Molecular insights into pyrolytic lignin solubility and Bio-Oil phase stability in representative organic solvents for sustainable Bio-Energy Applications
Bibliographic record
Abstract
Phase separation in fast-pyrolysis bio-oil (FPBO) arises partly from the limited solubility of pyrolytic lignin in the organic-solvent (OS) fraction and in water. Using molecular dynamics (MD), we examine lignin solubility in five representative OS, acetic acid (AA), phenol (PH), methanol (ME), hydroxyacetone (HA), furfural (FU), and their binary mixtures. In non-aqueous systems, lignin solubility follows ME > AA ≈ PH > HA > FU, reflecting ME's rapid diffusion and strong hydrogen bonding (H-bonding) capability. Under aqueous competition, the order becomes PH > AA ≈ ME > HA > FU; PH maintains π-π interaction with lignin, while AA preserves favorable polarity matching. Binary mixtures enriched in PH, AA or ME possess good lignin solubility, whereas FU/HA blends underperform, especially with water. Overall, lignin solubility is heavily influenced by polarity, H-bonding, and π-π interaction. These molecular insights provide mechanistic guidelines for designing bio-oil formulations that minimize phase separation.
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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 | 0.001 |
| 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) | 0.000 | 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".