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Record W2189249325

SULFONAMIDE PARTITIONING TO AQUEOUS CATIONIC MICELLAR SYSTEMS

2011· article· en· W2189249325 on OpenAlexfundno aff
Patrick J. Cashin

Bibliographic record

VenueQSpace (Queen's University Library) · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicChemical Analysis and Environmental Impact
Canadian institutionsnot available
FundersQueen's University
KeywordsSulfonamideCationic polymerizationAqueous solutionChemistryMicelleComputer scienceOrganic chemistry
DOInot available

Abstract

fetched live from OpenAlex

Advances in analytical chemistry have resulted in a growing body of literature showing measurable concentrations of pharmaceuticals in both drinking and wastewater.Removal of such chemicals is typically inefficient and often poorly characterized.To characterize one such method of removal (micellar enhanced ultrafiltration, (MEUF)), interactions of a cetyl trimethylammonium bromide (CTABr) surfactant and sulfonamide antibiotics were examined by NMR and semi-equilibrium dialysis (SED).The locus and orientation of binding in a micelle was established for seven sulfonamides by 1 H NMR, and it was found that hydrophilic sulfonamides showed weak coordination with the micelle, whereas hydrophobic sulfonamides penetrated into the micellar interior with coordination of the SO 2 NH group to the charged surface layer.Binding constants were determined by 1 H NMR and showed apparent order of magnitude differences between nuclei.Several compounds were unable to be characterized in this manner due to low change in chemical shift with addition of CTABr.SED was performed as an alternative method to determine binding constants.Values determined in this manner were higher than those determined by 1 H NMR. Binding constants were converted into changes in Gibbs free energy and used to evaluate and, where necessary, modify the orientation and locus proposed by 1 H NMR.Erwin Buncel, whose knowledge and encouragement were invaluable.Also, my thanks to Dr.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.238
Threshold uncertainty score0.999

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.002

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 head0.009
GPT teacher head0.159
Teacher spread0.151 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations0
Published2011
Admission routes1
Has abstractyes

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