaying improved mass transfer properties. We demonstrate that this results in a dramatic enhancement in photocatalyst stability, reactivity and productivity. This study highlights the importance of photocatalyst stability under homogeneous versus heterogenized conditions and in batch versus flow photochemistry.

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 categoriesMeta-epidemiology (narrow)
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 score0.058
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.005
GPT teacher head0.208
Teacher spread0.203 · 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