Solubility In Supercritical Carbon Dioxide Research Articles

Experimental measurement and thermodynamic/intelligent modeling of yellow 2 G solubility in supercritical carbon dioxide with/without co-solvent

In this study, the solubility of an azo dye, Yellow 2 G, was investigated in supercritical CO2, both with and without co-solvents (methanol and dimethyl sulfoxide (DMSO)), spanning temperatures from 308 to 338 K and pressures from 10 to 34 MPa. The supercritical solubility of Yellow 2 G ranged from 0.052×10−6 to 0.559×10−6 mole fractions, while in the presence of methanol and DMSO, it varied from 0.5×10−6 to 5.92×10−6 and 0.46×10−6 to 4.6×10−6, respectively. Co-solvents significantly increased supercritical solubility, with over a 9-fold increase with methanol and more than a 7-fold increase with DMSO. Semi-empirical models, Peng-Robinson (PR), Soave Redlich Kwong (SRK), Perturbed-Chain Statistical Associating Fluid (PCP-SAFT), and quadrupolar cubic plus association theory (qCPA) equations of state, and a multilayer perceptron (MLP) neural network were used to assess the solubility data. Among the semi-empirical models, the Khansary model for the binary system and the Jouyban and Soltani-Mazloumi models for the ternary system demonstrated the highest conformity. Among models based on equations of state, the PCP-SAFT model demonstrates the highest accuracy in correlating solubility data for both binary and ternary systems. Notably, the accuracy of the SRK model in correlating solubility data for the binary system matches that of PCP-SAFT. The proposed MLP neural network provided highly accurate estimations of Yellow 2 G solubility in both binary and ternary systems, achieving an accuracy of over 99 %.

Experimental measurement and modeling of the solubility of fluorinated compounds derived from dichlone in supercritical carbon dioxide

Dichlone, also known as 2,3-dichloronaphthalene-1,4-dione, is a solid organic substance employed in the field of agriculture for its fungicidal properties and as a retardant for vegetable decomposition. The bioactive properties of dichlone can be enhanced by modifying its structure, specifically through the synthesis of new derivatives achieved by replacing the functional groups within its molecular structure. Two new solid dichlone derivatives were synthesized in this work, namely 2-chloro-3-((4-fluorobenzyl)amino)naphthalene-1,4-dione (dCl-2B-F) and 2-chloro-3-((4-fluorophenethyl)amino)naphthalene-1,4-dione (dCl-3 P-F) and measured their solubility in supercritical carbon dioxide at (313, 323, and 333) K and pressures between (9. to 32) MPa. The results indicated that solubility ranged between 30.5 and 47.9 µmol of solute/mol of CO2 for dCl-2B-F, and from 2.2 to 243.5 µmol of solute/mol of CO2 for dCl-3 P-F. The solubility data of dichlone and its synthesized derivatives (dCl-2B-F, dCl-3 P-F, 2-chloro-3-((4-chlorobenzyl)amino)naphthalene-1,4-dione (dCl-2B-Cl), 2-chloro-3-((4-chlorophenethyl)amino)naphthalene-1,4-dione (dCl-3 P-Cl), 2-(benzylamino)-3-chloronaphthalene-1,4-dione (dCl-2B) and 2-chloro-3-(phenethylamino)naphthalene-1,4-dione (dCl-3 P)) was compared using the density-based correlation of Chrastil and the Statistical Associating Fluid Theory of Variable Range Mie-potential (SAFT-VR Mie) equation of state (EoS), to better comprehend the effects of the structural differences on the solubility. As a result, for the Chrastil model, a root mean square deviation (rmsd) of 3% was obtained for dCl-2B-F and 16% for dCl-3 P-F, whereas for the SAFT-VR Mie equation, it averaged 24% for dCl-2B-F and 28% for dCl-3 P-F. It was found that the solubility of the homologous compounds, differing only in one methylene group, increased with solute size (-2B derivatives were less soluble in CO2 than the −3 P ones), contrary to the expected trend, which could be attributed to the increased probability of ring-to-ring interactions as the chain length connecting the rings decreases. This demonstrates that geometric factors, along with the pressure and temperature, affect the behavior of the solubility and these should be accurately represented in the predictive models.

