Multifunctional lignin-based nanocomposites and nanohybrids
Bibliographic record
Abstract
Significant progress in lignins valorization and development of high-performance sustainable materials have been achieved in recent years. Reports related to lignin utilization indicate excellent prospects considering green chemistry, chemical engineering, energy, materials and polymer science, physical chemistry, biochemistry, among others. To fully realize such potential, one of the most promising routes involves lignin uses in nanocomposites and nanohybrid assemblies, where synergistic interactions are highly beneficial. This review first discusses the interfacial assembly of lignins with polysaccharides, proteins and other biopolymers, for instance, in the synthesis of nanocomposites. To give a wide perspective, we consider the subject of hybridization with metal and metal oxide nanoparticles, as well as uses as precursor of carbon materials and the assembly with other biobased nanoparticles, for instance to form nanohybrids. We provide cues to understand the fundamental aspects related to lignins, their self-assembly and supramolecular organization, all of which are critical in nanocomposites and nanohybrids. We highlight the possibilities of lignin in the fields of flame retardancy, food packaging, plant protection, electroactive materials, energy storage and health sciences. The most recent outcomes are evaluated given the importance of lignin extraction, within established and emerging biorefineries. We consider the benefit of lignin compared to synthetic counterparts. Bridging the gap between fundamental and application-driven research, this account offers critical insights as far as the potential of lignin as one of the frontrunners in the uptake of bioeconomy concepts and its application in value-added products.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".