Catalytic effect of alloxazinium and isoalloxazinium salts on oxidation of sulfides with hydrogen peroxide in micellar media
Bibliographic record
Abstract
Three novel amphiphilic alloxazinium salts were prepared: 3-dodecyl-5-ethyl-7,8,10-trimethylisoalloxazinium perchlorate (1c), 1-dodecyl-5-ethyl-3-methylalloxazinium perchlorate (2b), and 3-dodecyl-5-ethyl-1-methylalloxazinium perchlorate (2c). Their catalytic activity in thioanisole (3) oxidation with hydrogen peroxide was investigated in micelles of sodium dodecylsulfate (SDS), hexadecyltrimethylammonium nitrate (CTANO3) and Brij 35. Reaction rates were strongly dependent on the catalyst structure, on the type of micelles, and on pH value. Alloxazinium salts 2 were more effective catalysts than isoalloxazinium salts 1. Due to the contribution of micellar catalysis, the vcat/v0 ratio of the catalyzed and non-catalyzed reaction rates was almost 80 with salt 2b solubilized in CTANO3 micelles. Nevertheless, the highest acceleration was observed with non-amphiphilic 5-ethyl-1,3-dimethylalloxazinium perchlorate (2a) in CTANO3 micelles (vcat/v0 = 134). In this case, salt 2a presumably acts as a phase-transfer catalyst bringing hydrogen peroxide from the aqueous phase into the micelle interior. Synthetic applicability of the investigated catalytic systems was verified on semi-preparative scale.
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 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) | 0.000 | 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".