Catalytic hydrothermal liquefaction of wheat straw and chemical profiling of bio-crude oil
Bibliographic record
Abstract
In this study, a central composite design model was generated under response surface methodologies for the hydrothermal liquefaction of the wheat straw by considering the different process variables such as temperature (270–330 °C), reaction time (20–60 min) and feed concentration (5–15 wt%) to optimize the bio-crude oil yield and generate an experimental model. Moreover, at the optimized conditions, different catalysts such as K 2 CO 3 , Fe, ZrO 2 , Fe-K 2 CO 3 and ZrO 2 -K 2 CO 3 were employed for the hydrothermal liquefaction of wheat straw to maximize the bio-crude oil yield and decrease the oxygen content. Among all catalysts, Fe-K 2 CO 3 delivered the highest bio-crude oil yield of 29 wt% with the lowest oxygen content of 14 wt%. The chromatographic analysis established molecular profiling of bio-crude oil samples, which revealed the presence of different phenolic compounds like phenol, 2-methoxy phenol and catechol including other components such as aldehydes, cyclic ketones, octene, hexadecane and octadecane. Spectroscopic and compositional profiling of bio-crude oil revealed different organic functional groups and aromatic compounds essential for the generation of molecular-level reaction mechanisms and subsequent upgrading of bio-crude oil for transportation fuel and various chemicals. • Hydrothermal liquefaction of wheat straw with and without catalysts was performed. • Optimized HTL temperature, reaction time and feed concentration were 299.8 °C, 39.9 min and 7.7 wt.%, respectively. • Fe-K 2 CO 3 led to the highest bio-crude oil yield (29 wt.%) and lowest oxygen content (14 wt.%). • Catalysts reduced the formation of phenolic and oxygenated components in bio-crude oil. • A variety of value-added biochemical precursors were obtained in bio-crude oil.
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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".