Cost-Effective Single-Step Synthesis of Metal Oxide-Supported Ni Catalyst for H2-Production Through Dry Reforming of Methane
Bibliographic record
Abstract
Abstract Preparing catalysts from cheap metal precursors in a single pot are an appealing method for reducing catalytic preparation costs, minimizing chemical waste, and saving time. With regards to the catalytic conversion of dry reforming of methane, it offers the prospect of significantly reducing the cost of H2 production. Herein, NiO-stabilized metal oxides like Ni/TiO2, Ni/MgO, Ni/ZrO2, and Ni/Al2O3 are prepared at two different calcination temperatures (600 °C and 800 °C). Catalysts are characterized by X-ray diffraction, Raman spectroscopy, surface area-porosity analysis, Temperature program experiments, infrared spectroscopy, and thermogravimetry analysis. The MgO-supported Ni catalyst (Ni/MgO-600), ZrO2-supported Ni catalyst (Ni/ZrO2-600), and Al2O3-supported Ni (Ni/Al2O3-600) catalyst calcined at 600 °C show initial equal H2 yields (~ 55%). The population of CH4 decomposition sites over ZrO2-supported Ni catalyst remains highest, but H2-yield drops to 45% against high coke deposition. The catalytic activity remains constant over the Ni/MgO-600 catalyst due to the enrichment of “surface interacted CO2-species”. MgO-supported Ni catalyst calcined at 800 °C undergoes weak interactions of NiO-M′ (M′ = support), serious loss of CH4 decomposition sites and potential consumption of H2 by reverse water gas shift reaction, resulting in inferior H2 yield. H2-yield remains unaffected over an Al2O3-supported Ni catalyst even against the highest coke deposition due to the formation of stable Ni (which exsolves from NiAl2O4) and proper matching between carbon formation and rate of carbon diffusion.
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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".