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Record W4409956042 · doi:10.5539/apr.v17n1p158

Investigation and Monitoring of Rheological Behavior of Mg(OH)2 Nanoparticles at the Water / Oil Interface

2025· article· en· W4409956042 on OpenAlexvenueno aff
Papa Mady Sy, Nicolas Anton, Sidy Mouhamed Dieng, Alphonse Rodrigue Djiboune, A. Faye, Louis Augustin Diaga Diouf, Boucar Ndong, Gora Mbaye, Thierry Vandamme, Mounibé Diarra

Bibliographic record

VenueApplied Physics Research · 2025
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsnot available
FundersUniversité de Strasbourg
KeywordsRheologyMaterials scienceNanoparticleInterface (matter)Chemical engineeringComposite materialNanotechnologyContact angle

Abstract

fetched live from OpenAlex

Objectives of this study focused is understanding the mechanisms involved in the stabilization of water / oil interfaces by solid nanoparticles (NPs). Magnesium hydroxide of different sizes and in different electrolyte concentrations were studied and compared, at a model water / cyclohexane interface, in a drop tensiometer. Gradual interfacial adsorption of NPs, initially dispersed in water, were followed by tensiometry and the interfacial behavior of NPs layers were characterized by two-dimensional rheology. Owing to the direct relationship between emulsion stability and the interfacial properties of these layers, different nanoparticulate systems were compared, magnesium hydroxide (Mg(OH)2) NPs, with different size. In addition, the concentration of electrolytes (NaCl) in the bulk phase was shown to induce a partial NPs aggregation (so-called NPs flocs), with an important incidence on the interfacial layer stability. Theoretical phase shift was calculated from Winter and Chambon models corresponding to a two-dimensional gel behavior at the gel-point. This interfacial gelation results in strengthening the interfacial layer, and is actually a considerable advantage in the stabilization of Pickering emulsions.

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 score0.020
Threshold uncertainty score0.176

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 head0.053
GPT teacher head0.337
Teacher spread0.284 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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