A review on the efficient enzymatic regulation for lignin separation from pulping pre-hydrolysis liquor
Bibliographic record
Abstract
Pulping pre-hydrolysis liquor (PHL), a byproduct generated during the production of dissolving pulp, is rich in biomass resources, including hemicellulose, its degradation products, and lignin. However, due to the low lignin content, complex structure, and high reactivity, its separation and extraction remain challenging. This limitation hinders the high-value utilization of PHL, leading to biomass resource waste and exacerbating environmental pollution. Enzyme-assisted separation technology, characterized by its eco-friendliness, high selectivity, and efficiency, offers a promising strategy for the effective separation of lignin from PHL. This review systematically summarizes the recent advances in enzyme-assisted lignin separation from PHL. First, the formation process of PHL, the dissolution mechanisms of its chemical components, physicochemical properties, and potential applications are introduced. Then, the major lignin separation techniques from PHL are reviewed, with a particular focus on the latest progress in enzyme-assisted separation technologies. Furthermore, the structural characteristics of typical enzymes and their mechanisms in selectively regulating lignin extraction from PHL are discussed in detail. To identify research hotspots and key scientific challenges in this field, a keyword co-occurrence network was constructed using VOSviewer software. Based on the current technical bottlenecks, future research directions are proposed, including the development of highly efficient novel enzymes, optimization of enzyme immobilization techniques, and exploration of nanozyme applications. Finally, considering conventional pulping and papermaking processes, a process flow for enzyme-assisted lignin separation from PHL is designed to enhance lignin separation efficiency and achieve its high-value utilization.
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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 | 0.001 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".