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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".