Measurement and Modeling of Zolpidem Solubility in Supercritical Carbon Dioxide: Effect of Two Cosolvents

Measurement and Modeling of Zolpidem Solubility in Supercritical Carbon Dioxide: Effect of Two Cosolvents

Thermodynamic Modeling of Solubility of Some Antibiotics in Supercritical Carbon Dioxide Using Simplified Equation of State Approach

Antibiotics are playing crucial role in the treatment of humans since the last few centuries. Their usage has several benefits along with side effects. The efficacy of antibiotics for the treatment of ailments may be retained by controlling the drug dosage. This may be achieved with supercritical fluid technology (SFT). The antibiotic drug solubility in supercritical carbon dioxide (scCO2) is available only at specific temperature and pressure conditions, for effective utilization of SFT, solubility at various conditions are required. Equation of state (EoS) method is used for solubility data modeling and it requires critical properties of the solute, molar volume of the solute and sublimation pressure of the solute along with fugacity coefficient, pressure and temperature. These properties are estimated using group contribution methods. For antibiotics solute critical properties, molar volume and sublimation pressure are unavailable and existing group contribution methods are also not applicable due to the lack of functional group contributions in their techniques. Thus, there is a need to address EoS methodology without using solute properties. Hence, a new EoS methodology for solubility modeling is, proposed without using critical properties of the solute, molar volume of the solute and vapour pressure of the solute. Thus, this study focuses on the development of new solubility model that correlates antibiotics using equation of state (EoS). Importantly, the proposed solubility model does not use the critical properties of the antibiotics. Correlating ability of the proposed model is indicated in terms of regression coefficient and arithmetic average relative deviation percentage (AARD %).

Biodegradable polysaccharide aerogels based on tragacanth and alginate as novel drug delivery systems

Tragacanth, an anionic polysaccharide, is a natural material widely investigated for the synthesis of aerogels as drug delivery vehicles. Its biocompatibility, biodegradability, and affordability are all key features for its use in pharmaceutical applications. In this study, tragacanth and tragacanth alginate composite aerogels were prepared using the sol-gel technique followed by supercritical drying. Paracetamol was selected as a model drug for drug loading and release studies owing to its high solubility in ethanol and low solubility in supercritical carbon dioxide. The paracetamol loading into the aerogel pores was confirmed by infrared spectroscopy (IR) and x-ray diffraction (XRD) spectra of the resulting samples. Scanning electron microscopy (SEM) images showed that all aerogels were porous with a macroporous-mesoporous network. Due to the high porosity of the prepared aerogels, a loading of 99 wt% (mg drug/mg aerogel) for tragacanth and 114 wt% (mg drug/mg aerogel) for composite aerogels was achieved. Moreover, the release rate of the drug could be modified by manipulating the aerogel composition. Tragacanth aerogels had a faster release rate, while the addition of alginate prolonged the release rate of the model drug. Various empirical release models were investigated and the release rate was found to follow the Korsmeyer-Peppas (Power Law) model suggesting a diffusion-based release kinetics. Based on the results, the feasibility of utilizing tragacanth for the preparation of drug-loaded aerogels was shown.Graphical

Prediction of Drug-like Compounds Solubility in Supercritical Carbon Dioxide: A Comparative Study between Classical Density Functional Theory and Machine Learning Approaches

Prediction of Drug-like Compounds Solubility in Supercritical Carbon Dioxide: A Comparative Study between Classical Density Functional Theory and Machine Learning Approaches

Application of Near-Infrared Spectroscopy for Screening of Chlorothiazide Sodium Vials.

