Gholamhossein Sodeifian

Supercritical fluid (SF) technologies have wide applications in different industries. These are further gaining popularities owing to their high potential toward meeting sustainable goals (green technology). However, the properties of the fluids show a strong interplay of thermodynamic and kinetic properties owing to the large number of influential parameters like pressure, temperature, flow rate of SF, particle size, presence of cosolvents, time for extraction, influence of assisting mechanisms like ultrasound or microwave, and pretreatment or mix ratio of biomass subjected to extraction with their independent and interrelated influences. This review article aims to elaborate on the parameters influencing the SF properties, significance of experimental design (screening and optimization), need for mathematical modeling for data generation, and methods for data analysis. The article further studies the use of such generated and analyzed data toward scale‐up for appropriate response prediction and techno‐economic feasibility study for commercial implementation. Having enough knowledge about these issues helps us apply the SF as a sustainable and green technology in different fields.

In this study, the solubility of Florfenicol in supercritical carbon dioxide (SC-CO₂) was investigated for the first time under various conditions: pressures (P) from 120 to 270 bar (120 < P ≤ 270) and temperatures of 308, 318, 328, and 338 K. The equilibrium solubility parameter ranged from 0.22 to 3.65 kg/m³, resulting in mole fractions (y) ranging from 0.45 × 10^- 4 to 5.74 × 10^- 4. Two different modeling approaches were applied to correlate the drug solubility data. The first approach used an equation of state, specifically the Peng-Robinson equation of state with the van der Waals Type I mixing rule, yielding an average absolute relative deviation (AARD) of %10.48. The second approach comprised 22 density-based correlations categorized into three groups based on their independent variables: (T, ρ), (P, T), and (P, ρ, T). These correlations included those by Bartle et al., Garlapati and Madras (I & II), Chrastil, Andonova and Garlapati, Kumar and Johnston, del Valle and Aguilera, Sung-Shim, Ch and Madras, Adachi and Lu, Alwi-Garlapati, Bian et al., Cherif Si-Moussa, Mendez-Santiago and Teja, Jafari Nejad et al., Sodeifian et al. (Models I & II), Keshmiri et al., Jouyban et al., Khansary et al., Hozhabr et al., and Gordillo et al. These models have between three and six adjustable coefficients. Among the (T, ρ) category, the Adachi and Lu model showed better agreement (AARD = 4.91%), while among the (P, ρ, T) category, the Mendez-Santiago and Teja model showed the best agreement (AARD = 4.19%) with the solubility data of Florfenicol. Finally, the enthalpies of vaporization, total, and solvation were determined for the Florfenicol/SC-CO₂ system.

Reliable prediction of drug solubility in supercritical carbon dioxide (scCO₂) is crucial for the efficient design of pharmaceutical processes, including particle engineering and supercritical fluid-based extraction. Given that experimental determination of drug solubility in scCO₂ is costly and time-consuming, this study employs machine learning models to predict drug solubility in scCO₂, offering the advantage over thermodynamic models and empirical correlations of being able to predict the solubility of drugs beyond the model’s training range. In this work, authors use CatBoost, XGBoost, LightGBM, and RF models to predict the solubility of a set of drugs (Sixty-eight) in scCO₂. Statistical errors and graphical analyses showed that the XGBoost model performed better than other models and had high reliability for predicting solubility. Among the evaluated models, XGBoost delivered the most accurate predictions, achieving a root mean square error (RMSE) of just 0.0605 and an R² value of 0.9984. Notably, 97.68% of the data points fell within the model’s applicability domain, highlighting its strong predictive reliability. These outcomes underscore the capability of the XGBoost algorithm to serve as a robust and efficient approach for estimating solubility.

Exact drug solubility determination and modeling in supercritical carbon dioxide (SC-CO₂) would address the optimal experimental conditions for processing substances with supercritical fluid (SCF). In this investigation, for the first time, the solubility of Methadone hydrochloride is measured and modeled using the perturbed chain-statistical associating fluid theory (PC-SAFT) equation of state (EoS) alongside a set of eight semi-empirical models. The 24 experimental data points were obtained at pressures ranging from 120 to 270 bar and temperatures between 308 and 338 K. The minimum and maximum mole fractions observed were 1.74 × 10^-5 and 4.67 × 10^-5, respectively, occurring within these ranges. The results indicate that both pressure and temperature significantly affect Methadone hydrochloride solubility, so that as both pressure and temperature rise, the solubility increases. Among the models, the Chrastil model and PC-SAFT EoS exhibited the lowest absolute average relative deviation (AARD) % of approximately 6.71% and 9.73%, respectively. Finally, the enthalpies of the methadone hydrochloride/CO₂ binary system were calculated as = 29.14 kJ/mol, = 23.04 kJ/mol, = 40.61 kJ/mol, = -11.47 kJ/mol, and = -17.57 kJ/mol.

This study explored the potential of blending poly-(ether sulfone)/disulfonated poly-(ether sulfone) (PES/SPES) at various weight ratios with and without polyvinylpyrrolidone (PVP) to enhance the membrane properties for potential applications like fuel cells, water treatment, and lithium-ion battery separators. SPES was appropriately synthesized as a copolymer constituted of alternated unsulfonated (ES) and disulfonated (SES) sequences, achieving a degree of sulfonation of about 100%. SPES improved the membrane's hydrophilicity, while PVP acts as compatibilizer of PES/SPES blends and pore creator. For comparison purposes, two different approaches were exploited for membrane fabrication: casting and electrospinning techniques. The membranes were characterized using various techniques to evaluate thermal stability, hydrophilicity, uptake behavior, morphology, and microstructure. SEM images revealed that the SPES content and PVP incorporation significantly influenced membrane morphology and porosity. Water uptake increased with SPES content, while methanol uptake was less affected. PVP enhanced both water and methanol uptake in the solvent-cast membranes. Electrospun membranes exhibited higher uptake due to their increased porosity and surface area. Contact angle measurements confirmed that SPES improved hydrophilicity and surface roughness played a crucial role. TGA analysis showed that PES membranes had the highest thermal stability, while SPES decreased it. DSC analysis revealed that PVP acted as a compatibilizer in solvent-cast membranes but not in electrospun ones. The study demonstrated that both blend composition and fabrication techniques could be tailored to optimize membrane properties for specific applications. The findings provide valuable insights for developing advanced membranes with an enhanced performance.