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Record W2040798462 · doi:10.1139/cjp-2014-0321

Dynamic behavior of photo-induced orientation of poly(methyl methacrylate) – Solvent Red 23 thin films

2015· article· en· W2040798462 on OpenAlexvenueno aff
B. Abbas, Yousef T. Salman, M.S. EL-Dahr

Bibliographic record

VenueCanadian Journal of Physics · 2015
Typearticle
Languageen
FieldChemistry
TopicPhotochemistry and Electron Transfer Studies
Canadian institutionsnot available
FundersDamascus University
KeywordsDichroic glassMethyl methacrylatePerpendicularAnisotropyOpticsRelaxation (psychology)Thin filmSolventDichroismIsotropyPhysicsMaterials scienceAnalytical Chemistry (journal)Nuclear magnetic resonanceChemistryPolymerOrganic chemistryNanotechnologyMonomer

Abstract

fetched live from OpenAlex

We investigate the effect of the photo-orientation and stability of the Solvent Red 23 dye incorporated in poly(methyl methacrylate) thin films as a guest–host system. A probe–pump optical system was used to investigate the dichroic behavior of the samples. Orthogonally polarized laser lights induce a smaller increase in the transmitted beam intensity. A stronger polar order was induced using a parallel probe–pump laser light compared to that of the perpendicular state. It was found that the dichroic ratio value of the poly(methyl methacrylate) – Solvent Red 23 thin film increased by 0.174 at the photostationary state, indicating that the net orientation of Solvent Red 23 molecules has increased 17.4% in the direction perpendicular to the applied beam field. The dynamic evolution of the dichroic ratio showed that the relaxation is rather slow, and it deteriorates in a slow exponential fashion, which may take a very long time to reach the isotropic state. The contrast ratios were 0.449 and 0.267 in parallel and orthogonal probe–pump configurations, respectively. The order parameter calculations reveal that about 70% of photo-induced anisotropy remains after relaxation for a long time.

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 score0.002
Threshold uncertainty score0.620

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 head0.024
GPT teacher head0.266
Teacher spread0.242 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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