Phosphorization-Induced “Fence Effect” on the Active Hydrogen Species Migration Enables Tunable CO<sub>2</sub> Hydrogenation Selectivity
Bibliographic record
Abstract
Incorporating phosphorus (P) into the active metals of a catalyst is an effective strategy to enhance the catalytic performance. However, the mechanisms underlying the influence of the introduced phosphorus species on catalytic performance remain largely unknown. Herein, we observe a pronounced shift in the product selectivity of the CO 2 hydrogenation from CH 4 to CO upon introducing P into the Ru/SiO 2 catalysts. This alteration in product selectivity is attributed to the role of introduced P as a “fence” hindering the migration of active H species. The adsorbed CO, a key intermediate species for CO 2 methanation, is preferentially desorbed before H species cross the “fence” for further hydrogenation, thereby weakening the H 2 -assisted CO activation process and consequently inhibiting CH 4 generation. Our findings provide in-depth insights into the origin of phosphorization-induced modulation of product selectivity in CO 2 hydrogenation. Furthermore, the concept of phosphorization-induced “fence effect” opens a promising avenue for catalyst design in various industrial hydrogenation processes.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".