Solvation environment effects on the photoisomerization equilibrium of the model tannins catechin and epicatechin as natural sunscreens in aquatic systems
Bibliographic record
Abstract
The photoisomerization equilibrium between the model tannins (-)-catechin and (-)-epicatechin in aqueous solution was investigated at the density functional level of theory to gain insights into the action of these compounds as natural sunscreens in aquatic systems. Increasing water temperature, as might be expected on seasonal and diurnal bases, is predicted to shift the equilibrium further in favor of catechin. The isomerization energy between catechin and epicatechin was also considered in a range of polar protic, polar aprotic, apolar protic, and apolar aprotic solvents using the solvation model based on density (SMD) and integral equation formalism polarizable continuum model (IEFPCM). The IEFPCM yielded a modest range in isomerization energies depending on solvent polarity or proticity, whereas a substantial variation was observed with the SMD. The SMD results suggest that the solvation environment around catechin and epicatechin will play a major role on the photoisomerization equilibrium between these two compounds. As the freely dissolved monomer in aquatic systems, the catechin–epicatechin photoisomerization equilibrium will be in the range of 11:1 to 14:1. In the less polar environments of associations with dissolved organic matter or within a larger tannin structural framework, the theoretical modeling efforts indicate that the catechin–epicatechin photoisomerization equilibrium could be as low as 3:1.
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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".