Chlorothiazide sodium for injection, USP, is a diuretic and antihypertensive medication in the form of a white or practically white, sterile, lyophilized powder. Each vial contains 500 mg of chlorothiazide sodium, equivalent to 500 mg of chlorothiazide, and 250 mg of mannitol as an inactive ingredient. The pH is adjusted with sodium hydroxide. Chlorothiazide sodium has a molecular weight of 317.71 amu. Since 2020 there have been multiple national shortages of chlorothiazide. Recent studies target chlorothiazide's low bioavailability, aiming to enhance it through nanoparticle production via a supercritical method. The drug's solubility in supercritical carbon dioxide (scCO2) is vital, with measurements ranging from 0.417×10-5 to 1.012×10-5 mole fraction under specific conditions. Adding co-solvents, like ethanol, DMSO, and acetone, to scCO2 boosts solubility, with ethanol proving most effective, enhancing solubility by 2.02-11.75 times. Intra-lot variability was discovered in a sample of a lot of chlorothiazide sodium by the University of Kentucky Drug Quality Task Force. Two vials of six screened in one lot were displaced from the center of the lot by 4.0 and 4.2 SDs, respectively. Inter-lot variability was confirmed in the near-IR spectra of 204 vials obtained from 28 different lots of chlorothiazide sodium. Using full spectrum BEST analysis 13 vials (6.4%) were outliers.

Modeling solid solute solubility in supercritical carbon dioxide by machine learning algorithms using molecular sigma profiles

Supercritical carbon dioxide (ScCO2) is a solvent used in industries due to its non-toxic, non-flammable nature and unique liquid–gas properties. To predict the solubility of different solid solutes in ScCO2 for extraction or pharmaceutical process design, machine learning (ML) has been widely used to correlate the thermodynamics parameters with solubility. This work presents an investigation of the solubility of 116 drug-like solutes (comprising 2791 data points) in ScCO2 using machine learning models. Deep neural network (DNN) and random forest (RF) methods were employed with temperature, pressure, CO2 density, molecular weight, and sigma profile (σ-profile) as input features for predicting solubility. Results indicate strong ML model capabilities with R2 and ALD-x values of DNN being 0.9911 and 0.090 and those of RF being 0.9892 and 0.087, though DNN showed slightly higher errors. The effect of reducing input features of σ-profile on prediction accuracy revealed a negligible increase in error but maintained overall performance. This demonstrates that using a reduced number of σ-profile features offers practical estimation with limited computational resources. This work shows the robust potential of machine learning in accurately predicting the solubility of solids in ScCO2 using σ-profiles.

Solubility measurement of Triamcinolone acetonide (steroid medication) in supercritical CO2: Experimental and thermodynamic modeling

Triamcinolone Acetonide (TA, Steroid Medication) solubility in supercritical carbon dioxide (ScCO2) was determined for the first time at 308, 318, 328, and 338 K and pressures of 120–270 bar. The results showed that mole fractions of TA dissolved in ScCO2 ranged from 6.90 × 10−6 to 2.13 × 10−4, and the solubility ranged from 0.052 to 1.650 g/L. Four different types of models were examined; (1) seven known density-based models, with 3–6 adjustable parameters, namely Sung-Shim, Chrastil, Kumar–J and Bian et al., MST, Bartle, and Sodeifian; (2) Equation of state (EoS) based model, specifically Peng-Robinson (PR) EoS, coupled with two conventional types of van der Waals mixing rules; (3) a Solid-Liquid Equilibrium (SLE) based model, developed by Soedifian et al.,; (4) several machine learning-based models, namely Ridge Regression, Huber Regression, Random Forest, and Gaussian Process. Based on the results, all applied models accurately described TA solubility in ScCO2, although Sodeifian et al. model led to superior results based on AARD% and Kumar–Johnston model based on Bayesian Information Criterion (BIC). Finally, thermodynamic enthalpies of TA were estimated for the first time.

Experimental study and thermodynamic modeling of clonazepam solubility in supercritical carbon dioxide

Experimental study and thermodynamic modeling of clonazepam solubility in supercritical carbon dioxide

A machine learning technique based on group contributions to calculate the solubility of dye molecules in supercritical CO2

A new modeling approach is proposed for the estimation of dye solubility in supercritical carbon dioxide. This approach is based on the synergy obtained from a group contribution approach and an artificial neural network. 33 functional groups have been proposed to create the dye molecules, and a database containing 1917 experimental solubility points of 66 molecules was utilized to establish the best architecture of the artificial neural network. The results showed that this new approach could calculate the dye solubility in this supercritical fluid with modeling errors lower than 10 % under different pressure and temperature conditions. Specifically, it showed a competitive performance with R2 > 0.99, AARDlny = 0.91 ± 1.2 % and AARDy = 10.4 ± 17.9 %. The minimum and maximum values of ARDlny and ARDy were 4.0E-04 – 16.8 % and 5.0E-03 – 386 %. This contribution group – artificial neural network approach outperformed classical empirical and semi-empirical equations, and other surrogate models based on artificial intelligence, even those using more detailed information of tested compounds (e.g., molecular descriptors that should be obtained from computational chemistry calculations). It is an interesting option to be incorporated into process simulators to improve the process design of dyeing technologies using supercritical carbon dioxide.

