Effect of Pre-Extraction on Composition of Residual Liquor Obtained from Catalytic Organosolv Pulping of Sugar Maple Bark
Bibliographic record
Abstract
Background: We have determined previously that the water extract of sugar maple bark contained an important quantity of a complex sugar. In this study, we investigated the organosolv pulping of pre-extracted bark to follow the acid conversion of sugars into major products, furfural and 5-hydroxymethyl-2-furfural (HMF), while comparing the structures of organosolv lignins. Methods: The bark particles were pre-extracted with an ethanol–water mixture or water only. The extractives-free barks were then converted into cellulosic pulp and lignin by a patented organosolv process. The composition of residual liquor was determined by using HPLC-UV. Results: The pre-extraction with water was more efficient for complex sugars recovery than with the ethanol–water system. HMF was determined to be more abundant in residual liquor than furfural after ethanol–water pre-extraction while their quantities were comparable in the residual liquor after water pre-extraction. The higher yield of HMF from ethanol–water pre-extracted bark (1.18%) than from water pre-extracted (0.69%) could be related to the efficiency of complex sugar removal during the pre-extraction step. Conclusions: The pre-extraction before pulping affected, at least in part, the composition of residual liquor in terms of HMF production. These results demonstrate how the bark can be converted into valuable products and intermediates for organic synthesis.
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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 | 0.000 |
| 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".