Biorefining of softwoods using ethanol organosolv pulping: Preliminary evaluation of process streams for manufacture of fuel‐grade ethanol and co‐products
Bibliographic record
Abstract
Pulps with residual lignin ranging from 6.4-27.4% (w/w) were prepared from mixed softwoods using a proprietary biorefining technology (the Lignol process) based on aqueous ethanol organosolv extraction. The pulps were evaluated for bioconversion using enzymatic hydrolysis of the cellulose fraction to glucose and subsequent fermentation to ethanol. All pulps were readily hydrolyzed without further delignification. More than 90% of the cellulose in low lignin pulps (< or =18.4% residual lignin) was hydrolyzed to glucose in 48 h using an enzyme loading of 20 filter paper units/g cellulose. Cellulose in a high lignin pulp (27.4% residual lignin) was hydrolyzed to >90% conversion within 48 h using 40 filter paper units/g. The pulps performed well in both sequential and simultaneous saccharification and fermentation trials indicating an absence of metabolic inhibitors. Chemical and physical analyses showed that lignin extracted during organosolv pulping of softwood is a suitable feedstock for production of lignin-based adhesives and other products due to its high purity, low molecular weight, and abundance of reactive groups. Additional co-products may be derived from the hemicellulose sugars and furfural recovered from the water-soluble stream.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".