Compounds In Supercritical Carbon Dioxide Research Articles

UNIFAC and Wilson models to correlate solubility of pharmaceutical compounds in supercritical carbon dioxide

New thermodynamic correlations were proposed for the solubility of pharmaceutical compounds in supercritical carbon dioxide based on the solid-liquid equilibrium model. The activity coefficient models proposed in the study were based on Universal quasi-chemical Functional Group Activity Coefficient and Wilson models. The pharmaceutical drug compounds used in this study belong to anticancer, antibiotics, anti-androgenic, non-steroidal anti-inflammatory drugs, anti-prostatic tumour drugs and antiemetic drugs. The correlating ability of the proposed models was discussed in terms of various statistical parameters. Further, the proposed model results were compared with six existing solid-fluid equilibrium model results. It was found that the newly proposed models were more promising than those of existing solid-fluid equilibrium models. The correlating ability of the proposed models was discussed in terms of various statistical parameters.

Binary phase behavior of select organosulfur compounds in supercritical carbon dioxide: A Monte Carlo molecular simulation study.

Pressure-composition binary phase diagrams were determined for methanethiol, dimethyl sulfide, thiophene, benzothiophene, or dibenzothiophene with carbon dioxide at temperatures from 363 to 453K and pressures from 2 to 20 MPa. Utilizing Gibbs ensemble Monte Carlo molecular simulation, phase coexistence compositions were determined, along with the impact of 4% water cosolvent on select results. Solution structure as a function of pressure and temperature is characterized via radial distribution functions. Comparison to available experimental composition data gives overall mean absolute percentage deviations of 2.2% for thiophene, 37% for methanethiol, and 99% for benzothiophene. Solubilities in a CO2-rich phase are calculated to be sufficient to allow extraction and detection of the compounds studied here via supercritical fluid chromatography and mass spectrometry as a possible analysis approach for future Mars rover missions.

Experimental solubility of aripiprazole in supercritical carbon dioxide and modeling

The solubility of compounds in supercritical carbon dioxide (SC-{mathrm{CO}}_{2}) has found crucial significance in the fabrication of micro/nano-scaled drugs. In this research, the solubility of Aripiprazole was measured in SC-{mathrm{CO}}_{2} at various temperatures (308–338 K) and pressures (12–30 MPa). Moreover, the experimental solubility results were correlated with several semi-empirical models (Chrastil, Bartle et al., Kumar & Johnston, Menden-Santiago & Teja, Sodeifian et al., and Jouyban et al.) as well as the modified Wilson model. The molar fraction of the drug in SC-{mathrm{CO}}_{2} varied in the range of 1.830times {10}^{-6} to 1.036times {10}^{-5}. The solubility highly depended on the operating pressure and temperature. The Chrastil (0.994), Jouyban et al. (0.993) and Sodeifian et al. (0.992) models showed the highest consistency with the obtained values. Furthermore, self-consistency tests were performed on the solubility of Aripiprazole in SC-{mathrm{CO}}_{2}. The approximate total enthalpy ({mathrm{Delta H}}_{mathrm{total}}), vaporization enthalpy ({mathrm{Delta H}}_{mathrm{vap}}), and solubility enthalpy ({mathrm{Delta H}}_{mathrm{sol}}) were also calculated.

The transformation of cellulose acetate into a new biocidal polymer by effluent-free grafting in supercritical carbon dioxide

The study reports an effluent-free, green process for the production of materials with favorable antibiofilm properties by cellulose acetate grafting with quaternary ammonium compounds in supercritical carbon dioxide. Two quaternary ammonium compounds, N-(2-Hydroxyethyl)-N,N-dimethylundecan-1-aminium Bromide and N-(11-Hydroxyundecanyl-N,N-dimethyltetradecan- 1-aminium Bromide, were synthesized and chemically attached to cellulose acetate via hexamethylene diisocyanate as a linker under the conditions of 30 MPa and 70 °C. The polymer modification occurred in its whole volume. The chemical conversion led to a decrease in the degree of crystallinity and the appearance of a rugged polymer surface. However, the cross-section imaging of the newly obtained materials revealed a compact polymer structure. The functionalized materials acquired hydrophobic properties. Microbiological tests showed the impossibility of Staphylococcus aureus ATCC 29213, Methicillin-resistant Staphylococcus aureus ATCC 43300, Escherichia coli ATCC 10536, and Salmonella Enteritidis ATCC 13076 attachment to the material obtained by the cellulose acetate grafting procedure with N-(11-Hydroxyundecanyl-N,N-dimethyltetradecan- 1-aminium Bromide.

