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Enhancing Cannabis Extraction Efficiency and Sustainability through Quantum Computing: (A-Review).
- Source :
- Oriental Journal of Chemistry; Dec2023, Vol. 39 Issue 6, p1419-1436, 18p
- Publication Year :
- 2023
-
Abstract
- The plant is also known as hemp, although this term is often used only to refer to varieties of Cannabis cultivated for non-drug use. Cannabis has long been used as hemp fiber, hemp seeds and their oil, hemp leaves as vegetable and juice, for medicinal purposes and as a recreational drug. It has been widely used specifically in incense, peaceful sleep for cancer affected patients and traditional medicine. Its common uses include treating knee joint pain, inflammatory-related complaints, diarrhea, and a tonic, sedative, and cardio caring agent. Cannabis sativa is the hemp plant from which marijuana and cannabinoids (leaves, stems, seeds) are derived. The most potent form of this plant's extracts is hash oil, a liquid. Quantum computing, on the other hand, offers unprecedented computational power and can revolutionize various scientific fields. The study's goal is to explore the potential of quantum computing to enhance the extraction process. By employing quantum algorithms, the project aims to optimize critical parameters such as pressure, temperature, and extraction time, leading to improved efficiency and higher yields. Quantum simulations will model the behavior of CO<subscript>2</subscript> as a supercritical fluid within the cannabis matrix, supplying insights into the complex dynamics of the extraction process. Finally, the use of quantum algorithms promises to ease the development of more efficient and sustainable extraction methods, resulting in the production of high-quality Cannabis-derived products with enhanced medicinal and industrial applications. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 0970020X
- Volume :
- 39
- Issue :
- 6
- Database :
- Complementary Index
- Journal :
- Oriental Journal of Chemistry
- Publication Type :
- Academic Journal
- Accession number :
- 175970916
- Full Text :
- https://doi.org/10.13005/ojc/390604