首页|New Findings from Arizona State University Update Understanding of Machine Learn ing (Comprehensive Study of Medications Solubility In Supercritical Co2 With and Without Co-solvent; Laboratory, Theoretical, and Intelligent Approaches)
New Findings from Arizona State University Update Understanding of Machine Learn ing (Comprehensive Study of Medications Solubility In Supercritical Co2 With and Without Co-solvent; Laboratory, Theoretical, and Intelligent Approaches)
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Current study results on Machine Learn ing have been published. According to news reporting originating in Phoenix, Ari zona, by NewsRx journalists, research stated, "Determining the dissolution chara cteristics of medicines in supercritical CO2 is vital for formulating innovative drug delivery systems through an efficient supercritical process. This study in vestigates the solubility of three poorly bioavailable drugs -Topiramate, Mecliz ine, and Dimenhydrinate-in supercritical CO2, both with and without ethanol co-solvent, over a temperature range of 308 K to 348 K and pressures from 17 MPa to 41 MPa." The news reporters obtained a quote from the research from Arizona State Univers ity, "The solubility of these medicines in supercritical CO2 (binary system) is notably low, ranging from 2.5 x 10-6 4.54 x 10-6, 0.26 x 10-5 -2.3 x 10-5, and 0.20 x 10-5 -1.91 x 10-5 in mole fraction, respectively. However, in the presen ce of ethanol (ternary system), their supercritical solubility significantly inc reases by factors of 2.75-5.84, 1.40-3.20, and 2.04-4.85, respectively. The supe rcritical solubility of the mentioned compounds are theoretically evaluated usin g several approaches, including empirical models, a machine learning methodology employing a multilayer perceptron neural network, thermodynamic models based on two cubic equations of state (Peng-Robinson (PR) and Soave-Redlich-Kwong (SRK)) , and a non-cubic equation of state (perturbed chain-statistical associating flu id theory (PC-SAFT)), as well as two expanded liquid models (UNIQUAC and Wilson) . The findings revealed that all the specified models demonstrate acceptable acc uracy in correlating the experimental data of the specified drugs in both binary and ternary systems. Among these, the PR and SRK thermodynamic models, along wi th some empirical models, show the best results."
PhoenixArizonaUnited StatesNorth a nd Central AmericaCyborgsEmerging TechnologiesMachine LearningArizona St ate University
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