Catalytic Upgrading of Oleic Acid to Aromatic Hydrocarbons by Tandem Deoxygenation and Aromatization over MoO<sub>3</sub>/HZSM-5
Bibliographic record
Abstract
The global petrochemical sector confronts escalating regulatory and environmental pressures for the production of platform chemicals such as benzene, toluene, ethylbenzene, and xylenes (BTEX), thereby necessitating a transition from petroleum-derived feedstocks to sustainable, biobased feedstocks. Unlike traditional hydrocarbon feedstocks, these biobased alternatives predominantly comprise of oxygenated compounds, which undergo multistep chemical transformations to produce BTEX. Such processes typically require hydrotreating conditions and rely on expensive noble metal catalysts, posing significant economic and operational concerns. Herein, we demonstrate a novel one-pot catalytic approach for direct conversion of oleic acid (OA) into aromatics under subcritical hydrothermal conditions. Oleic acid was strategically selected as the model compound due to its abundance in nonedible oils and thermodynamic stability under our operating temperatures. Using a MoO 3 /HZSM-5 catalyst without additional H 2 requirement, tandem deoxygenation–aromatization of OA resulted in a total hydrocarbon yield of ca. 77% (net) or 90% theoretical yield versus decarboxylated intermediates. The liquid products obtained were characterized by infrared (IR) and 1 H NMR spectroscopy as well as GC-FID analysis. Multivariate design of experiments with parametric evaluation of temperature (350 °C–375 °C), reaction time (1–4 h), and catalyst loading (0–10%) resulted in 65% BTEX selectivity and complete deoxygenation of OA. This efficiency surpasses conventional approaches and leverages an efficient catalyst for tandem deoxygenation-aromatization chemistry, thereby providing a scalable route to decarbonize aromatic production while valorizing lipid waste streams─a critical advancement towards circular chemical manufacturing.
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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) | 0.000 | 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 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".