Characterization of sugar maple and red oak bark: Efficient lignin extraction via the Organosolv and acidic dioxane process
Bibliographic record
Abstract
The barks of two wood species, sugar maple (SM) and red oak (RO), were investigated to characterize their chemical composition and isolate the lignins they contain, representing relatively underexplored and underutilized polymers. Characterizing these two barks involved assessing extractive components, Klason and acid-soluble lignin, ash content, and sugars. It was observed that RO bark had a higher concentration of extractive substances and a lower Klason lignin content compared to SM bark. The Klason lignin content in the barks of the SM and RO bark was measured at 35.0% and 26.4%, respectively, higher than values reported for corresponding to wood of the same species, 21.8% and 22.2%, respectively, for RO and SM. Lignin isolation from bark was achieved by utilizing Organosolv catalytic and acid dioxane processes. Notably, the Organosolv process yielded lignins of considerably higher purity. The structural analysis of the bark lignins was peformed by combination of spectroscopic methods: FTIR,13P NMR, and solid-state NMR. Compared to dioxane lignins, a higher concentration of OH bound to syringyl and guayacyl units were found in Organosolv lignins. In contrast, aliphatic OH units are more important in dioxane lignins than in Organosolv lignins. Furthermore, sugar analysis was conducted via HPLC, thermal analysis was conducted through DSC and TGA, and ash composition identification was done using ICP-OES. Notably, higher Mw and Mn values are determined by GPC for dioxane lignins than for Organosolv lignins.
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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".