Recent advances in cannabidiol (CBD) extraction: a review of potential eco-friendly solvents and advanced technologies
Bibliographic record
Abstract
• sCO 2 is the preferred solvent for CBD extract due to its efficiency and purity. • Ionic liquids protect CBD from degradation due to low volatility and strong thermal stability. • CBD extraction can be influenced by the DES composition and proportion of components. • Optimizing the solvent-to-feed ratio in operating conditions is critical for CBD extraction. • UAE improved CBD yield and decreased extraction time. Cannabinoids, particularly cannabidiol (CBD), have been gaining attention for their numerous potential health benefits and are employed in various industries. However, there are unresolved challenges in CBD extraction including low yields, impurity issues, and environmental concerns, suggesting the requirement for green methods. Hence, our review objectives are to assess the efficacy, and the impact of novel solvents used for CBD extraction, considering green and sustainable techniques. The traditional extraction methods such as maceration and Soxhlet extraction used for CBD extraction have limitations such as low efficiency, long extraction times, high energy consumption, and substantial CO₂ emissions, raising environmental concerns. Emerging green extraction techniques, such as supercritical fluid extraction, deep eutectic solvents, and microwave-assisted extraction, offer promising alternatives by reducing solvent use, minimizing processing time, and enhancing extraction yields. Supercritical CO 2 extraction, utilizing supercritical fluids' unique properties offers efficient and safe CBD extraction. Emerging green solvents such as ionic solvents and deep eutectic solvents provide promising alternatives for CBD extraction due to their properties such as no or low toxicity compared to the traditionally used solvents. Pressurized liquid extraction, such as subcritical water extraction, and techniques like microwave-assisted and ultrasonic-assisted extraction provide rapid and efficient alternatives for CBD extraction. To fulfill the growing demand for CBD extracts, future research can aim at developing efficient and sustainable extraction techniques while reducing the potential degradation of CBD, removing impurities produced during extraction, and considering concepts of sustainability and the One Health approach.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".