Fractionation of Flax Shives by Water and Aqueous Ammonia Treatment in a Pressurized Low-Polarity Water Extractor
Bibliographic record
Abstract
Fractionation of flax shives into cellulose, hemicellulose, and lignin with a two-stage extraction process using water and aqueous ammonia was carried out in a pressurized low-polarity water extractor operated at different temperatures, flow rates, and ammonia concentrations. During the first stage with water, 84% of hemicellulose and 32% of lignin were removed at 190 degrees C at a flow rate of 1.5 mL/min for 30 min. During the second stage with aqueous ammonia, more than 77% of the lignin was removed, and hemicellulose removal reached 95% at 200 degrees C at a flow rate of 0.5 mL/min and with a solvent/feed ratio of 40 mL/g. The temperature and flow rate had a significant effect on lignin removal. The impact of additives (anthraquinone and hydrogen peroxide) and modifications (overnight soaking, reduced particle size, and elevated temperature) on lignin extraction was also studied. The combination of higher temperatures and reduced particle sizes resulted in enhanced lignin extraction. The extraction profiles of free phenolics (vanillin, acetovanillone, and vanillic acid) during the two-stage processing were monitored and compared with those of lignin.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".