Solvolytic Liquefaction of Bark: Understanding the Role of Polyhydric Alcohols and Organic Solvents on Polyol Characteristics
Bibliographic record
Abstract
Bark was liquefied in polyhydric alcohols of various functionality, equivalent weight, and hydroxyl type, and organic solvents of varying polarity to determine how these features impact liquefaction behavior and polyol characteristics. It was found that the liquefaction yield was highly tunable with the use of polyhydric alcohols with primary hydroxyl groups, with low equivalent weight alcohols providing the highest liquefaction yield (59.3%). This showed that the highly polar hydroxyls (primary) and short chains created a highly protic solvent that improved conversion and protected the biopolymers from degradation. This was corroborated by 1 H NMR analysis that indicated a greater amount of sugar degradation products were observed when polyhydric alcohols with secondary hydroxyl groups were used. Regarding organic solvents, ketonic solvents showed the greatest increase in the liquefaction yield. The composition and carbon content analysis of the residues suggested that the highly polar carbonyl group of ketonic solvents like acetyl acetone and cyclohexanone may have hindered condensation side reactions. These results have shown that selection of polyhydric alcohols and organic cosolvents can be quite impactful on the liquefaction yield and the polyol characteristics.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".