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Lacosamide, a widely used antiepileptic drug, suffers from poor solubility in conventional solvents, which limits its bioavailability. Supercritical carbon dioxide (SC-CO₂) has emerged as an environmentally friendly substitute solvent for pharmaceutical processing. In this study, the solubility of Lacosamide in SC-CO₂ was modeled and predicted using several machine learning techniques, including Gradient Boosting Decision Tree (GBDT), Multilayer Perceptron (MLP), Random Forest (RF), Gaussian Process Regression (GPR), Extreme Gradient Boosting (XG Boost), and Polynomial Regression (PR). These models have the ability to model nonlinear relationships. Experimental solubility information within a large span of pressures and temperatures were employed for model training and validation. The findings suggested that all applied models were competent in providing reliable predictions, with GBDT (R 2 = 0.9989), XG Boost (R 2 = 0.9986), and MLP (R 2 = 0.9975) exhibiting the highest accuracy, achieving the highest coefficient of determination (R 2 ). Overall, combining experimental data with advanced machine learning algorithms offers a powerful approach for predicting and optimizing drug solubility in supercritical systems, thereby facilitating the design of scalable pharmaceutical processes.

The solubility of an anti-hyperglycemic agent drug, (R)-4-oxo-4-[3-(trifluoromethyl)-5,6-dihydro [1,2,4] triazolo[4,3-a] pyrazin-7(8H)-yl]-1-(2,4,5-trifluorophenyl) butan-2-amine (also known as Sitagliptin phosphate ) in supercritical carbon dioxide (scCO 2 ) was determined by ananalytical and dynamic technique at different temperatures (308, 318, 328 and 338 K) and pressure (12–30 MPa) values. The measured solubilities were in the range of 3.02 × 10 –5 to 5.17 × 10 –5 , 2.71 × 10 –5 to 5.83 × 10 –5 , 2.39 × 10 –5 to 6.51 × 10 –5 and 2.07 × 10 –5 to 6.98 × 10 −5 in mole fraction at (308, 318, 328 and 338) K, respectively. The solubility data were correlated with existing density models and with a new association model.

A static method is employed to determine the solubilities of verapamil in supercritical carbon dioxide (SC-CO 2 ) at temperatures between 308 and 338 K and pressures between 12 and 30 MPa. The solubility of verapamil in SC-CO 2 expressed as mole fraction are in the range of 3.6 × 10 –6 to 7.14 × 10 –5 . Using four semi-empirical density-based models, the solubility data are correlated: Chrastil, Bartle, Kumar–Johnston (K–J), and Mendez-Santiago and Teja (MST), two equations of state (SRK and PC-SAFT EoS), expanded liquid models (modified Wilson's models), and regular solution model. The obtained results indicated that the regular solution and PC-SAFT models showed the most noteworthy exactness with AARD% of 1.68 and 7.45, respectively. The total heat, vaporization heat, and solvation heat of verapamil are calculated at 39.62, 60.03, and − 20.41 kJ/mol, respectively. Regarding the poor solubility of verapamil in SC-CO 2 , supercritical anti-solvent methods can be an appropriate choice to produce fine particles of this drug.

The size of the drug particles is one of the essential factors for the proper absorption of the drug compared to the dose of the drug. When particle size is decreased, drug uptake into the body increases. Recent studies have revealed that the rapid expansion of supercritical solution with cosolvent plays a significant role in preparing micron and submicron particles. This paper examines the preparation of Erlotinib hydrochloride nanoparticles using a supercritical solution through the cosolvent method for the first time. An examination of the parameters of temperature (318–338 K), pressures (15–25 MPa) and nozzle diameter (300–700 μm) was investigated by Box-Behnken design, and their respective effects on particle size revealed that the nozzle diameter has a more significant impact on particle size than the other parameters. The smallest particles were produced at temperature 338 K, pressure 20 MPa, and nozzle diameter 700 μm. Besides, the ERL nanoparticles were characterized using SEM, DLS, XRD, FTIR, and DSC analyses. Finally, the results showed that the average size of the ERL particles decreased from 31.6 μm to 200–1100 nm.

In this study, the solubility of mesalazine in supercritical carbon dioxide with and without cosolvent was carried out for the first time at different temperatures and pressure values ranging from 308 to 338 K and 12 to 30 MPa, respectively. The determined experimental molar solubilities of mesalazine in supercritical carbon dioxide were in the range of 4.41 × 10 –5 to 9.97 × 10 –5 (308 K), 3.9 × 10 –5 to 13.1 × 10 –5 (318 K), 3.4 × 10 –5 to 16 × 10 –5 (328 K) and 3.3 × 10 –5 to 18.4 × 10 –5 (338 K). Meanwhile, the determined experimental molar solubilities in supercritical carbon dioxide using 2% dimethyl sulfoxide as cosolvent were in the range of 28.22 × 10 –5 to 36.2 × 10 –5 (308 K), 26.07 × 10 –5 to 51.41 × 10 –5 (318 K), 25.02 × 10 –5 to 69.07 × 10 –5 (328 K) and 25.86 × 10 –5 to 82.6 × 10 –5 (338 K). A novel association model was employed to simulate the solubility data of the binary and ternary systems. Various semiempirical correlations were utilized to calculate the solubility of mesalazine in supercritical carbon dioxide. The new association model was deemed the most superior model, achieving an average absolute relative deviation value of 4.13% without a cosolvent, and 3.36% when a cosolvent was included.

