Green valorization of cannabis wastes into CNC/AgNP nanohybrids for controlled silver ion release in antibacterial hydrogels
Bibliographic record
Abstract
The rapid expansion of cannabis cultivation has led to a substantial accumulation of agricultural waste, particularly stems and leaves, posing environmental challenges. In this study, the cannabis wastes were valorized into nanomaterials. Cellulose nanocrystals (CNC) were extracted from cannabis stems via KMnO 4 /oxalic acid oxidation, yielding rod-shaped particles with 19 nm in diameter and 196 nm in length. Cannabis leaf extract exhibited strong reducing capability for the synthesis of silver nanoparticles (AgNPs). CNC served as a substrate for AgNPs to form nanohybrids. UV–Vis spectroscopy and TEM confirmed the formation of AgNPs with a diameter of 16 nm, while the CNC/AgNP retained a comparable dimension. XPS confirmed the presence of metallic silver (Ag⁰) in the nanohybrids, while XRD revealed no significant alteration in the crystalline structure after incorporation. Nanohybrids were incorporated into polyvinyl alcohol/chitosan hydrogels, enhancing mechanical strength and conferring antibacterial activity against Pseudomonas aeruginosa and Staphylococcus aureus at 0.5 wt% loading. AgNP hydrogels showed rapid Ag + release within 6 h, while CNC/AgNP enabled a two-stage, sustained release. Kinetic modeling indicated Fickian diffusion for AgNP films and non-Fickian transport for nanohybrids. Cytotoxicity tests of the nanohybrid hydrogels demonstrated an acceptable viability rate in HDFa. These findings position CNC/AgNP nanohybrids as sustainable materials offering controlled Ag + release, antibacterial efficacy, and biocompatibility for advanced biomedical systems.
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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) | 0.001 | 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".