Plant celluloses, hemicelluloses, lignins, and volatile oils for the synthesis of nanoparticles and nanostructured materials
Bibliographic record
Abstract
A huge variety of plants are harvested worldwide and their different constituents can be converted into a broad range of bionanomaterials. In parallel, much research effort in materials science and engineering is focused on the formation of nanoparticles and nanostructured materials originating from agricultural residues. Cellulose (40-50%), hemicellulose (20-40%), and lignin (20-30%) represent major plant ingredients and many techniques have been described that separate the main plant components for the synthesis of nanocelluloses, nano-hemicelluloses, and nanolignins with divergent and controllable properties. The minor components, such as essential oils, could also be used to produce non-toxic metal and metal oxide nanoparticles with high bioavailability, biocompatibility, and/or bioactivity. This review describes the chemical structure, the physical and chemical properties of plant cell constituents, different techniques for the synthesis of nanocelluloses, nanohemicelluloses, and nanolignins from various lignocellulose sources and agricultural residues, and the extraction of volatile oils from plants as well as their use in metal and metal oxide nanoparticle production and emulsion preparation. Furthermore, details about the formation of activated carbon nanomaterials by thermal treatment of lignocellulose materials, a few examples of mineral extraction from agriculture waste for nanoparticle fabrication, and the emerging applications of plant-based nanomaterials in different fields, such as biotechnology and medicine, environment protection, environmental remediation, or energy production and storage, are also included. This review also briefly discusses the recent developments and challenges of obtaining nanomaterials from plant residues, and the issues surrounding toxicity and regulation.
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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 | 0.001 | 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 | 0.001 |
| 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".