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Record W2075698130 · doi:10.1021/ie060927t

Adsorption of Non-ionic Surfactants onto Sand and Its Importance in Naphthalene Removal

2006· article· en· W2075698130 on OpenAlexafffund
Santanu Paria, Pak K. Yuet

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2006
Typearticle
Languageen
FieldChemistry
TopicSurfactants and Colloidal Systems
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaNova Scotia Research Innovation Trust
KeywordsAdsorptionChemistryNaphthalenePulmonary surfactantEthylene oxideChemical engineeringChromatographyOrganic chemistry

Abstract

fetched live from OpenAlex

The kinetic and equilibrium adsorption studies of four NP (nonylphenyl ethoxylates) series non-ionic surfactants with different EO (ethylene oxide) groups on sand are presented here. The adsorption behavior of a NP series of surfactants is compared in batch and continuous column studies. The adsorption isotherms are found to be similar in nature in all cases, and the maximum amount adsorbed per gram of sand decreases with an increasing number of EO groups. The comparison of maximum amounts adsorbed in the batch and column shows that the amount adsorbed is the same for both cases. When two surfactants were mixed with a calculated average EO number, the mixed solution showed the same equilibrium amount adsorbed to that of the actual EO number, although transportation through the column showed different behavior. The organic removal efficiency of the surfactants from a sand column depends on the adsorption density on the sand surface and the lowering of the surface tension at the air−water interface. The order of naphthalene removal efficiency of different NP surfactants from a sand column are NP-40 < NP-15 < NP-9. The main objective of this study is to improve the knowledge of surfactant adsorption and its importance in organic removal from the sand surface for the application of soil and groundwater remediation.

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)0.001
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 score0.025
Threshold uncertainty score0.949

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.050
GPT teacher head0.298
Teacher spread0.248 · 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

Citations27
Published2006
Admission routes2
Has abstractyes

Explore more

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