A new hybrid quantitative structure property relationships‐support vector regression (QSPR‐SVR) approach for predicting the solubility of drug compounds in supercritical carbon dioxide

AbstractThe purpose of this work was to compare the performance of 7 meta‐heuristics algorithms namely: Dragonfly (DA), Ant Lion (ALO), Grey Wolf (GWO), Artificial Bee Colony (ABC), Particle Swarm (PSO), Whale (WAO), and a hybrid Particle Swarm with Grey Wolf (HPSOGWO) optimizers in terms of fine‐tuning hyper‐parameters of a hybrid quantitative structure property relationships (QSPR)‐support vector regression (SVR) for the prediction of molar fraction solubilities of drug compounds in supercritical carbon dioxide (SC‐CO2). A dataset of 168 drug compounds, 13 inputs, and 4490 experimental data points was used to achieve the goal. All 7 models were statistically and graphically approved while the HPSOGWO‐SVR was found to over‐perform with an average absolute relative deviation (AARD) of 0.706% and an AIC of −14,434,249. The model was subjected to an external test (validation) using 160 experimental data points that were not used in the training and the test set. The overall results proved that the obtained model has good predictivity ability and robustness.

Solubility measurement and thermodynamic modeling of pantoprazole sodium sesquihydrate in supercritical carbon dioxide

Knowing the solubility data of pharmaceutical compounds in supercritical carbon dioxide (ScCO2) is essential for nanoparticles formation by using supercritical technology. In this work, solubility of solid pantoprazole sodium sesquihydrate in ScCO2 is determined and reported at 308, 318, 328 and 338 K and at pressures between 12 and 27 MPa. The solubilities are ranged between 0.0301 times 10–4 and 0.463 times 10–4 in mole fraction. The determined solubilities are modelled with a new model using solid–liquid equilibrium criteria and the required activity coefficient is developed using regular solution theory. The measured solubilities data are also modelled with three recent and four conventional empirical models. The recent models used are, Alwi-Garlapati (AARD = 13.1%), Sodeifian et al. (14.7%), and Tippana-Garlapati (15.5%) models and the conventional models used are Chrastil (17.54%), reformulated Chrastil (16.30%), Bartle (14.1%) and Mendenz Santiago and Teja (MT) (14.9%) models. The proposed model is correlating the data with less than 14.9% and 16.23% in terms of AARD for temperature dependent and independent cases. Among exiting models, Mendez Santiago and Teja (MT) and Alwi-Garlapati models correlate the data better than other models (corresponding AARD% and AICc are 14.9, 13.1 and −518.89, −504.14, respectively). The correlation effectiveness of the models is evaluated in terms of Corrected Akaike’s Information Criterion (AICc). Finally, enthalpy of solvation and vaporization of pantoprazole sodium sesquihydrate are calculated and reported. The new model proposed in this study can be used for the combination of any complex compound with any supercritical fluid.

QSPR Modelling of the Solubility of Drug and Drug-like Compounds in Supercritical Carbon Dioxide.

