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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".