MétaCan
Menu
Back to cohort
Record W2228582453 · doi:10.1021/acs.langmuir.5b03893

Structural and Energetic Studies on the Interaction of Cationic Surfactants and Cellulose Nanocrystals

2016· article· en· W2228582453 on OpenAlexaff
César Brinatti, John S. Huang, Richard M. Berry, Kam Chiu Tam, Watson Loh

Bibliographic record

VenueLangmuir · 2016
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsCelluForce (Canada)University of Waterloo
FundersCiência sem FronteirasConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsPulmonary surfactantIsothermal titration calorimetryChemistryAlkylCationic polymerizationChemical engineeringSurface tensionMonolayerSurface chargeMicelleTitrationPolymer chemistryOrganic chemistryAqueous solutionPhysical chemistry

Abstract

fetched live from OpenAlex

We report a comprehensive study on the interactions between cationic surfactant homologues CnTAB (n = 12, 14, and 16) with negatively charged cellulose nanocrystals (CNCs). By combining different techniques, such as isothermal titration calorimetry (ITC), surface tension, light scattering, electrophoretic mobility, and fluorescence anisotropy measurements, we identified two different driving forces for the formation of surface induced micellar aggregates. For the C12TAB surfactant, a surfactant monolayer with the alkyl chains exposed to the water is formed via electrostatic interactions at low concentration. At a higher surfactant concentration, micellar aggregates are formed at the CNC surface. For the C14TAB and C16TAB systems, micellar aggregates are formed at the CNC surface at a much lower surfactant concentration via electrostatic interactions, followed by hydrophobic interactions between the alkyl chains. At higher surfactant concentration, charge neutralization and association of the surfactant decorated CNC aggregates led to flocculation.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.139

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 head0.043
GPT teacher head0.322
Teacher spread0.279 · 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

Citations70
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueLangmuirSame topicAdvanced Cellulose Research StudiesFrench-language works237,207