The nitro to amine reduction: from millions of tons to single molecule studies
Bibliographic record
Abstract
Abstract Palladium nanostructures are interesting heterogeneous catalysts because of their high catalytic activity in a vast range of highly relevant reactions such as cross couplings, dehalogenations, and nitro-to-amine reductions. In the latter case, the catalyst Pd@GW (palladium on glass wool) shows exceptional performance and durability in reducing nitrobenzene to aniline under ambient conditions in aqueous solutions. To enhance our understanding, we use a combination of optical and electron microscopy, in-flow single molecule fluorescence, and bench chemistry combined with a fluorogenic system to develop an intimate understanding of Pd@GW in nitro-to-amine reductions. We fully characterize our catalyst in situ using advanced microscopy techniques, providing deep insights into its catalytic performance. We also explore Pd cluster migration on the surface of the support under flow conditions, providing insights into the mechanism of catalysis. We show that even under flow, Pd migration from anchoring sites seems to be minimal over 4 h, with the catalyst stability assisted by APTES anchoring.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".