Conformational and Tautomeric Effects on Sulfonated Azo Dyes: A Prelude to Photodegradation
Bibliographic record
Abstract
In this work, we present a systematic investigation of the ground- and excited states of six sulfonated azo dyes with broad applications in the textile industry: Allura Red, Amaranth, Carmoisine, Ponceau, Red 2G, and Sunset Yellow. Ground-state properties were evaluated using density functional theory (DFT) at the ωB97X-D4/ma-def2-SVP level of theory. To identify the most suitable functional for excited-state calculations and to assess vertical excitation energies to low-lying excited states, we tested a series of hybrid, meta-hybrid, and range-separated hybrid functionals from Jacob’s ladder using the Tamm-Dancoff approximation (TDA) and the ma-def2-SVP basis set and compared to results from RI-CC2 and STEOM-DLPNO-CCSD approaches. Excited-state properties were then further examined using TDA at the CAM-B3LYP//ωB97X-D4/ma-def2-SVP level of theory. Additionally, the quantum theory of atoms in molecules (QTAIM) was applied to explore density-related properties in both ground and excited states. We found, perhaps not surprisingly, that excitation weakens the N−−N bond in both the enol and keto forms of the dyes, but with variations depending on the molecule, conformer, and electronic state. This bond weakening, as reflected by changes in electron density descriptors, suggests a possible route for photodegradation in aquatic environments.
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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".