Catalytic Supercritical Water Gasification of Canola Straw with Promoted and Supported Nickel-Based Catalysts
Bibliographic record
Abstract
Lignocellulosic biomass such as canola straw is produced as low-value residue from the canola processing industry. Its high cellulose and hemicellulose content makes it a suitable candidate for the production of hydrogen via supercritical water gasification. However, supercritical water gasification of lignocellulosic biomass such as canola straw suffers from low hydrogen yield, hydrogen selectivity, and conversion efficiencies. Cost-effective and sustainable catalysts with high catalytic activity for supercritical water gasification are increasingly becoming a focal point of interest. In this research study, novel wet-impregnated nickel-based catalysts supported on carbon-negative hydrochar obtained from hydrothermal liquefaction (HTL-HC) and hydrothermal carbonization (HTC-HC) of canola straw, along with other nickel-supported catalysts such as Ni/Al2O3, Ni/ZrO2, Ni/CNT, and Ni/AC, were synthesized for gasification of canola straw on previously optimized reaction conditions of 500 °C, 60 min, 10 wt%, and 23–25 MPa. The order of hydrogen yield for the six supports was (10.5 mmol/g) Ni/ZrO2 > (9.9 mmol/g) Ni/Al2O3 > (9.1 mmol/g) Ni/HTL-HC > (8.8 mmol/g) Ni/HTC-HC > (7.7 mmol/g) Ni/AC > (6.8 mmol/g) Ni/CNT, compared to 8.1 mmol/g for the non-catalytic run. The most suitable Ni/ZrO2 catalyst was further modified using promotors such as K, Zn, and Ce, and the performance of the promoted Ni/ZrO2 catalysts was evaluated. Ni-Ce/ZrO2 showed the highest hydrogen yield of 12.9 mmol/g, followed by 12.0 mmol/g for Ni-Zn/ZrO2 and 11.6 mmol/g for Ni-K/ZrO2. The most suitable Ni-Ce/ZrO2 catalysts also demonstrated high stability over their repeated use. The superior performance of the Ni-Ce/ZrO2 was due to its high nickel dispersion, resilience to sintering, high thermal stability, and oxygen storage capabilities to minimize coke deposition.
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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 | 0.000 |
| 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 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".