Non-thermal plasma-enhanced reverse water-gas shift reaction over hydroxyapatite-supported Ni catalyst: Effect of severe process conditions
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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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) | 0.000 | 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 | 0.000 |
| 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".