Computational intelligence modeling of nanomedicine preparation using advanced processing: Solubility of fludrocortisone acetate in supercritical carbon dioxide

Hani, Umme; sinnah, Zainab Ali Bu; Obaidullah, Ahmad J.; Huwaimel, Bader; Alanazi, Muteb; Alharby, Tareq Nafea; Lahiq, Ahmed A.; Alshehri, Abdullah

doi:10.1016/j.csite.2023.102968

Case Studies in Thermal Engineering

2023

DOI: 10.1016/j.csite.2023.102968

|View full text |Cite

Computational intelligence modeling of nanomedicine preparation using advanced processing: Solubility of fludrocortisone acetate in supercritical carbon dioxide

Umme Hani

Zainab Ali Bu sinnah

Ahmad J. Obaidullah

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

2024

Publication Types

Select...

Article5

Relationship

Self Cite0

Independent5

Authors

Journals

Cited by 5 publications

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

A new semi-empirical model for correlation of solubility of ligands in supercritical carbon dioxide

Subashree,

Shruti Krishna,

Pitchaiah

et al. 2024

Chem. Pap.

View full text Add to dashboard Cite

The solubility data of task specific ligands in supercritical carbon dioxide medium (scCO2) plays an important role in development of green separation methods for recovery of metal ions. However, experimental determination of solubility of these ligands at different process conditions is difficult, time-intensive and necessitating suitable model predictions. Many models are developed in the literature for the correlation of solubility of liquid and solid solutes in scCO2 medium. However, specific models for solubility correlation of ligands in scCO2 medium are not reported. Hence, in the present study, a new semi-empirical model was proposed to predict the solubility of ligands in scCO2 medium and is a modified version of existing Chrastil model. The correlation ability of new model was compared with existing density based models (Chrastil,

show abstract

A new semi-empirical model for correlation of solubility of ligands in supercritical carbon dioxide

Subashree,

Shruti Krishna,

Pitchaiah

et al. 2024

Chem. Pap.

View full text Add to dashboard Cite

show abstract

Advanced modeling and intelligence-based evaluation of pharmaceutical nanoparticle preparation using green supercritical processing: Theoretical assessment of solubility

Alshahrani

Obaidullah

Alanazi

et al. 2023

Case Studies in Thermal Engineering

View full text Add to dashboard Cite

Development of SVM-based machine learning model for estimating lornoxicam solubility in supercritical solvent

Zhang

Mahdi

2023

Case Studies in Thermal Engineering

View full text Add to dashboard Cite

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Computational intelligence modeling of nanomedicine preparation using advanced processing: Solubility of fludrocortisone acetate in supercritical carbon dioxide

Cited by 5 publications

References 26 publications

A new semi-empirical model for correlation of solubility of ligands in supercritical carbon dioxide

A new semi-empirical model for correlation of solubility of ligands in supercritical carbon dioxide

Advanced modeling and intelligence-based evaluation of pharmaceutical nanoparticle preparation using green supercritical processing: Theoretical assessment of solubility

Development of SVM-based machine learning model for estimating lornoxicam solubility in supercritical solvent

Contact Info

Product

Resources

About