Hydrogen Spillover Is Regulating Minority Rh<sub>1</sub> Active Sites on TiO<sub>2</sub> in Room-Temperature Ethylene Hydrogenation
Bibliographic record
Abstract
The complicated dynamics of active sites on single-atom catalysts under reducing conditions limits their applications in hydrogenation reactions and mechanistic understanding. Herein, we report that on Rh 1 /TiO 2, *H spillover during room-temperature ethylene hydrogenation hydroxylates and reduces TiO 2, enhancing the intrinsic activity of Rh 1 by 9-fold. Spectroscopic and kinetic evidence suggests that the spillover of *H is suppressed by their facile reaction with C 2 H 4, most of the spilled *H are nonreactive spectators, and >99% turnovers occur on a small subset (<20%) of exposed “active Rh 1 ”. Steady-state kinetics indicates competitive adsorption between H and C 2 H 4, H 2 dissociation is the rate-determining step, and the apparent activation barrier ( E a,app ) of the reaction is ∼48 kJ/mol. The evolution of Rh 1 under H 2 was further tracked by spectroscopic and microscopic techniques at elevated temperatures. At 200 °C, more Rh 1 are exposed, but these Rh 1 are at least 5-fold less active than that of the “active Rh 1 ”. At 300 °C, Rh clusters derived from Rh 1 become the main active sites, shifting E a,app to 62 kJ/mol, characteristic of Rh nanoparticles. At ≥400 °C, larger and more active Rh particles in the strong metal–support interaction state are created. This work revealed the unexpected regulation effects of *H spillover on M 1 active sites under ambient conditions, differentiated the minority active M 1 sites, and demonstrated how the stability of M 1 under reducing atmospheres affects hydrogenation catalysis.
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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".