This study investigated the solubility of chlordiazepoxide in supercritical carbon dioxide (SCCO₂) at pressures ranging from 12 to 30 (MPa) and temperatures ranging from 308 to 338 (K). The measured solubilities ranged from 19.8 to 576 parts per million (ppm), or mole fractions of 0.0198 × 10⁻³ to 0.576 × 10⁻³. We used two equations of state (SRK and ECM-PR), enlarged liquid models, and semi-empirical analysis to assess the experimental data. Applying these models to the experimental solubility data of the chlordiazepoxide-SCCO₂ system revealed that Chrastil’s empirical models best aligned with the two sets of data. An important step in understanding the system’s behavior was applying the SRK and UNIQUAC models to the drug’s solubility data. Density-dependent empirical models, particularly the Chrastil correlation (AARD% = 5.30, R² = 0.996) and the UNIQUAC model (AARD% = 6.12), provided the highest predictive accuracy for chlordiazepoxide solubility in SCCO₂. In contrast, cubic EoS models (SRK and ECM-PR) exhibited significant deviations, with AARD% values approaching 20%, suggesting their limited reliability for this system. The UNIQUAC model indicated excellent accuracy, with AARD% of 6.12 and R 2 of 0.981 in correlating solubility data of this binary system.

In this study, mechanisms of surfactant mixture and different salts for enhanced oil recovery in carbonate oil reservoirs were investigated experimentally. Therefore, a novel idea of combining two different surfactants including cetyltrimethylammonium bromide (CTAB) as a cationic surfactant and TR-880 as a zwitterionic surfactant was investigated. The efficiency of the surfactant mixture was investigated in the presence of different concentrations of magnesium chloride (MgCl 2 ) and calcium chloride (CaCl 2 ) and sodium chloride (NaCl). The efficiency of the prepared solutions for improving oil recovery was investigated through interfacial tension (IFT) and contact angle measurement, zeta potential measurements, micro-model experiments and core flooding experiments. Experimental results showed that critical micelle concentrations (CMC) value of CTAB surfactant and TR-880 is 350 and 1000 ppm, respectively. Experimental results showed that CTAB surfactant was more effective than the TR-880 in reducing IFT and contact angle values. In addition, the surfactant mixture prepared by CMC values of both surfactants was even more effective than the surfactants alone. In addition, optimum concentration of MgCl 2 and CaCl 2 was found to be 6000 ppm, however, MgCl 2 was more compatible with surfactant mixture than CaCl 2 in reducing IFT and contact angle. In this study, minimum IFT of 1.65 mN/m and contact angle of 49° were achieved at optimum MgCl 2 concentration of 6000 ppm and 32,000 ppm NaCl in the presence of surfactant mixture. In addition, the values were further reduced to 1.25 mN/m and 41° when all three salts (6000 ppm MgCl 2  + 6000 ppm CaCl 2  + 26,000 ppm NaCl) were used in the presence of CMC values of both surfactants. The secondary oil recovery factor results showed that a solution prepared by 6000 ppm of MgCl 2 and 32,000 ppm NaCl and CMC values of both surfactants resulted in maximum oil recovery factor of 76.36% in a micro-model set up. In addition, the maximum oil recovery factor of 79.70% was achieved during tertiary recovery in a core flooding system.

This study investigated a novel method for increasing desalinated water mass in solar desalination plants. For this purpose, solar panels and a cylindrical parabolic collector (CPC) were used to raise basin water temperature. The effect of different components of basin solar still on freshwater mass was also investigated. The aluminum basin has been associated with maximum water desalination among the different materials constituting a basin. The effects of different colors (e.g. black, brown, and red) on the basin, as well as different water depths (5, 10, and 15 mm), were also explored. The highest amount of freshwater in the black aluminum basin at a 5-mm water depth was 2.97 kg/day. ANN modeling was employed to validate the experimental data, indicating good compliance of experimental data with ANN prediction. According to the results of the simulation with varying numbers of neurons (n = 2–25), the highest and lowest agreement between experimental data and ANN prediction data were related to 24 and 10 neurons, respectively. Under optimum conditions, R2 and %AAD error were 0.993 and 2.654, respectively.

The activity of pharmaceutical substances crucially depends on the bioavailability of the substances. The bioavailability of drugs in body and their rate of dissolution in the biological fluids are increased if the particle size is decreased. In the present paper, the Gas Anti-Solvent (GAS) method was used to lower the size of rosuvastatin particles. The effects of temperature (313–338 K), pressure (105–180 bar) and initial solute concentration (20–60 mg/ml) were evaluated by Response Surface Methodology (RSM). The optimum initial solute concentration, temperature and pressure were found to be 20 mg/ml, 313 K and 180 bar, respectively which resulted in the minimum particle size. Furthermore, the particles were characterized by Differential Scanning Calorimetry (DSC), Dynamic Light Scattering (DLS), Fourier Transform Infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), and X-Ray Diffraction (XRD). The analyses showed that the rosuvastatin particles (60.3 nm) precipitated by GAS process become significantly smaller than the initial particles (45.8 µm).

A supercritical fluid extraction (SFE) method has been investigated to find the optimal conditions for selectively extracting free amino acids using carbon dioxide. This study examined three variables: temperature, pressure, and time. It was found that the SFE can selectively extract Alanine, Glycine, Cysteine, Histidine, and glutamic acid depending on the temperature and pressure of the extraction vessel. However, SFE showed a low extraction performance for other amino acids in the operating range studied. A design Expert was employed to generate a statistical model using the experimental data. The final optimization obtained the highest degree of selectivity for glutamic acid (P = 14 MPa T = 20 ºC t = 40 min), Glycine (P = 12 MPa T = 60 ºC t = 40 min), Histidine (P = 20 MPa T = 60 ºC t = 20 min), Alanine and serine (P = 20 MPa T = 60 ºC t = 60 min). This condition can be used to selectively extract each amino acid studied due to the striking difference between the optimal operating conditions of each category.