New concepts in low‐temperature catalytic hydrogenation and their implications for process intensification
Bibliographic record
Abstract
The study of dynamic catalytic processes occurring at nanoscale provides necessary information to innovate in process intensification (PI). The present study addresses the catalytic performance of Rh, Cu, and Ru‐supported nanoparticles, under mild reaction conditions (< 250 °C, < 500 kPa), for three reactions: (i) the hydrogenation of CO2 to methane, (ii) the hydrogenation of CO2 to methanol, and (iii) the hydrogenation of N2 to ammonia. In all systems, the activity and selectivity are promoted by the presence of large metal nanoparticles, and therefore, a broad distribution of sizes is needed for high activity. The enhanced performance of Rh and Ru nanocatalysts results from catalytic cooperation between small and larger metal nanoparticles. Larger particles provide small ones with the hydrogen needed for an efficient hydrogenation of adsorbed reaction intermediates. Large Cu particles promote methanol formation on Cu/ZnO catalysts, indicating that a similar catalytic cooperation might proceed between large Cu particles and the sites adsorbing reaction intermediates. Some particular crystal planes and defects developed in larger nanoparticles would facilitate the activation and transfer of hydrogen, giving rise to a cooperation mechanism via hydrogen supply. More accurate kinetic models can be developed from a deep understanding of dynamic processes occurring at nanoscale. The new models can be later applied in the design and development of high‐performance units and plants. In addition, a multidisciplinary PI approach including the study of nanoscale catalytic processes would certainly lead to important improvements on the existing process technologies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".