Photobleaching of Erythrosine B in Aqueous Environment Investigation Beyond pH<sup>†</sup>
Bibliographic record
Abstract
In the scientific literature, the term aqueous environment is loosely employed as it encompasses a broad range of different buffering agents. While there is an increasing number of experimental evidence that point toward specific buffer effects extending far beyond pH, the impact of the chemical nature of the buffering ions is often disregarded, especially in photochemical studies. Herein, we highlighted the importance of buffer specific effects on both the photobleaching and the singlet oxygen quantum yields of a dye in aqueous environments. For this study, we chose erythrosine B (EB) as our model photosensitizer as its photochemistry and photobleaching are well documented in the literature. We followed EB's photobleaching via absorption spectroscopy in four different aqueous solvents, including pure water, phosphate, Tris and HEPES buffer. These buffer systems were selected because they are commonly used in biochemical and biological applications. Our results show that specific buffer effects cannot be neglected. Indeed, the singlet oxygen quantum yield for EB is significantly different in HEPES compared to the other solvents. Furthermore, we showed that EB's photoproduct is highly dependent on the nature of the chemical buffer being used.
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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".