Quantitative structure-property relationship (QSPR) modeling was investigated to predict drug and drug-like compounds solubility in supercritical carbon dioxide. A dataset of 148 drug\drug-like compounds, accounting for 3971 experimental data points (EDPs), was collected and used for modelling the relationship between selected molecular descriptors and solubility fraction data achieved by a nonlinear approach (Artificial neural network, ANN) based on molecular descriptors. Experimental solubility data for a given drug were published as a function of temperature and pressure. In the present study, 11 significant PaDEL descriptors (AATS3v, MATS2e, GATS4c, GATS3v, GATS4e, GATS3 s, nBondsM, AVP-0, SHBd, MLogP, and MLFER_S), the temperature and the pressure were statistically proved to be sufficient inputs. The architecture of the optimized model was found to be {13,10,1}. Several statistical metrics, including average absolute relative deviation (AARD=3.7748 %), root mean square error (RMSE=0.5162), coefficient of correlation (r=0.9761), coefficient of determination (R2 =0.9528), and robustise (Q2 =0.9528) were used to validate the obtained model. The model was also subjected to an external test by using 143 EDPs. Sensitivity analysis and domain of application were examined. The overall results confirmed that the optimized ANN-QSPR model is suitable for the correlation and prediction of this property.

An extraction-based facile method for measuring the solubility of organic solid compounds in supercritical carbon dioxide

Due to its dual gas- and liquid-like properties, supercritical carbon dioxide (scCO2) is considered an excellent solvent for dissolving precursors and penetrating nanoscale pores. After subsequent calcination, uniformly dispersed metal or metal oxide nanoparticles or nanowires can be obtained. Organometallic salts are commonly used precursors in this method because they usually have high solubility in scCO2. Therefore, solubility data are essential for the controllable synthesis of such nanocomposites. This work establishes a facile and accessible method for measuring the solubility of organic solids in scCO2. The solubilities of copper acetylacetonate and cobalt acetylacetonate were obtained at temperatures of 313–333 K and pressures of 11–24 MPa. And the solubilities were within 2.3 × 10−2 to 14.1 × 10−2 g/L for Cu(acac)2 and 6.9 × 10−2 to 13.4 × 10−2 g/L for Co(acac)2. The experimental data were fitted to the semi-empirical model with the AARD values of 4.58% and 2.14% for Cu(acac)2 and Co(acac)2, respectively, and the obtained equations could be used to predict solubility of the same systems in the experimental range.

Computation of drug solvation free energy in supercritical CO[formula omitted]: Alternatives to all-atom computer simulations

Despite the modern level of development of computational chemistry methods and technological progress, fast and accurate determination of solvation free energy remains a huge problem for physical chemists. In this paper, we describe two computational schemes that can potentially solve this problem. We consider systems of poorly soluble drug compounds in supercritical carbon dioxide. Considering that the biggest contribution among all intermolecular interactions is made by van der Waals interactions, we model solute and solvent particles as coarse-grained ones interacting via the effective Lennard-Jones potential. The first proposed approach is based on the classical density functional theory and the second one relies on molecular dynamics simulation of the Lennard-Jones fluid. Sacrificing the precision of the molecular structure description while capturing the phase behavior of the fluid with sufficient accuracy, we propose computationally advantageous paths to obtaining the solvation free energy values with the accuracy satisfactory for engineering applications. The agreement reached between the results of such coarse-graining models and the experimental data indicates that the use of the all-atom molecular dynamic simulations for the studied systems seems to be excessive.

The solubility of Sulfabenzamide (an antibacterial drug) in supercritical carbon dioxide: Evaluation of a new thermodynamic model

Equilibrium solubilities of sulfabenzamide compound in supercritical carbon dioxide (ScCO2) have been explored at various pressures and temperatures. Solubilities measurements were carried using an equilibrium cell. Solubilities of sulfabenzamide reported were at four temperatures and at six pressures between (308 and 338) K and (12 and 27) MPa. The observed equilibrium solubilities were (1.53×10−6 to 6.15×10−6 at 308 K, 2.87×10−6 to 8.98×10−6 at 318 K, 6.15×10−6 to 13.61×10−6 at 328 K and 7.95×10−6 to 22.35×10−6 at 338 K). The measured saturation solubilities have been modeled with two new thermodynamic models based on solid–liquid equilibrium criteria. Moreover, Peng-Robinson’s equation of state (PREoS) with Van der Walls mixing rules (VdWmr) and few other empirical solubility models have been explored. PREoS as temperature independent and Sodefian et al., models are quite good in predicting the data. From the correlation results it is clear that PREoS as temperature independent is able to correlate the data below 8.59% (corresponding AICc is −660.82). Among empirical models Sodefian et al., model is able to correlate the data below 8.24% (corresponding AICc is −640.24).

