Catalytic Upgrading of Low Cost Carbon Resources Under Methane Environment
Bibliographic record
Abstract
The heavy reliance on fossil fuels raises concerns about economic stability and environmental impact, making it desirable to find a renewable energy source that is cost competitive with traditional fuels. Catalytic conversion of low cost carbon resources such as biomass into biofuels and valuable chemicals has the potential of alleviating the dependence on fossil fuels. However, due to high oxygen content of biomass, the upgraded products are highly oxygenated and disadvantageous. The presence of oxygenated compounds, such as alcohols, carboxylic acids, phenolics, and furans, results in the products with low energy density, poor quality as well as incompatibility infrastructure. Hydrodeoxygenation is an efficient method to improve the quality of biomass-derived products by oxygen elimination. However, it requires high operating pressure and substantial consumption of expensive and unavailable hydrogen, which makes this process unpractical and economically unfeasible in a large scale. Methane, as the main component in natural gas that is an abundant natural resource present in Canada, is an ideal alternative to hydrogen for the valorization of bio-derived products. Methane (CH4) with the highest H/Ceff ratio could benefit the formation of hydrocarbon products with higher energy density and reduce the coke formation. The activation of methane can provide hydrogen atoms for the deoxygenation of oxygenated chemical compounds, and methyl moieties to form the aromatic hydrocarbons, thus improve the quality and yield of liquid products. Due to the complexity of biomass, several model compounds including ethanol, acetic acid, phenol, furfural, cellulose and lignin representing different functional groups, are selected to investigate the technical feasibility of catalytic co-conversion of bio-based compounds and methane to valuable aromatic hydrocarbons. Mechanistic investigations such as liquid and solid-state 1H, 2H and 13C NMR combined with experimental analyses evidence methane incorporation into aromatic products. Various catalyst characterizations including XRD, TEM, DRIFT, NH3-TPD, XPS, and XAS, are employed to reveal the relationship between the physicochemical properties of the catalyst and its excellent performance. The mechanistic understanding provides valuable insights into the catalytic chemistry of biomass valorization with methane, and the rational design of catalysts for cost-efficient utilizations of biomass and natural gas resources.
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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) | 0.001 | 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".