MétaCan
Menu
Back to cohort
Record W2034103813 · doi:10.1039/c3cp55079f

pH and thermo-responsive tetronic micelles for the synthesis of gold nanoparticles: effect of physiochemical aspects of tetronics

2014· article· en· W2034103813 on OpenAlexaff
Vijender Singh, Poonam Khullar, Pragnesh N. Dave, Aman Kaura, Mandeep Singh Bakshi, Gurinder Kaur

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2014
Typearticle
Languageen
FieldMaterials Science
TopicGold and Silver Nanoparticles Synthesis and Applications
Canadian institutionsCollege of the North AtlanticWilfrid Laurier University
Fundersnot available
KeywordsMicelleNanoreactorNanoparticleChemistryChemical engineeringPolymerMacromoleculeColloidal goldNanotechnologyAqueous solutionMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Micelles of the star shaped block polymers "tetronics" were employed for the synthesis of gold (Au) nanoparticles (NPs) under the effect of pH and temperature variation. The presence of the diamine core in the tetronic macromolecule made its micelles highly pH responsive, thereby dramatically altering the physiochemical properties. Likewise, a high degree of hydration made the micelles temperature sensitive. UV-visible studies, transmission electron microscopy (TEM), gel electrophoresis, and structure optimization by energy minimization were applied to understand the physiochemical aspects of tetronic micelles and their further role in the synthesis of Au NPs. Synthesis of Au NPs was triggered by the surface cavities of the micelles and hence the NPs simultaneously adsorbed on the micelle surface. Low pH induced high hydration and temperature responsive well defined vesicular morphologies bearing Au NPs, while high pH produced mainly large and compact compound micelles carrying NPs. Both pH and temperature responsive behaviors of different tetronics significantly influenced the synthesis of Au NPs and thus demonstrated their ability to act as nanoreactors for the materials synthesis under different experimental conditions.

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 score0.004
Threshold uncertainty score0.601

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.007
GPT teacher head0.228
Teacher spread0.222 · 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

Citations42
Published2014
Admission routes1
Has abstractyes

Explore more

Same venuePhysical Chemistry Chemical PhysicsSame topicGold and Silver Nanoparticles Synthesis and ApplicationsFrench-language works237,207