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Record W2322128437 · doi:10.1021/ja5049175

Re-Evaluating the Surface Tension Analysis of Polyelectrolyte-Surfactant Mixtures Using Phase-Sensitive Sum Frequency Generation Spectroscopy

2014· article· en· W2322128437 on OpenAlexafffund
Dan Hu, Keng C. Chou

Bibliographic record

VenueJournal of the American Chemical Society · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpectroscopy and Quantum Chemical Studies
Canadian institutionsUniversity of British Columbia
FundersInstitute for Oil Sands Innovation, University of AlbertaNatural Sciences and Engineering Research Council of CanadaUniversity of British ColumbiaCanada Foundation for InnovationOracle
KeywordsChemistrySurface tensionPulmonary surfactantPolyelectrolyteCationic polymerizationAqueous solutionSurface chargeEnthalpySodium dodecyl sulfateGibbs isothermMoleculeSpectroscopyPhase (matter)Chemical engineeringAnalytical Chemistry (journal)Inorganic chemistryChromatographyThermodynamicsPhysical chemistryPolymer chemistryOrganic chemistryPolymer

Abstract

fetched live from OpenAlex

Surface tension (ST) has been the most important measure of a molecule's surface activity. However, in many cases the complex behaviors of ST are challenging to interpret. For example, aqueous solutions of sodium docecyl sulfate (SDS) and poly(diallyldimethylammonium chloride) (PDADMAC) show dramatic changes in ST when the concentration of SDS varies. Although surfactants are generally described as "substances that reduce surface tension", new evidence shows that ST may have little changes when a significant amount of SDS is present at the water surface. The decrease of surface entropy resulting from a better ordering of interfacial molecules, such as water, counteracts the decrease of surface enthalpy and is able to keep the ST nearly unchanged. The dramatic ST decrease and recovery of the SDS-PDADMAC mixtures was discovered to be a result of a surface charge reversal. Similar surface charge reversal was also observed in cationic surfactant and anionic polyelectrolyte mixtures.

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 score0.093
Threshold uncertainty score0.434

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.027
GPT teacher head0.334
Teacher spread0.307 · 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

Citations67
Published2014
Admission routes2
Has abstractyes

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