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Record W4244517206 · doi:10.32920/ryerson.14660337.v1

Photolytic and Photocatalytic Treatment of Linear Alkylbenzene Sulfonate in Water

2021· preprint· en· W4244517206 on OpenAlexafffund
Sarah Rebecca Hatfield Venhuis

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicWater Quality Monitoring and Analysis
Canadian institutionsMcMaster UniversityToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhotocatalysisLinear alkylbenzeneHydrogen peroxideSulfonateDegradation (telecommunications)ChemistryPhotodissociationIrradiationReaction rate constantCatalysisSlurryBatch reactorKineticsNuclear chemistryPhotochemistryPulmonary surfactantMaterials scienceOrganic chemistrySodium

Abstract

fetched live from OpenAlex

Treatment of linear alkylbenzene sulfonate using various photolytic and photocatalytic processes is described. Based on first order rates, it is shown that 5,000 mg/L of H²O² for degradation of a 100 mg/L solution of linear alkylbenzene sulfonate is optimum. Two different photocatalysts, Degussa P25 and Hombikat UV 100 TiO², are used to degrade LAS in slurry batch reactors. The optimum photocatalyst loading for Degussa P25 is higher than UV 100 for treatment of LAS since >20% adsorbs to the surface of the UV 100 photocatalyst. Combination of photocatalysts does not improve degradation rates in batch tests. Combination of Degussa P25 and 600 mg/L H²O² and irradiation with either UV light at 254 or 365 nm does not improve degradation rates over the photocatalytic or photolytic processes individually. Photolysis of LAS with UV light at 254 nm and 600 mg/L H²O² added at different time intervals was not successful and no improvement in the first order rate constant was observed. For optimum results, the hydrogen peroxide was added at the beginning of irradiation.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.275
Threshold uncertainty score0.984

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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.265
Teacher spread0.238 · 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

Citations0
Published2021
Admission routes2
Has abstractyes

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