A comprehensive review on lignin extraction from lignocellulosic biomass, and nano‐lignin synthesis and modification for potential applications
Bibliographic record
Abstract
Abstract Lignin is the second‐largest natural aromatic polymer available on Earth. It plays a vital role in a plant's structural framework, which gives strength to the plant to sustain when facing adversities, and it also restricts it from being attacked by foreign entities such as insects and microorganisms. With significant properties, including biodegradability and being atoxic, it has immense possibility for high‐value applications such as antimicrobial agents, UV protectors, emulsion stabilizers, dye synthesis via its derivatives, carbon fibre, and biomaterials—lignin extraction from various lignocellulosic biomass (LCB) through different extraction methods. Further, after extraction, lignin is converted into nano‐lignin to broaden its applications through different routes. Due to high functional groups, nano‐lignin has a larger surface area and more reactivity. After nano‐lignin synthesis, it can be further modified through different chemical routes to increase the application area for specific end products. Therefore, this article summarizes the lignin extraction methods and nano‐lignin conversion routes, as well as the modification of lignin for its variety of applications. The article also provides the current technologies and future outlooks in lignin valorization for its potential applications in different fields.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".