Reduction and structural modification of MoOx/γ-Al2O3 catalysts through acetic acid treatment for green diesel production from corn distiller’s oil
Bibliographic record
Abstract
• Acetic acid modification enhances MoO x catalyst’s stability and activity for deoxygenation. • Deoxygenation activity is closely related to catalyst’s metal oxidation states and surface acidity. • Liquid hydrocarbons contain 48% green diesel, 24% bio-jet fuel and 24% lighter gasoline. • Nearly complete deoxygenation of non-edible CDO into bio-hydrocarbons was achieved. • No graphitic coke was found on spent catalyst’s surface. Enabling new environmentally-friendly resources for fuels and platform chemicals is crucial to fulfill our future energy demands while enhancing circularity. Corn distiller’s Oil (CDO) − as a coproduct of the ethanol industry, serves as a sustainable supply of carbon that may be converted into energy, fuels and specialty chemicals. A current challenge in using renewable oxygenated feedstocks is the high amount of coking and catalyst fouling that occurs. This study examined the hydrothermal deoxygenation of CDO into green diesel using molybdenum oxide (MoO x ) catalysts in a continuous process without using any external hydrogen. The possibility of reduction and developing oxygen-deficient surfaces on molybdenum oxide (MoO x ) catalysts through acetic acid treatment or adding ceria (CeO 2 ) was investigated in order to enhance the acidity of catalysts as well as catalytic activity and stability required for the production of green diesel from CDO. Results showed that the acidity of these catalysts as measured by NH 3 temperature programmed desorption (NH 3 -TPD) had a strong correlation between the degree of deoxygenation and catalyst stability. Acetic acid treatment of the 7.5 wt% MoO 3 /γ-Al 2 O 3 catalyst reduced MoO 3 to MoO 3-x , increased the Mo 5+ species from 8.7 % to 22 %, created Mo 4+ species and increased the catalyst surface acidity in the range of low to moderate strength by approximately 17 %. The use of acetic acid-treated molybdenum oxide (Hac-7.5 wt% MoO 3 /γ-Al 2 O 3 ) catalyst enabled nearly complete (99.9 %) deoxygenation of CDO and heavy hydrocarbons cracking to produce diesel-like fuels. A moderate increase in catalyst surface acidity by acetic acid treatment also increased the selectivity of diesel-like fuel through hydrocracking of long-chain hydrocarbons. This low-cost modification step of catalyst improvement is promising for industrial application. However, cracking of hydrocarbons generated amorphous coke (no graphitic coke was observed) on the catalyst surface, which required periodic removal by controlled calcination of the catalyst in presence of air for further use.
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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".