A machine learning approach for thermodynamic modeling of the statically measured solubility of nilotinib hydrochloride monohydrate (anti-cancer drug) in supercritical CO<sub>2</sub>.

Nateghi, Hassan; Sodeifian, Gholamhossein; Razmimanesh, Fariba; Mohebbi Najm Abad, Javad

A machine learning approach for thermodynamic modeling of the statically measured solubility of nilotinib hydrochloride monohydrate (anti-cancer drug) in supercritical CO₂.

Nateghi, Hassan; Sodeifian, Gholamhossein; Razmimanesh, Fariba; Mohebbi Najm Abad, Javad.

Afiliación

Nateghi H; Department of Chemical Engineering, Faculty of Engineering, University of Kashan, Kashan, 87317-53153, Iran.
Sodeifian G; Laboratory of Supercritical Fluids and Nanotechnology, University of Kashan, Kashan, 87317-53153, Iran.
Razmimanesh F; Modeling and Simulation Centre, Faculty of Engineering, University of Kashan, Kashan, 87317-53153, Iran.
Mohebbi Najm Abad J; Department of Chemical Engineering, Faculty of Engineering, University of Kashan, Kashan, 87317-53153, Iran. sodeifian@kashanu.ac.ir.

Sci Rep ; 13(1): 12906, 2023 08 09.

Article en En | MEDLINE | ID: mdl-37558797

Asunto(s)

Dióxido de Carbono; Solubilidad; India; Termodinámica

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Dióxido de Carbono Tipo de estudio: Prognostic_studies País/Región como asunto: Asia Idioma: En Revista: Sci Rep Año: 2023 Tipo del documento: Article País de afiliación: Irán Pais de publicación: Reino Unido

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