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Record W1879508563 · doi:10.1139/cgj-2012-0198

Examining fly ash as a sorbent for benzene, trichloroethylene, and ethylbenzene in cement-treated soils

2013· article· en· W1879508563 on OpenAlexafffundvenue
Craig B. Lake, Ghazal Arefi, Pak K. Yuet

Bibliographic record

VenueCanadian Geotechnical Journal · 2013
Typearticle
Languageen
FieldChemistry
TopicRadioactive element chemistry and processing
Canadian institutionsUniversity of British ColumbiaDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEthylbenzeneSorptionFly ashSorbentTrichloroethyleneCementBenzeneFreundlich equationEnvironmental chemistryBTEXChemistryEnvironmental scienceWaste managementMaterials scienceAdsorptionComposite materialOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

The objective of this study was to examine the potential for beneficial reuse of fly ash as a sorbent for benzene, trichloroethylene, and ethylbenzene in soils treated with cement (i.e., cement-based solidification/stabilization (S/S)). Batch testing was performed with soil–cement mixtures containing fly ash and compared with similar samples with humic acid as a source of organic carbon. The experimental batch test results were fit to the Freundlich sorption model assuming both linear and nonlinear behavior. It was found that the level of sorption is low for benzene and trichloroethylene, but relatively higher for ethylbenzene. Cement addition appeared to not only decrease sorption values obtained for fly ash, but also eliminate the nonlinear sorptive behavior, likely due to the blocking–coating of sorption sites. To demonstrate the practical application of the sorption observed, a hypothetical contaminated site is modeled with a one-dimensional contaminant transport program. It iss shown that the addition of fly ash can potentially reduce off-site migration of trichloroethylene (TCE), but is more effective at reducing ethylbenzene migration, for the time frames considered in the modeling.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.357
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.249
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 teacher head, not a consensus.

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

Citations5
Published2013
Admission routes3
Has abstractyes

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