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Record W4362661677 · doi:10.1139/cjc-2022-0083

Environment polarity effects on the microscopic nonlinear optical properties of some asymmetrical azobenzene molecules

2023· article· en· W4362661677 on OpenAlexvenueno aff
M. Khadem Sadigh, Amir Nasser Shamkhali

Bibliographic record

VenueCanadian Journal of Chemistry · 2023
Typearticle
Languageen
FieldMaterials Science
TopicNonlinear Optical Materials Research
Canadian institutionsnot available
Fundersnot available
KeywordsHyperpolarizabilityChemistryDipoleAzobenzeneChemical physicsPolarity (international relations)SolvatochromismMoleculeChromophoreExcited stateAbsorption (acoustics)SubstituentSolvent polaritySolventNonlinear opticsNonlinear opticalComputational chemistryPhotochemistrySolvent effectsGround stateNonlinear systemOpticsPolarizabilityAtomic physicsOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

Solvent as a complex environment can surround solute molecules and modify their function. In this work, different solute–solvent interaction effects on the nonlinear optical behavior of some azo dyes were studied at the molecular level. In this case, spectroscopic technique and density functional theory were used. According to the results, molecular dipole transition, changes between ground and excited state dipole moments, substituent, and environment polarity play considerable effects on the molecular nonlinear optical responses. In addition to solvent effects on the molecular first- and second-order hyperpolarizability, the two-photon absorption cross-section is also modified due to solvent-induced interactions. Moreover, the determination of the contribution of dominant solvent effects on the molecular nonlinear optical behavior can facilitate the studies on the application of optical samples in designing various optical systems.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.020
GPT teacher head0.237
Teacher spread0.217 · 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 source (direct Gemma or distilled Codex), 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
Published2023
Admission routes1
Has abstractyes

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