Decoupling Activity‐Selectivity Trade‐off in Photothermal Catalytic CO <sub>2</sub> Hydrogenation: A Hydrogen Spillover‐Assisted Dual‐Site Synergy Mechanism
Bibliographic record
Abstract
Abstract The persistent trade‐off between catalytic activity and selectivity remains a critical barrier to efficient CO 2 valorization. Herein, we propose a concept of decoupling the activity‐selectivity trade‐off in the photothermal catalytic reverse water‐gas shift (RWGS) reaction by hydrogen spillover‐assisted dual‐site synergy. This concept is demonstrated through a hybrid catalyst constructed by immobilizing abundant Ru single sites and trace Ru clusters onto high‐efficiency photothermal support of N‐doped hierarchical carbon nanocages (hNCNC). Theoretical calculations reveal that the Ru─N 4 sites are highly active and selective for the RWGS reaction, contingent on the efficient migration of dissociated *H species to adjacent C atoms of Ru. Importantly, we experimentally confirm that Ru single sites dominate CO 2 hydrogenation to CO, whereas Ru clusters facilitate H₂ activation and supply hydrogen species to adjacent single sites via spillover over hNCNC. Leveraging this synergistic interaction, the hybrid catalyst achieves an exceptional CO production rate of 3.1 mol·g Ru −1 ·h −1 and selectivity over 98%. This mechanism shows universal applicability as demonstrated by the effective promotion of CO 2 hydrogenation of Ru single sites by other typical hydrogen‐spillover‐active metal clusters, e.g., Pt and Pd clusters. This design concept liberates the potential to overcome the longstanding activity‐selectivity trade‐off in hydrogenation reactions.
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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".