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Record #66008

Prediction of busulfan solubility in supercritical CO2 using tree-based and neural network-based methods

Publisher
Elsevier
Original paper date
Jan 29, 2022
Institution
School of Chemical Science and Technology, Yunnan University, Yunan province, 650091, China · Department of Chemical Engineering and Petroleum Industries, Al-Mustaqbal University College, Babylon, 51001, Iraq · Institute of Pharmacy, Sechenov First Moscow State Medical University, 8 Trubetskaya St, bldg 2, Moscow, 119991, Russian Federation · Laboratory of Food Chemistry, Federal Research Center of Nutrition, Biotechnology and Food Safety, 2/14 Ustyinsky pr, Moscow, 109240, Russian Federation · Department of Health and Rehabilitation Sciences, College of Applied Medical Sciences, Prince Sattam bin Abdulaziz University, Al Kharj, Saudi Arabia · Department of Physical Therapy, Kasr Al-Aini Hospital, Cairo University, Giza, Egypt · Department of Pharmacology, Saveetha Dental College, Saveetha Institute of Medical and Technical Science, Saveetha University, Chennai, India · Department of Chemical Engineering, Ming Chi University of Technology, New Taipei City, Taiwan · Faculty of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City, 700000, Viet Nam · Department of Computer Science, Jeddah International College, Jeddah, Saudi Arabia · Mechanical and Materials Engineering Department, Faculty of Engineering, University of Jeddah, PO Box 80327, Jeddah, 21589, Saudi Arabia
Open access
No
Article type
Research Article
Broad categories
(B/T) Technology(PHY) Engineering - Chemical
Citation lifecycle

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Pre-retraction
16
Same day
0
Post-retraction
9
% post-retraction
37.5%
~6.5 citations/yr before retraction · ~4.2 citations/yr after
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