The Effect of Flow Rate of Compressed Hot Water on Xylan, Lignin, and Total Mass Removal from Corn Stover
Bibliographic record
Abstract
Flowing hot water through cellulosic biomass offers many promising features for advanced pretreatment, and a better understanding of the mechanisms responsible for flowthrough behavior could allow us to capitalize on its key attributes while overcoming its limitations. In this study, extensive data were developed to show the effect of flow on the fate of hemicellulose, lignin, and total mass for hot-water pretreatment of corn stover in a small tubular flowthrough reactor at 180, 200, and 220 °C. Solubilization of hemicellulose increased with flow, especially at high temperatures; a result that is inconsistent with traditional first-order kinetic models. The dissolved xylan in the hydrolyzate was mostly oligomers over this temperature range, and the fraction as oligomers increased with flow rate. Also of importance, lignin removal increased from less than about 30% for batch reactors to about 75% at high flow rates and was nearly linearly related to hemicellulose release for the flowthrough reactor. These observations suggest that mass transfer or other physical factors, and not strictly first-order homogeneous chemical kinetics, impact hemicellulose hydrolysis. In addition, lignin appears to be released throughout hydrolysis, but its fate may be governed by subsequent precipitation reactions unless it is removed first.
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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 | 0.001 |
| 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 | 0.001 |
| 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".