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Solvent Vapour Induced Film Formation of PS/AgNPs Composites Using Spectroscopic Techniques

2018· article· en· W2900234356 on OpenAlexvenueno aff
Can Akaoğlu, Şaziye Uğur

Bibliographic record

VenueJournal of Coating Science and Technology · 2018
Typearticle
Languageen
FieldEnergy
TopicTiO2 Photocatalysis and Solar Cells
Canadian institutionsnot available
Fundersnot available
KeywordsComposite materialMaterials scienceSolventChemical engineeringChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

In this work, pyrene labeled polystyrene (PS) latex dispersions and silver nanoparticles (AgNPs) were mixed at different weight ratios of AgNPS in the range of 0-50 wt%. Powder (PS/AgNPs) films were prepared on glass substrates from these mixtures by the drop-casting method at room temperature. The fast transient fluorescence (FTRF) technique was used to monitor the film formation process of PS/AgNPs composites by measuring the fluorescence lifetimes of pyrene during vapour exposure. It was observed that pyrene lifetimes decreased as vapour exposure time, t increased obeying Stern–Volmer kinetic. The optical transmission (UVV) experiments showed that the transparency of the films decreased with increasing AgNPs content. Transparent films were produced for the composites with 0 to 20 wt % of AgNPs content. The Prager–Tirrel model was employed to FTRF and UVV data to obtain back-and-forth frequencies, ν, of the reptating PS chains during vapour induced film formation process and polymer interdiffusion was found to obey a t1/2 law. Although the conductivity increased by 3 orders of magnitude with increasing AgNPs content, the composites behaved like an insulator. The morphology of composite films also progressively changed during vapour exposure and affected by AgNPs content.

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 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 score0.011
Threshold uncertainty score0.244

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.020
GPT teacher head0.288
Teacher spread0.268 · 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
Published2018
Admission routes1
Has abstractyes

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