<i>tert</i> ‐butanol and hydrogen peroxide react over Amberlyst‐15 to form <i>tert</i> ‐butyl hydroperoxide
Bibliographic record
Abstract
Abstract Organic peroxides are explosive compounds that are applied as disinfectants, bleaching agents, and as initiators for polymer synthesis because of their high reactivity. Traditional homogeneous processes with H 2 SO 4 catalyst produce salts, either in the neturalization step after reaction or due to the foramtion of tert ‐butyl hydrogen sulphate, that must be disposed of, which introduces cost and represents an environmental burden. Here, we devised a flow chemistry approach to oxidize tert ‐butyl alcohol (TBA) to tert ‐butyl hydroperoxide (TBHP) over various heterogeneous catalysts. Under acidic conditions, TBHP is the main product and di‐ tert ‐butyl peroxide (DTBP) and peroxy‐ketal are by‐products. The most active catalyst was Amberlyst‐15, while yield of Nafion, activated carbon, and heteropoly acids (HPA) on carbon and silica matrices was less than 1% at 70°C. In in the range of 30 to 50°C, a first order kinetic expression characterizes the tert ‐butyl alcohol conversion well (). The reaction rate is slow and the rate constant, , was 0.003 min −1 . Above 50°C, by‐products reacted further to acetone, methane, ethane, and other compounds.
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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".