Solubility in supercritical carbon dioxide of two novel amine derivatives of 2,3-dichloronaphthalene-1,4-dione (dichlone)

Dichlone (2,3-dichloronaphthalene-1,4-dione) is an important antimicrobial agent for agriculture, which effectiveness could be improved by modifying its structure, while the recovery of high-purity synthesized derivatives from a reaction mixture could be accomplished by extracting them with supercritical carbon dioxide. Two new amine derivatives, 2-chloro-3-((4-chlorobenzyl)amino)naphthalene-1,4-dione (dCl-2B-Cl) and 2-chloro-3-((4-chlorophenethyl)amino)naphthalene-1,4-dione (dCl‐3P‐Cl), were synthesized from dichlone, and their solubility in supercritical carbon dioxide was measured afterwards at (313, 323 and 333) K and a pressure range from (8−33) MPa. Experimental solubilities spanned from (10.3·10-6 to 22.1·10-6) mol·mol-1 for dCl-2B-Cl, and from (32.7·10-6 to 131·10-6) mol·mol-1 for dCl‐3P‐Cl. The solubility data of the dichlone family (dichlone, dCl-2B-Cl, dCl‐3P‐Cl, 2‐(benzylamino)‐3‐chloronaphthalene‐1,4‐dione (dCl-2B) and 2-chloro-3-(phenethylamino)naphthalene-1,4-dione (dCl‐3P)) was compared using three models, i.e., the Chrastil equation, the Molecular Connectivity Indices model, and the Statistical Associating Fluid Theory of Variable Range and Mie Potential equation of state, to identify the quantitative structure-property relationship between them. Solubility had an inverse relation with solute size and polarity, but there were some exceptions that could be explained by performing a stereochemical analysis, which showed that steric effects involved in the folding of dCl‐3P and dCl‐3P‐Cl provided them a better geometry for solvation than dCl-2B and dCl-2B-Cl, respectively, making them more soluble. This demonstrates that the solute geometry is an important factor in the solvation process, and it must be represented accurately to develop better predictive models.

Cubic plus association (CPA/qCPA) and classical thermodynamic modeling of pharmaceuticals solubility in supercritical carbon dioxide

Cubic plus association (CPA/qCPA) and classical thermodynamic modeling of pharmaceuticals solubility in supercritical carbon dioxide

Development of computational intelligence models for assessment of drug nanonization using green chemistry technique: Improvement of drug solubility

Determination of solubility via theoretical approaches was carried out in this study. Because of its importance to the expansion of the pharmaceutical industry, this study models Lenalidomide solubility in supercritical carbon dioxide using multiple tree-based techniques which are of machine learning nature. These parameters are molded based on temperature and pressure input features due to the significant variability of drug solubility with the temperature and pressure. The experimental data have been collected and inputted the models to train them and used the data for testing the machine learning models. The results are useful for production of nanomedicine with enhanced solubility in solvents. Decision Tree (DT), Extra Trees (ET), and Gradient Boosting (GB) models are used and optimized using SCA algorithm to obtain more robust models for prediction of the drug solubility in the solvent. So, the developed models are called SCA-DT, SCA-ET, and SCA-GB in this study and have R2-scores of 0.932, 0.951, and 0.997, respectively. The SCA-DT model has an RMSE error rate of 0.0948, this rate is 0.0822 for SCA-ET, and 0.0203 for SCA-GB. So, the SCA-GB is introduced as the best model of this research for prediction of Lenalidomide solubility in the solvent.

