Effect of initial moisture content and chip size on the bioconversion efficiency of softwood lignocellulosics
Bibliographic record
Abstract
Previous optimization strategies for the bioconversion of lignocellulosics by steam explosion technologies have focused on the effects of temperature, pH, and treatment time, but have not accounted for changes in severity brought about by properties inherent in the starting feedstock. Consequently, this study evaluated the effects of chip properties, feedstock size (40-mesh, 1.5 x 1.5 cm, 5 x 5 cm), and moisture content (12% and 30%) on the overall bioconversion process, and more specifically on the efficacy of removal of recalcitrant lignin from the lignocellulosic substrates following steam explosion. Increasing chip size resulted in an improvement in the solids recovery, with concurrent increases in the water soluble, hemicellulose-derived sugar recovery (7.5%). This increased recovery is a result of a decrease in the "relative severity" of the pretreatment as chip size increases. Additionally, the decreased relative severity minimized the condensation of the recalcitrant residual lignin and therefore increased the efficacy of peroxide fractionation, where a 60% improvement in lignin removal was possible with chips of larger initial size. Similarly, increased initial moisture content reduced the relative severity of the pretreatment, generating improved solids and hemicellulose-derived carbohydrate recovery. Both increased chip size and higher initial moisture content results in a substrate that performs better during peroxide delignification, and consequently enzymatic hydrolysis. Furthermore, a post steam-explosion refining step increased hemicellulose-derived sugar recovery and was most effectively delignified (to as low as 6.5%). The refined substrate could be enzymatically hydrolyzed to very high levels (98%) and relatively fast rates (1.23 g/L/h).
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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 | 0.000 |
| 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".