Catalytic Performance of Hydroxyapatite-Based Supports: Tailored vs. Commercial Formulations for Dry Reforming of Methane
Bibliographic record
Abstract
Catalyst deactivation, mainly due to coke deposition, presents a significant challenge in the process of dry reforming of methane (DRM). This study focused on coke-resistant catalysts for DRM, particularly nickel-based catalysts supported on hydroxyapatite (HAP). A novel HAP formulation (HAPS) with a Ca/P ratio of 1.54, below the stochiometric ratio studied in previous studies, was compared with commercial HAP (HAPC), and both were impregnated with 10 wt% nickel. The synthesis of HAPS involved low temperature (60 °C), moderate stirring, and a pH of 11, using a custom setup. Dry-reforming reactions were conducted under severe conditions (T = 800 °C) to assess the resistivity of both supports over 120 h. Our findings indicated sustained high conversion rates, reaching 93% for CH4 and 98% for CO2 with HAPS, despite an increase in gas hourly space velocity. Characterisation, including X-ray diffraction, thermogravimetric analysis, and transmission electron microscopy, revealed coke formation using HAPC, leading to initial deactivation, in contrast with the custom support. This discrepancy may be attributed to the distinct physical and chemical properties of the catalysts, their reaction mechanisms, and the deactivation precursors. Overall, the performance of nickel-based catalysts significantly hinges on support–catalyst interactions, in addition to thermal stability.
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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".