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Record W2913460186 · doi:10.3390/catal9020181

Photocatalytic Degradation of Microcystins by TiO2 Using UV-LED Controlled Periodic Illumination

2019· article· en· W2913460186 on OpenAlexafffund
Olivia M. Schneider, Robert Liang, Leslie M. Bragg, Ivana Jaciw-Zurakowsky, Azar Fattahi, Shasvat Rathod, Peng Peng, Mark R. Servos, Y. Zhou

Bibliographic record

VenueCatalysts · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsUniversity of Waterloo
FundersWaterloo Institute for Nanotechnology, University of WaterlooNatural Sciences and Engineering Research Council of Canada
KeywordsPhotocatalysisTitanium dioxideDegradation (telecommunications)MicrocystinChemistryFiltration (mathematics)CatalysisPollutantWater treatmentEnvironmental chemistryChemical engineeringMaterials scienceChromatographyEnvironmental scienceCyanobacteriaEnvironmental engineeringOrganic chemistryMetallurgyComputer science

Abstract

fetched live from OpenAlex

Toxic microcystins (MCs) produced by freshwater cyanobacteria such as Microcystis aeruginosa are of concern because of their negative health and economic impacts globally. An advanced oxidation process using UV/TiO2 offers a promising treatment option for hazardous organic pollutants such as microcystins. The following work details the successful degradation of MC-LA, MC-LR, and MC-RR using a porous titanium–titanium dioxide (PTT) membrane under UV-LED light. Microcystin quantitation was achieved by sample concentration and subsequent LC–MS/MS analysis. The PTT membrane offers a treatment option that eliminates the need for the additional filtration or separation steps required for traditional catalysts. Controlled periodic illumination was successfully used to decrease the total light exposure time and improve the photonic efficiency for a more cost-effective treatment system. Individual degradation rates were influenced by electrostatic forces between the catalyst and differently charged microcystins, which can potentially be adjusted by modifying the solution pH and the catalyst’s isoelectric point.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.699
Threshold uncertainty score0.612

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.006
GPT teacher head0.214
Teacher spread0.208 · 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

Citations32
Published2019
Admission routes2
Has abstractyes

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