In-Depth Insight into the Chemical Composition of Bio-oil from Hydroliquefaction of Lignocellulosic Biomass in Supercritical Ethanol with a Dispersed Ni-Based Catalyst
Bibliographic record
Abstract
A comprehensive compositional analysis was conducted on biofuel obtained from woody biomass hydroliquefaction in supercritical ethanol with a dispersed Ni-based catalyst. Gas chromatography–mass spectrometry (GC–MS) and 1 H nuclear magnetic resonance (NMR) were used to analyze the bio-oil compositions, and the results indicated the presence of carboxylic acid, ethyl ester, aldehyde, ketone, phenol, and its derivatives. As a result of the inherent limitations of these techniques, an intensive compositional characterization of bio-oil was accomplished through Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS). The results revealed that the dominant oxygen-containing compounds were O 2 –O 13 with double bond equivalent (DBE) values of 1–20 and carbon numbers of 10–25. The minor N 1 O x class species with 4–15 carbon numbers and 10–35 DBE were also detected. The use of FT-ICR MS provided an in-depth compositional analysis of liquefaction-derived oil and would improve the understanding of biocrude for further process upgrading.
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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) | 0.001 | 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".