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Persulfate Treatment of Dissolved Gasoline Compounds

2013· article· en· W1972327890 on OpenAlexafffund
Neil R. Thomson, Jim Barker

Bibliographic record

VenueJournal of Hazardous Toxic and Radioactive Waste · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced oxidation water treatment
Canadian institutionsUniversity of WaterlooGolder Associates (Canada)
FundersNational Research Council Canada
KeywordsPersulfateChemistryEthylbenzeneTolueneBenzeneBTEXGasolineInorganic chemistryPeroxideHydrocarbonNuclear chemistryEnvironmental chemistryOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

Bench-scale treatability of an ensemble of gasoline compounds was investigated using unactivated and activated persulfate. The activation strategies explored were chelated-iron, peroxide, alkaline conditions, and the presence of aquifer solids. Batch reactor trials were designed with an initial total petroleum hydrocarbon (TPH) concentration of ∼25 mg/L, and nine organic compounds were monitored over a 28-day reaction period. First-order oxidation rate coefficients (kobs) were estimated for all experimental trials. Unactivated persulfate at a concentration of 20 g/L resulted in almost complete oxidation of benzene, toluene, ethylbenzene, and xylenes (BTEX) (>99%), trimethylbenzenes (>95%), and significant oxidation of naphthalene (∼70%). Oxidation rate coefficients were enhanced by 2–15 times using the peroxide or chelated-iron activation strategy. Alkaline activation at pH 11 or 13 yielded kobs that were ∼2 times higher than the unactivated case, except for the kobs for benzene, toluene, and ethylbenzene, which were reduced by 50% at pH 13. Natural activation by two aquifer materials resulted in kobs similar to the unactivated case. Significant oxidant strength (60–85%) was observed in all 20 g/L persulfate reactors, implying significant persulfate persistence under gasoline-contaminated conditions. The overall bulk gasoline stoichiometry for these experimental trials varied from 120 to 340 g-persulfate/g-TPH.

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 score0.170
Threshold uncertainty score0.429

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 head0.008
GPT teacher head0.224
Teacher spread0.215 · 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

Citations34
Published2013
Admission routes2
Has abstractyes

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