Potential for Application of Lignin Based Micro/Nanostructures as aMicro/Nanocarrier in the Controlled Release Systems: A Review
Bibliographic record
Abstract
Background: A promising strategy is to apply biodegradable and biocompatibility lignin micro/nanoparticles (LMPs/LNPs) as carriers or coating materials for biological active agent delivery in agriculture medicine and pharmaceuticals. Controlled release systems (CRSs) based on LMPs/LNPs are suitable systems to target specific tissues, cells, or plant roots by taking advantage of the unique properties of LMPs/LNPs. Methods: This review discusses changes in the properties of LNPs caused by different parameters in the synthesis method, such as the type of biologically active agent, loading/release method, modification method, encapsulation efficiency, and release rate of the CRSs based on LMPs/LNPs. Results: Research shows that during the LMPs/LNPs synthesis, nanospheres with a porous surface, nanocapsules, or hollow nanospheres with excellent stability and chemical properties are produced, which causes high loading capacity and reduced release rates of active agents. Moreover, the advantages and technical challenges of lignin application as a micro/ nanocarrier were investigated. Conclusion: Finally, several suggestions for the future trend of research and development were recommended.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".