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Record W2023767577 · doi:10.1520/jai104302

Naphthalene Sorption to Organic Additives in Cement-Treated Soils

2012· article· en· W2023767577 on OpenAlexaff
Craig B. Lake, Jill Searle, Evan Bridson-Pateman

Bibliographic record

VenueJournal of ASTM International · 2012
Typearticle
Languageen
FieldEngineering
TopicElectrokinetic Soil Remediation Techniques
Canadian institutionsDalhousie University
Fundersnot available
KeywordsNaphthaleneSorptionCementSoil waterMaterials scienceChemical engineeringEnvironmental chemistryChemistryEnvironmental scienceComposite materialOrganic chemistryAdsorptionSoil science

Abstract

fetched live from OpenAlex

Abstract Cement-based solidification/stabilization (S/S) is a widely used source-controlled treatment method for contaminated sediments. Increasingly, the technology is being used to remediate sites that contain high molecular weight organic compounds. The low level of organic content in cement-based S/S mixtures often creates a need for organic additives in the mixtures in order to improve the level of sorption in the treatment process. Very little work has been published related to the quantification of the sorption of organic contaminants to cement-based S/S mixtures and the level of improvement afforded by additives such as fly ash and organoclays. The objective of this study is to examine the sorption levels of naphthalene to several cement-treated soil mixtures with and without organic additives (i.e., fly ash and organoclay) using batch testing. It is found that the sorption values of naphthalene vary but appear to be dependent on the amount of organic carbon present in the mixture. In order to assess the potential benefit of this improved sorption for field applications, contaminant migration modeling is performed using the results obtained. It is shown that cement-based S/S remediation systems can provide long-term protection against naphthalene contaminant migration, especially cement-based S/S mixtures with organoclay additives, for the assumptions 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 categoriesInsufficient 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 score0.172
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 integrity0.0000.000
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 head0.007
GPT teacher head0.240
Teacher spread0.233 · 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

Citations1
Published2012
Admission routes1
Has abstractyes

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