Analysis of Nonvolatile Molecules in Supercritical Carbon Dioxide Using Proton-Transfer-Reaction Ionization Time-of-Flight Mass Spectrometry.

Proton-transfer-reaction (PTR) mass spectrometry (MS) is capable of detecting trace-level volatile organic compounds (VOCs) in gaseous samples in real time. Therefore, PTR-MS has become a popular method in many different study areas. Most of the currently reported PTR-MS applications are designed to determine volatile compounds. However, the method might be applicable for nonvolatile organic compound detection. Supercritical fluid chromatography (SFC) has been studied in the last 5 decades. This approach has high separation efficiency and predictable retention behavior, making separation optimization easy. Atmospheric ionization techniques, such as atmospheric chemical ionization (APCI) and electrospray ionization (ESI), are the most studied SFC-MS interfaces. These processes require the addition of makeup solvents to prevent precipitation or crystallization of the solute while depressurizing the mobile phase. In contrast, the PTR process is carried out in a vacuum; supercritical carbon dioxide may release solute into the PTR flow tube without a phase transition as long as it is maintained above a critical temperature. Therefore, this might constitute yet another use for the SFC-MS interface. Caffeine and a few other nonpolar compounds in supercritical carbon dioxide were successfully detected with time-of-flight MS without adding solvent by using preliminarily assembled supercritical flow injection and supercritical fluid extraction (SFE)-PTR interfaces.

Modelling of Solubility of Some Parabens in Supercritical Carbon Dioxide and New Correlations

In this work, new model correlations were established for the solubility of methyl, ethyl and n-propyl paraben compounds in supercritical carbon dioxide. These correlations constants were compared against the literature data (Asghari-Khiavi and Yamini in Chem Eng Data 48:61–65, 2003; Li et al. in Fluid Phase Equilb 264:93–98, 2008; Chengz et al. in Fluid Phase Equilib 201:79–96, 2002). The new correlations are significantly different and more accurate than the literature-reported correlations. A new expanded liquid model and a new simple density-based model were proposed and successfully used.

A new semi empirical model for the solubility of dyestuffs in supercritical carbon dioxide

Solubility determination and prediction of azobenzene and anthraquinone derivatives in supercritical carbon dioxide (ScCO2) is a challenging and tedious task. Therefore, proper prediction of these solutes are quite important for application of ScCO2 technologies to dyeing industries. In this work, a new semi-empirical model is developed to correlate industrially important dye compounds solubility in ScCO2. The proposed model successfully correlated solute solubility of 22 industrial dye compounds in supercritical carbon dioxide (ScCO2). The correlating ability of the new model is compared with 16 existing literature solubility models in terms of various statistical parameters (AARD, R2, adj. R2, SSE, and RMSE). Furthermore, Akaike’s information criterion was employed to evaluate the relative goodness of the model. We found that the proposed model is correlating better than existing models considered.

Synthesis of Polymer–Metal Composites by the Impregnation of Hypercrosslinked Polystyrene with Palladium Compounds in Supercritical Carbon Dioxide and Their Catalytic Activity in the Liquid-Phase Hydrogenation of Diphenylacetylene

Composites—palladium metal particles in a polymer matrix—are obtained by impregnation of hypercrosslinked polystyrene with palladium compounds in supercritical carbon dioxide (SC-CO2) followed by molecular hydrogen reduction. The synthesized composites exhibit a high level of catalytic activity in the liquid-phase hydrogenation of diphenylacetylene (DPA). The turnover frequency (TOF) is 15–50 min–1 and the selectivity in the formation of diphenylethylene is 60–80% at ~90% conversion of the substrate.

