Valorization of Bark Using Ethanol–Water Organosolv Treatment: Isolation and Characterization of Crude Lignin
Bibliographic record
Abstract
Bark serves multiple functions for a tree by combining protection and fluid transport. The tissue itself is composed of multiple types of material that includes typical cell wall structural polymers of cellulose and lignin, as well as extractives. With suitable fractionation, these bark constituents may serve to help valorize biomass when converted into feedstocks for polymeric precursors, such as aromatic polyols. In this study, organosolv processing was used to solubilize around 50% of pine bark (PB) and oak bark (OB), followed by the recovery of around 20% crude lignin. Two-dimensional 1H–13C heteronuclear single quantum coherence (2D HSQC) NMR and 13C NMR revealed the crude lignin also contained mixed polyphenolics, suberin-compounds, and carbohydrate compounds, especially for PB. To optimize the extraction process, a two-level factorial design was used to study the impacts of temperature, ethanol concentration, and solid loading on the yield and related characteristics of crude lignin. Increased temperature and ethanol concentration during processing allowed more solubilization of the components along with the extraction of crude lignin with higher yield. The correlation analysis found that the thermal stability of crude lignin had a direct relationship with aromatic hydroxyl group content, while the glass transition temperature was impacted by the lower molar mass components contained in the crude lignin. Overall, the study showed promise to solubilize a high portion of bark material and provided insight into structure–property relationships of crude lignin-derived from an important, yet underutilized resource as a function of processing conditions.
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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 | 0.000 |
| 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) | 0.000 | 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".