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Record W2463600540 · doi:10.1021/acs.estlett.5b00207

A Mini-Fluidic UV Photoreaction System for Bench-Scale Photochemical Studies

2015· article· en· W2463600540 on OpenAlexaff
Mengkai Li, Zhimin Qiang, James R. Bolton, Jiuhui Qu, Wentao Li

Bibliographic record

VenueEnvironmental Science & Technology Letters · 2015
Typearticle
Languageen
FieldEngineering
TopicInnovative Microfluidic and Catalytic Techniques Innovation
Canadian institutionsUniversity of Alberta
FundersDirectorate-General for Research and InnovationNational Natural Science Foundation of China
KeywordsAbsorbanceFluencePhotodissociationUltravioletPhotochemistryCollimated lightIrradiationReaction rate constantFluidicsChemistryMaterials scienceAnalytical Chemistry (journal)OpticsOptoelectronicsLaserKineticsChromatographyPhysics

Abstract

fetched live from OpenAlex

A mini-fluidic ultraviolet (UV) photoreaction system (MUPS) has been developed for bench-scale photochemical studies. While ensuring a high accuracy in UV fluence measurements, the MUPS can also increase the maximal available fluence rate (FR) by ∼100-fold (i.e., similar to the practical FRs existing in engineering applications), as compared to the commonly used quasi -collimated beam apparatus, and measure sample absorbance online. Photolysis experiments with two chemical actinometers (KI/KIO 3 and atrazine) demonstrate that the MUPS can easily be applied to photochemical studies in both low (<100 mJ/cm 2 ) and high (≥100 mJ/cm 2 ) fluence ranges with accurate quantifications of FR and exposure time; in addition, online absorbance measurements greatly facilitate the determination of photochemical parameters (e.g., rate constants and quantum yields).

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)0.001
metaresearch head score (Gemma)0.001
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: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.015
GPT teacher head0.241
Teacher spread0.226 · 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

Citations9
Published2015
Admission routes1
Has abstractyes

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