Evaluation of steam explosion pretreatment on the cellulose nanocrystals (CNCs) yield from poplar wood
Bibliographic record
Abstract
The high sulfuric acid concentration used in the hydrolysis of cellulose to isolate cellulose nanocrystals (CNCs) leads to low yields due to the dissolution of both amorphous and semi-crystalline cellulose. The present study explored the use of steam explosion pretreatment before acid hydrolysis to enhance the crystallization of semi-crystalline/ non-crystalline cellulose and generating new CNC precursors with poplar wood as feedstock. The crystallinity of steam exploded poplar wood increased 1.3-fold compared to untreated poplar wood. Consequently, the overall yield of CNCs of steam exploded poplar wood increased 2.5-fold compared to untreated poplar wood. Moreover, the steam explosion pretreatment did not affect the quality of the CNCs with regard to the crystal size, crystallinity, and colloidal stability. Whereas the thermal stability of the CNCs increased due to the steam explosion pretreatment. This study demonstrates a simple and scalable pretreatment step that can significantly improve the CNCs yield from the acid hydrolysis step thereby improving the overall economics and commercial viability.
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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) | 0.000 | 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".