Deep eutectic solvents as enablers of lignin nanoparticles: Advances in extraction, valorization and challenges
Bibliographic record
Abstract
Lignin nanoparticles (LNPs) exhibit high specific surface area, superior absorption capacity, and tunable size/structure, driving significant research interest. Deep eutectic solvents (DESs) have emerged as efficient and sustainable media for LNPs extraction. This review fills a critical gap by systematically analyzing DESs-based LNPs extraction and applications. We comprehensively evaluate acidic, neutral, and alkaline DESs systems for LNPs synthesis, with emphasis on interfacial formation mechanisms during fractionation. The work further explores interfacial engineering strategies for LNPs valorization in functional composites. The DES systems enable efficient, green LNPs extraction with precise size control. But the different system displayed different LNPs characteristics, extraction efficiency. The interfacial self-assembly mechanisms govern LNPs formation during DES fractionation. Furthermore, LNPs enhance composites in food packaging, enzyme immobilization, wastewater adsorbent, energy storage. However, critical barriers persist in aggregation control, purity, and economic feasibility. DES-extracted LNPs offer transformative potential for high-value bio-composites. This review is expected to provide a platform database but insightful understanding for effective engineering design of LNPs extraction by DESs, and for further innovations of functionalized LNPs composites.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".