Producing Bark-based Polyols through Liquefaction: Effect of Liquefaction Temperature
Bibliographic record
Abstract
Bark-based polyols were synthesized through a solvent liquefaction in a polyethylene glycol (PEG)/glycerol cosolvent. Liquefaction reactions were carried out at temperatures of 90, 130, and 160 °C. The bark-based polyols were analyzed for their yield, composition, and structural characteristics using the standard titration method for hydroxyl value, combined with gel permeation chromatography (GPC), Fourier transform infrared (FTIR), and liquid state phosphorus ( 31 P), carbon ( 13 C), and proton ( 1 H) NMR analyses. As the liquefaction temperature increased, viscosity of the polyols became higher with a corresponding broadening of the molecular weight (MW) distributions that also shifted toward higher MW. The liquefaction of biomass induced a high degree of modification to the bark components. These polyols had similar hydroxyl values but differed greatly in molecular structures. The polyol obtained through liquefaction at 90 °C had more secondary alcohols and contained sugars. Meanwhile, sugars were degraded into levulinate and formic esters in the polyols obtained at 130 and 160 °C. None of the polyols had condensed tannins, neither in their polymeric or monomeric state. Instead, aromatic ethers were seen in the carbon NMR spectra and various carboxyl functionalities were observed from the FTIR analysis. These results demonstrated the influence of the liquefaction temperature on the liquefaction behaviors of the bark biopolymers and provided an insight into the physical and structural properties of these bark-based polyols.
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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 | 0.000 |
| 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".