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Record W2328287444 · doi:10.1021/la504518c

In Situ Synthesis and Characterization of Silver/Polymer Nanocomposites by Thermal Cationic Polymerization Processes at Room Temperature: Initiating Systems Based on Organosilanes and Starch Nanocrystals

2015· article· en· W2328287444 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueLangmuir · 2015
Typearticle
Languageen
FieldChemistry
TopicPhotopolymerization techniques and applications
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsNanocompositeMaterials sciencePolymerizationCationic polymerizationPolymerX-ray photoelectron spectroscopyChemical engineeringNanoparticlePolymer chemistrySilanesThermogravimetric analysisPolymer nanocompositeNanotechnologyComposite materialSilane

Abstract

fetched live from OpenAlex

New methods for the preparation of silver nanoparticles/polymer nanocomposite materials by thermal cationic polymerization of ε-caprolactone (ε-CL) or α-pinene oxide (α-PO) at room temperature (RT) and under air were developed. The new initiating systems were based on silanes (Si), starch nanocrystals (StN) and metal salts. Excellent polymerization profiles were revealed. It was shown that silver nanoparticles (Ag(0) NPs) were in situ formed and that the addition of StN improves the polymerization efficiency. The as-synthesized nanocomposite materials contained spherical nanoparticles homogeneously dispersed in the polymer matrices. Polymers and nanoparticles were characterized by gel permeation chromatography (GPC), X-ray diffraction (XRD), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), and UV-vis spectroscopy. A coherent picture of the involved chemical mechanisms is presented.

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.

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 score0.007
Threshold uncertainty score0.585

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 head0.009
GPT teacher head0.224
Teacher spread0.215 · 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