CO<sub>2</sub> Hydrogenation to Methanol in a Slurry Reactor: Catalytic Performance of CuO-Enhanced In<sub>2</sub>O<sub>3</sub>/ZrO<sub>2</sub>
Bibliographic record
Abstract
Converting CO 2 to value-added fuels and chemical commodities is crucial for the chemical industry and the limitation of global warming. Methanol, a high-production volume chemical, can be synthesized via direct CO 2 hydrogenation; however, this conversion faces challenges in thermodynamic limitations and requires stable catalysts with high methanol selectivity and CO 2 conversion. Gas–liquid–solid slurry reactors have the advantage of removing the exothermic heat of CO 2 hydrogenation and potentially improving methanol synthesis. In this work, the catalytic performance of In 2 O 3 and CuO at various loadings supported on ZrO 2 are compared in the slurry reactor for methanol formation through direct CO 2 hydrogenation. The 5 wt % CuO/ZrO 2 catalyst showed a higher conversion of 24% but a lower methanol selectivity of 29% when compared to the 5 wt % In 2 O 3 /ZrO 2 with a conversion of 14% and a selectivity of 42%. CuO was thus added to enhance the In 2 O 3 /ZrO 2 catalyst, which increased the CO 2 conversion to 20% with a methanol selectivity of 38%, thus obtaining a promising methanol activity of 0.17 g MeOH g cat –1 h –1 . Influences of the reaction temperature, reaction pressure, H 2:CO 2 gas ratio, and stirring speed on methanol formation are also reported. The highest methanol activity of 0.28 g MeOH g cat –1 h –1 was reached by the proposed 5 wt % In 2 O 3 -5 wt % CuO/ZrO 2 catalysts at 280 °C and 8.5 MPa.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.006 |
| 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; both teacher heads agree on what is shown here.
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".