Structure properties of pyrolytic lignin extracted from aged bio-oil
Bibliographic record
Abstract
Fast pyrolysis is a promising technology that can convert biomass into liquid. Bio-oil is one such product, known not only as a greenhouse gas-neutral energy source, but also an opportunity to reduce reliance on fossil fuels. Pyrolytic lignin, a fine homogeneous powder, is the water-insoluble fraction of bio-oil and it contributes to the instability of bio-oil. Additionally, pyrolytic lignin can be used in commercial materials such as adhesives in the wood-based panel industry. This paper presents the structural characterization of pyrolytic lignin extracted from aged bio-oil and the relationship between its properties and the treatment temperature of the aged bio-oil. Pyrolytic lignin samples were characterized by Fourier transform infrared spectroscopy, gel permeation chromatography, differential scanning calorimetry, thermogravimetric analysis and proton nuclear magnetic resonance spectroscopy. The average molecular weight of pyrolytic lignin increased from 700 to 1000 g/mol with increasing aging temperature (6–50°C). Differential scanning calorimetry showed that the glass transition temperature of pyrolytic lignin increases with lower heating rate and higher treatment temperature of bio-oil. An increase in the initial decomposition temperature and the temperature at 95 wt% weight loss of the aged pyrolytic lignin in thermogravimetry were observed for the bio-oil aged at higher temperature. An increase in residue weight of aged pyrolytic lignin was found in bio-oil aged at higher temperatures.
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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 | 0.001 | 0.001 |
| 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".