Non-thermal plasma-enhanced reverse water-gas shift reaction over hydroxyapatite-supported Ni catalyst: Effect of severe process conditions
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Bibliographic record
Abstract
CO 2 hydrogenation to carbon monoxide and water (Reverse Water Gas Shift reaction) is a promising way to valorize CO 2. It is the preliminary step in the methanol and Fischer-Tropsch synthesis processes. However, the thermodynamic barrier has limited the industrial scale-up of this process, and thus, the need for a proper catalyst formulation and reactor configuration is still ongoing. In this paper, we studied the catalytic performance of a hydroxyapatite-supported nickel-zirconia catalyst in a microwave plasma reactor for the CO 2 hydrogenation to carbon monoxide. A 10 wt% Ni- ZrO 2 /HAp catalyst was prepared by the wetness impregnation method, dried at 180 ° C for 18 h and calcined at 500 ° C for 3 h. The influence of the H 2 /CO 2 molar ratio, power, and GHSV on CO 2 conversion and CO selectivity was studied. In the selected range of GHSV, it did not influence the output parameters. Moreover, CO selectivity remained in the range of 98–100 % in all experiments. The highest carbon yield was 83 % under H 2 /CO 2 to 2:1, Power= 2.25 kW, and GHSV= 80,000 mL.gr −1 .hr −1 while maintaining 9 % energy efficiency. The high CO 2 conversion is justified due to the interaction of a basic catalyst with the CO 2 (weak acid) which facilitated the CO 2 reduction to CO as well as the microwave discharge reactor, whose temperature characteristics meet the requirement of the RWGS reaction. In addition, an energy efficiency equal to 28 % was observed under H 2 /CO 2 to 1:1, Power= 0.8 kW, and GHSV= 120,000 mL.gr −1 .hr −1 . To the best of our knowledge, these values have never been attained before. In this study, we proposed a novel catalyst formulation for the CO 2 hydrogenation reaction, tested a microwave discharge for the reaction, and operated at very high GHSV levels, which are suitable for industrial production.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it