Measurement of metoprolol solubility in supercritical carbon dioxide; experimental and modeling study

Metoprolol is a beta-blocker agent, widely used to treat hypertension, and some heart diseases. However, due to low bioavailability and short half-life of this drug, the dose and the frequency of its daily use are high, causes to some digestive problems in patients. Designing a new drug delivery system with high efficiency and controlled drug release using a suitable supercritical method can solve these problems. To reach this goal, the solubility of Metoprolol in supercritical CO2 (scCO2) should be known. Here, solubility of Metoprolol in supercritical CO2 (scCO2) was measured at the pressure and temperature range of 120–300 bar and 308–338 K, respectively. It was determined between the mole fractions of 0.02 × 10−5 to 8.11 × 10−5. Also, obtained values were correlated via some well-known empirical models suggested through Kumar- Johnston (K-J), Sung and Shim, Adachi and Lu, Sparks, Bartle, Hozhabr, Khansary, and Jouyban. The empirical model proposed by Adachi and Lu shows the highest correlation precision, with the minimum AARD% of 13.07. Furthermore, the total mixing heat, vaporization enthalpy, and solvation enthalpy of the Metoprolol-scCO2 binary mixture were obtained as about 107.25 kJ/mol, 115.56 kJ/mol, and −8.31 kJ/mol, respectively.

Supercritical carbon dioxide solubility measurement and modelling for effective size reduction of nifedipine particles for transdermal application

Nifedipine (NIF) is a Class II drug of the Biopharmaceutical Classification System (BCS) with low oral bioavailability, low dissolution rate and significant hepatic drug metabolism. The transdermal route using supersaturated systems could be considered. For this purpose, physicochemical properties of NIF such as its dissolution rate, may be a limiting factor and must be improved. Crystallization processes assisted by supercritical carbon dioxide (scCO2) and particularly the Rapid Expansion of Supercritical Solution (RESS) process may improve drug bioavailability by reducing particle size and consequently increasing surface area. This study addresses the reduction of NIF particle size using scCO2-RESS as crystallization process. Experimental solubility studies were performed at different temperature (308 and 318 K) and pressure ranges (9–24 MPa). Solubility data were correlated with two thermodynamic models in order to predict NIF solubility in scCO2. Optimized operating conditions, identified by thermodynamic modelling, allowed the production of thinner NIF particles and a size reduction up to ten fold. Particle size reduction improved NIF dissolution kinetics in aqueous medium: after 90 min, 42 % of raw NIF was released against 80 % for crystallized NIF. The scCO2-RESS process is a solvent free process, that can produce micronized or nanosized crystals able to improve physicochemical properties of poorly water-soluble drugs.

Theoretical and experimental study on Chloroquine drug solubility in supercritical carbon dioxide via the thermodynamic, multi-layer perceptron neural network (MLPNN), and molecular modeling

The design and development of supercritical carbon dioxide (sc-CO2) based processes for production of pharmaceutical micro/nanoparticles is one of the interesting research topics of pharmaceutical industries owing to its attractive advantages. The solubility of drugs in sc-CO2 at different temperatures and pressures is an essential parameter which should be determined for this purpose. Chloroquine as a traditional antirheumatic and antimalarial agent is approved as an effective drug for the treatment of Covid-19. Pishnamazi et al. (2021) measured the solubility of this drug in sc-CO2 at the pressure range of 120–400 bar and temperature range of 308–338 K, and correlated the obtained data using some empirical models. In this work, a comprehensive computational approach was developed to more accurately study the supercritical solubility of Chloroquine. The thermodynamic models include two equations-of-state based models (Peng-Robinson and Soave-Redlich-Kowang) and two activity coefficient-based models (modified Wilson's and UNIQUAC)), as well as, a multi-layer perceptron neural network (MLPNN)) were used for this purpose. Also, molecular modeling was performed to study the electronic structure of Chloroquine and identify the potential centers of intermolecular interactions during the dissolution process. According to the obtained results, all of the theoretical models can predict Chloroquine solubility in sc-CO2 with acceptable accuracy. Among these models, the MLPNN model possesses the highest precision with the lowest average absolute relative deviation (AARD%) of 1.76 % and the highest Radj value of 0.999.

