Utilization of Lignocellulosic Biomass for Production of Nanocellulose
Bibliographic record
Abstract
Agricultural wastes, forest remains, domestic wastes, industrial food processing residues, crop residues, and algae are termed as lignocellulosic biomass. These biomasses are rich sources, in varying proportions, of lignin, cellulose, and hemicellulose. The utilization, or upcycling, of these biomasses for extraction and development of high-end products can be an approach towards sustainable development. However, the structure of these biomasses is very complex, which makes them quite tough to convert to high-end products. The utilization of these biomasses also depends upon the source, composition, and structure of cellulose present in the raw material. Therefore, this chapter provides a comprehensive discussion on various pre-treatment methods and further extraction processes for isolating cellulose, lignin, and hemicellulose from the biomass for its valorization into high-end products. This chapter also includes various green extraction technologies for the isolation of nanocellulose, including methods with deep eutectic solvent and ionic liquids, microwave-assisted, ultrasound-assisted, and high hydrostatic pressure extraction processes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.014 |
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".