MétaCan
Menu
Back to cohort
Record W2180238791 · doi:10.1139/cjp-2013-0250

Solvent polarity studies of highly fluorescent laser dye ADS740WS and its fluorescence quenching with silver nanoparticles

2013· article· en· W2180238791 on OpenAlexvenueno aff
V.B. Tangod, Prasad Raikar, B.M. Mastiholi, U.S. Raikar

Bibliographic record

VenueCanadian Journal of Physics · 2013
Typearticle
Languageen
FieldChemistry
TopicPhotochemistry and Electron Transfer Studies
Canadian institutionsnot available
FundersKarnatak University DharwadUniversity Grants Commission
KeywordsFluorescencePhotochemistryExcited stateQuenching (fluorescence)Dye laserAbsorption (acoustics)Polarity (international relations)SolventLaser-induced fluorescenceSilver nanoparticleFluorescence in the life sciencesDipoleAnalytical Chemistry (journal)NanoparticleChemistryMaterials scienceLaserNanotechnologyPhysicsOpticsAtomic physicsOrganic chemistry

Abstract

fetched live from OpenAlex

Determination of ground state and excited state dipole moments of a highly fluorescent laser dye molecule ADS740WS and comparison of excited state dipole moment by Lippert, Bakhashiev, Kawski–Chamma–Viallet, McRae, Suppan, and solvent polarity methods. Kamlet–Abboud–Taft and Katritzky multilinear analysis for characterizing the solvent contribution into spectral features of the dye has also been studied. Correlation analysis of spectroscopic data with multiple polarity parameters was carried out by multiple linear regression analysis. Optical absorption and fluorescence of ADS740WS in alcohol and other solvents with attachment of silver nanoparticles (AgNPs) shows quenching of absorption and fluorescence intensities. This is due to size, shape, and coupling between the AgNPs and the dye, and energy transfer between the dye and the AgNPs. Fluorescence quenching of ADS740WS leads to many applications notably for advancement in biomolecular labeling and fluorescence patterning and chemotherapy in cancer treatment.

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 score0.007
Threshold uncertainty score0.561

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 head0.017
GPT teacher head0.225
Teacher spread0.208 · 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

Citations6
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of PhysicsSame topicPhotochemistry and Electron Transfer StudiesFrench-language works237,207