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Record W2940839863 · doi:10.1021/acsami.9b03942

Nanoparticle Concentration vs Surface Area in the Interaction of Thiol-Containing Molecules: Toward a Rational Nanoarchitectural Design of Hybrid Materials

2019· article· en· W2940839863 on OpenAlexafffund
Keshav Goël, Matías Zúñiga-Bustos, Caitlin Lazurko, Erik Jacques, Constanza Galaz-Araya, Francisco Valenzuela-Henríquez, Natalia L. Pacioni, Jean‐François Couture, Horacio Poblete, Emilio I. Alarcón

Bibliographic record

VenueACS Applied Materials & Interfaces · 2019
Typearticle
Languageen
FieldChemistry
Topicthermodynamics and calorimetric analyses
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchFondo Nacional de Desarrollo Científico y TecnológicoUniversidad Nacional de Córdoba
KeywordsIsothermal titration calorimetryMaterials scienceThiolMoleculeNanoparticleDispersityNanotechnologyNanostructureSurface (topology)TitrationChemical engineeringPhysical chemistryPolymer chemistryOrganic chemistryChemistry

Abstract

fetched live from OpenAlex

The effect of accounting for the total surface in the association of thiol-containing molecules to nanosilver was assessed using isothermal titration calorimetry, along with a new open access algorithm that calculates the total surface area for samples of different polydispersity. Further, we used advanced molecular dynamic calculations to explore the underlying mechanisms for the interaction of the studied molecules in the presence of a nanosilver surface in the form of flat surfaces or as three-dimensional pseudospherical nanostructures. Our data indicate that not only is the total surface area available for binding but also the supramolecular arrangements of the molecules in the near proximity of the nanosilver surface strongly affects the affinity of thiol-containing molecules to nanosilver surfaces.

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 categoriesInsufficient payload (model declined to judge)
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 score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.021
GPT teacher head0.249
Teacher spread0.227 · 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.

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

Citations16
Published2019
Admission routes2
Has abstractyes

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