Valorization of cannabis stalks into cellulose nanocrystals: A sustainable solution for cannabis waste management
Bibliographic record
Abstract
In the cannabis industry, inflorescences are the primary products, while byproducts like stalks are typically discarded due to regulatory constraints on tetrahydrocannabinol (THC) levels. Although stalks usually contain THC below the regulatory thresholds, microbial contamination often leads to the disposal of entire plants as waste. This study investigates the extraction and characterization of cellulose nanocrystals (CNCs) from cannabis stalks through sequential alkali treatment, bleaching, and metal salt oxidation, offering a sustainable solution for cannabis waste valorization. Chemical analysis on raw stalks resulted in 57 % cellulose, 17 % hemicellulose, and 14 % lignin, with total THC content of 0.078 %. A mild NaOH pretreatment (1–3 %, 90°C, 1 h) further reduced total THC to 0.036 % in stalks and from 6.016 % to 0.384 % in mixed biomass (combination of inflorescences, leaves and stalks), suggesting a cost-effective manner to produce reduced-THC cannabis biomass for further utilization meeting regulatory standards. The extracted CNCs exhibited diverse functional groups, a crystallinity of 72 %, and a spindle-shaped morphology with average length of 280 nm, width of 9 nm, and aspect ratio of 34. With a 27 % yield, CNCs from cannabis stalks hold possibilities for applications in bio-composites, adhesives, absorbent and packaging materials. By transforming cannabis waste into high-value nanomaterials, this study supports circular economy principles and offers a sustainable approach to resource optimization in the cannabis industry. • The cannabis industry produces ∼50 % waste, primarily stalks. • Cannabis waste disposal (mainly landfilling) creates cost/environmental burdens. • Stalks contain ∼57 % cellulose, ideal for conversion into high-value bioproducts. • Chemically processed stalks yield ∼27 % cellulose nanocrystals (C-CNCs). • Spindle-shaped C-CNCs exhibit excellent sustainable biomaterial potential.
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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".