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Record W2047976148 · doi:10.1021/ie050808y

Effects of Chain Length and Electrolyte on the Adsorption of <i>n</i>-Alkylpyridinium Bromide Surfactants at Sand−Water Interfaces

2005· article· en· W2047976148 on OpenAlexafffund
Santanu Paria, Pak K. Yuet

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2005
Typearticle
Languageen
FieldChemistry
TopicSurfactants and Colloidal Systems
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaNova Scotia Research Innovation Trust
KeywordsAdsorptionElectrolytePulmonary surfactantChemistryBromideDesorptionInorganic chemistrySaturation (graph theory)Cationic polymerizationChemical engineeringChromatographyOrganic chemistryPhysical chemistryElectrode

Abstract

fetched live from OpenAlex

The kinetic and equilibrium studies of the adsorption of four cationic surfactants (pyridinium bromide) with different chain lengths (C 16, C 14, C 12, and C 10 ) onto sand are presented here. The adsorption and desorption behavior in the absence and presence of different electrolytes (NaCl, CaCl 2, and Na 2 SO 4 ) are compared in batch and continuous column experiments. The kinetic studies show that the rates of adsorption of pyridinium bromide surfactants on sand surfaces are very high (∼70% of saturation adsorption occurs in 30 s) and are almost the same at low concentration (0.5 mM) for different chain lengths. The amount of surfactant adsorbed is enhanced by the presence of electrolyte because of a reduction in electrostatic repulsion among the headgroups, whereas the effect of the valence of coion does not appear to be important. Desorption studies based on column experiments show that a lower amount of surfactant is retained when eluted with electrolyte solution instead of pure water.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.033
GPT teacher head0.263
Teacher spread0.229 · 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 source (direct Gemma or distilled Codex), 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

Citations32
Published2005
Admission routes2
Has abstractyes

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