Experimental solubility and modeling of Crizotinib (anti-cancer medication) in supercritical carbon dioxide

Measurement of saturation solubility of drugs in a supercritical fluid is an important parameter for the implementation of supercritical technology in pharmaceutical industry. CO2 is the most sorted substance as a supercritical fluid since it has attractive properties like easily achievable critical temperature, moderate pressure. Cancer is increasingly affecting the mankind, a proper dosage while treating would help in minimizing the drug usage. The bioavailability of the drug is mainly influenced by the drug particle size. An appropriate technology is always useful in making suitable drug particles; thus, supercritical fluid technology (SFT) is considered as promising technique for the production of micro and nanoparticles. Since, particle production process through SFT needs solubility information, appropriate solubility information is necessary. In the present work, Crizotinib (anti-cancer drug) solubility in supercritical carbon dioxide (scCO2) is measured and reported, for the first time. The obtained solubilities are at temperatures 308, 318, 328,338 K and pressures 12, 15, 18, 21, 24 to 27 MPa. The measured solubilities are ranged in terms of mole fraction from (0.483 × 10−5 to 0.791 × 10−5) at 308 K, (0.315 × 10−5 to 0.958 × 10−5) at 318 K, (0.26 × 10−5 to 1.057 × 10−5) at 328 K, (0.156 × 10−5 to 1.219 × 10−5) at 338 K. The cross over region is observed at 14.5 MPa. To expand the application of the solubility data, few important solubility models and three cubic equations of sate (cubic EoS) models along with Kwak and Mansoori mixing rules are investigated. Sublimation and salvation enthalpies of Crizotinib dissolution in scCO2 are calculated.

Laboratory determination and thermodynamic analysis of alendronate solubility in supercritical carbon dioxide

Alendronic acid, with the commercial name of Alendronate, is a bisphosphonate medicine which is generally used orally to treat the osteoporosis and Paget's disease of bones. However, its absorption is poor, and its oral bioavailability is <1%. So, development of novel delivery systems to enhance its bioavailability and reduce its side effects is essential.The design a modern drug delivery systems, through supercritical carbon dioxide (scCO2) as a green solvent, which is known as an efficient solution to increase the drug bioavailability, reduce toxicity and side effects. Solubility of pharmaceutical substances in scCO2 is an essential parameter that must be determined for this purpose.Here, solubility of Alendronate was experimentally studied in the span of 0.01 × 10−4 to 1.5 × 10−4 (equilibrium mole fraction), at 308–338 K and 120–300 bar. Also, obtained experimental values were correlated by two fugacity coefficient models (PR-EoS and SRK-EoS), and two activity coefficient models (modified Wilson and UNIQUAC). All these models could successfully correlate the Alderonate solubility values. Meanwhile, the solubility data obtained from the SRK-EoS (AARD% = 10.11) and modified Wilson’s model (AARD% = 11.52) are more accurately fitted to the experimental data.

Exploring the relationship between food-grade wax solubility in supercritical carbon dioxide and resulting hydrophobic development of impregnated paper substrates

Alkyl ketene dimer (AKD) and vegetable wax (VW) are suitable contenders for supercritical impregnation (SCI) processes, where the waxes may be deposited onto paper fiber surfaces to improve their hydrophobicity for food packaging applications. The solubility of both waxes in supercritical carbon dioxide (scCO2) with n-heptane cosolvent was measured (40 – 60 oC and 10 – 18 MPa). VW was two orders of magnitude more soluble in scCO2 compared with AKD, and both sets of data were validated using the Chrastil empirical equation. SCI of AKD wax into paper matrixes was performed at pre-determined solubility conditions, and the resulting hydrophobicity was measured. Results demonstrated that annealing the samples (4 h, 140, 160, 180 oC) immediately following impregnation generally enabled a more rapid development of the CA over a 2-week period. Crucially, using impregnation temperatures and pressures that maximized AKD solubility in scCO2 resulted in CA up to 130o. Gravimetric analysis of the impregnated samples confirmed the expected deposited wax quantities to within 25% of the solubility quantities. Scanning electron microscopy (SEM) imaging revealed better distribution of AKD in annealed samples and partially confirmed that more intricate structures were responsible for higher measured CA. These results explicitly connect the intuitive relationship that higher solute solubility in scCO2 should lead to bulk property improvements (i.e. higher CA or hydrophobicity) after SCI. Continued research in this area will enable the development of metrics, linking solubility conditions to desired bulk material properties, and is expected to lead to innovative packaging of green materials and processes.