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Record W2729602027 · doi:10.1021/acs.langmuir.7b01246

Complex Formations between Surfactants and Polyelectrolytes of the Same Charge on a Water Surface

2017· article· en· W2729602027 on OpenAlexafffund
Amirhossein Mafi, Dan Hu, Keng C. Chou

Bibliographic record

VenueLangmuir · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpectroscopy and Quantum Chemical Studies
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of British Columbia
KeywordsPolyelectrolyteChemistryDivalentIonHydrogen bondPulmonary surfactantPhase (matter)Surface chargeChemical physicsSodiumInorganic chemistryMoleculePolymerPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The mechanism of complex formation between surfactants and polyelectrolytes with the same charge on the water surface was investigated using molecular dynamics simulations and phase-sensitive sum-frequency generation vibrational spectroscopy. Although complex formation between highly charged surfactants and polyelectrolytes of the same charge is generally expected to be prohibited by the electrostatic repulsive force, our study shows that it is possible to form thermodynamically stable complexes when excess ions are present in the solution. We found that anionic partially hydrolyzed polyacrylamide (HPAM) could interact with anionic sodium dodecyl sulfate (SDS) on a water surface in the presence of salts. With excess Na + ions in the solution, the charge screening effect allows HPAM to weakly interact with SDS via hydrogen bonds. In the presence of divalent Ca 2+ ions, the surfactant and the polymer are strongly coupled by forming Ca 2+ ion bridges and hydrogen bonds. Our calculation shows that the presence of Ca 2+ ions creates a steep binding energy of ∼30 kJ/mol near the water surface. These results were qualitatively verified using phase-sensitive sum-frequency generation vibrational spectroscopy.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.338
Threshold uncertainty score0.314

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)0.0000.000

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 head0.029
GPT teacher head0.282
Teacher spread0.252 · 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 designObservational
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

Citations29
Published2017
Admission routes2
Has abstractyes

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