Molecular Origins of Dye Aggregation and Complex Formation Effects in Coumarin 343
Bibliographic record
Abstract
Solvent effects have a significant impact upon the optoelectronic properties of coumarin 343, which are mainly exploited in the solution phase for a diverse range of applications, including lasers and dye-sensitized solar cells. The influence of solvation on the structure–property relationships for coumarin 343 are analyzed herein using (time-dependent) density functional theory (DFT/TD-DFT) based quantum calculations, in conjunction with UV–vis absorption and 1 H NMR spectroscopic measurements. Specifically, these solvent effects on the peak UV–vis absorption wavelengths (λ max abs ), and molecular geometry changes of coumarin 343 are investigated. Coumarin 343 is shown to be prone to dye aggregation; its different molecular aggregation phases, UV–vis absorption profiles, and formation mechanisms are discussed using three representative solvents: cyclohexane [nonpolar and non-hydrogen bonding (NHB)], benzene (polar and nearly NHB), and ethanol (polar and hydrogen-bond accepting/donating). It is also demonstrated that the intramolecular hydrogen bond in coumarin 343 can be “opened up” in hydrogen-bond accepting solvents. Furthermore, this study elucidates the solvent effects on the molecular conformations of coumarin 343. The much deeper understanding of the molecular aggregation and complex formation mechanisms in coumarin 343 provides key building blocks for the knowledge-based molecular design of coumarin dyes with antiaggregation characteristics. The associated molecular engineering prospects for these dyes stand to improve device performance for optoelectronic applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".