Elucidation of Changes in Cellulose Ultrastructure and Accessibility in Hardwood Fractionation Processes with Carbohydrate Binding Modules
Bibliographic record
Abstract
We have recently presented a sequential treatment method, in which steam explosion (STEX) was followed by hydrotropic extraction (HEX), to selectively fractionate cellulose, hemicellulose, and lignin in hardwood into separate process streams. However, above a treatment severity threshold, the structural alterations in the cellulose-enriched fraction appeared to restrict the enzymatic hydrolyzability and delignification efficiency. To better understand the ultrastructural changes in the cellulose, hardwood chips were treated by single (STEX or HEX) and combined treatments (STEX and HEX), and the cellulose accessibility quantified with carbohydrate-binding modules (CBMs) that bind preferentially to crystalline (CBM2a) and paracrystalline cellulose (CBM17). Fluorescent-tagged versions of the CBMs were used to map the spatial distribution of cellulose substructures with confocal laser scanning microscopy. With increasing severities, STEX increased the apparent crystallinity (CBM2a/CBM17-ratio) and overall accessibility (CBM2aH6 + CBM17) of the cellulose, whereas HEX demonstrated the opposite trend. The respective effects could also be discerned in the combined treatments where increasing severities further resulted in higher hemicellulose dissolution and, although initially beneficial, in stagnating accessibility and hydrolyzability. This study suggests that balancing the severities in the two treatments is required to maximize the fractionation and simultaneously achieve a reactive and accessible cellulose that is readily hydrolyzable.
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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 | 0.001 |
| 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".