Rhodium-doped iron oxides promoted by sodium for highly selective hydrogenation of CO2 to ethanol and C2+ hydrocarbons
Bibliographic record
Abstract
Hydrogenation of CO2 to produce high-value C2 + products has gained great interest, but catalyst design for highly selective ethanol still remains a challenge. Herein, bifunctional catalysts of iron oxide promoted with rhodium and sodium, Na-Rh-FeOx and Rh-FeOx-Na, were prepared by using simple methods of incipient wetness impregnation (IWI) and coprecipitation. The former catalyst prepared via IWI, Na-Rh-FeOx, is remarkably efficient for the hydrogenation of CO2 to ethanol with high selectivity of 91 % at 200 °C in the liquid fraction. The latter catalyst synthesized solely through the coprecipitation in a sodium-containing medium, Rh-FeOx-Na, displayed the synergy effect to perform a better CO2 conversion of 23 % at 200 °C. Characterization of catalysts via XRD, XPS, N2 physisorption, CO2-TPD, and gas pulse-chemisorption reveals that different preparation methods influenced the dispersion of sodium particles. This property, in turn, impacts the ratio of hydrogen-activating sites to CO-adsorption sites, directly related to the balance between dissociative- and non-dissociative CO* intermediates, which is crucial for ethanol formation. Adding alkali metal facilitates the formation of iron carbide Fe5C2, known to be active for C–C propagation. Integrating Na-Rh-FeOx with ZSM-5 support further increased ethanol selectivity up to 97 % in liquid phase and 90 % in gas phase. By selecting a proper route for introducing alkali metals and combining with supports, the catalyst properties can be effectively tailored, thereby regulating the catalytic activity and selectivity.
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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 | 0.001 |
| 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".