Structure–process–yield interrelations in nanocrystalline cellulose extraction
Bibliographic record
Abstract
Abstract In order to improve the extraction of nanocrystalline cellulose (NCC) from sulfuric acid hydrolysis of chemical pulps, we have studied the effect of hydrolysis conditions on the degree of polymerization (DP), the extent of sulfation, morphological, and solid‐state characteristics of the extracted materials vis‐à‐vis yield. Our results demonstrate that sulfation plays a significant role in (i) determining the yield of, and (ii) imparting the unique solid‐state characteristics to, the extracted, H 2 O‐insoluble cellulose nanomaterial from sulfuric acid hydrolysis. The hydrolysis process is itself proven to be highly reproducible, and NCC with high crystallinity (>80%) and a yield between 21% and 38% could be extracted from a fully bleached, commercial softwood kraft pulp using 64 wt.% sulfuric acid at 45–65°C after freeze drying. The NCC aggregates, with iridescent patterns typical of chiral nematic materials, are parallelepiped rod‐like structures which possess cross‐sections in the nanometer range and lengths orders of magnitude larger, resulting in high aspect ratios. The Ruland–Rietveld analysis was employed to precisely resolve X‐ray diffraction patterns and obtain information on crystallite size, crystalline and amorphous areas, and crystallinity of the extracted materials.
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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 | 0.001 | 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".