A new simple model for calculation of solubilities of derivatized anthraquinone compounds in supercritical carbon dioxide

A new modified Redlich–Kwong equations of state (βRK-EoS) is proposed to calculate the solubilities of eight dyeing compounds from anthraquinone family in supercritical CO2. In order to evaluate the results, the proposed model was also compared with the Peng–Robinson equation of state (PR-EoS) in combination with the van der Waals (vdW1) and Wong–Sandler (WS) mixing rules. The βRK–vdW0 model was found to deviate from the experimental results by 3.65%, whereas higher deviations were observed in PR–vdW1 and PR–WS models with providing AARDs of 11.44% and 6.8%, respectively. The modeling results show that the combination of βRK equation of state with vdW0 mixing rules, despite its simplicity, offers better results than other models and this combination can be used as a proper model to calculate the solubility of dye compounds in supercritical CO2.

Possibility of pressure crossover prediction by classical DFT for sparingly dissolved compounds in scCO2

A new method of solubility estimations, applied to sparingly dissolved compounds in supercritical carbon dioxide has been introduced in this paper. The method is based on determination of solubility contributions along the thermodynamic path consisting of sublimation and solvation processes. The contribution of the sublimation process is taken from the experiment, while the free energy of solvation is calculated from the classical density functional theory based on the fundamental measure theory. The parameterization of potential was performed using the Weeks-Chandler-Anderson procedure, where the Lennard-Jones parameters were obtained from the thermodynamics data of solute and solvent critical points. The introduced method can efficiently predict the pressure crossover on solubility curve of sparingly dissolved compounds in supercritical carbon dioxide.

Solubility of functional compounds in supercritical CO2: Data evaluation and modelling

The solubility data of functional compounds in supercritical carbon dioxide were compiled and discussed. The investigation included essential oils, phenolic compounds, carotenoids, tocopherols, carboxylic acids, alkaloids and vitamins. The data were correlated with four mathematical models. The parameters of each model were adjusted for each compound using the experimental data from literature. From the evaluation of the compiled data, it was possible to observe that the essential oils are the most soluble compounds and the phenolic compounds are the least soluble compounds in supercritical CO2, in the range of temperatures and pressures that were evaluated. In relation to the models, it was observed that the prediction of the solubilities of the biocompounds in supercritical fluids was acceptable for all the models, with the Jiang et al. equation being the model that presented the best results to predict the solubility for all functional groups.

A comprehensive comparison among four different approaches for predicting the solubility of pharmaceutical solid compounds in supercritical carbon dioxide

Supercritical technologies have been developed in the food, environmental, biochemical and pharmaceutical product processing during the recent decades. Obtaining accurate experimental solubilities of pharmaceutical compounds in supercritical carbon dioxide (SC-CO2) and their correlations are highly important and essential for the design of industrial operating units. In this study, the solubilities of six pharmaceutical compounds (Anti-HIV, Antiinflammatory and Anti-cancer) in SC-CO2 were correlated using four different models: cubic equation of state (EoS) model (SRK and modified-Pazuki EoSs), empirical and semi-empirical models (Chrastil, Mendez-Santiago-Teja, Spark et al. and Bian et al. models), regular solution model coupled with the Flory-Huggins equation, and an artificial neural network-based (ANN-based) model. In EoS calculations, twin-parametric van der Waals (vdW2) and Panagiotopoulos-Reid (mrPR) mixing rules were used for estimating the supercritical solution properties, with three different sets employed for obtaining critical and physicochemical properties of the solid compounds. To evaluate the capabilities of various approaches, a comprehensive comparison was carried out among the four models based on several statistical criteria, including AARD, Radj and F-value. Results of the analysis of variance (ANOVA) indicated that the ANN-based model provided the best results in terms of correlating the experimental solubility of the pharmaceutical compounds in SC-CO2.

A grey wolf optimizer-based support vector machine for the solubility of aromatic compounds in supercritical carbon dioxide

The prediction of solute solubility in supercritical carbon dioxide (SCCO2) is crucial for the development of supercritical applications. Many models have been developed to calculate the solubility of aromatic compounds. In this work, a grey wolf optimizer-based support vector machine (GWO-SVM) was proposed for correlating solute solubility in SCCO2. The proposed GWO-SVM model utilized the temperature, pressure and the density of SCCO2 as input parameters and the solubility of different solutes in SCCO2 as target parameter on the basis of gray correlation analysis. The new model successfully correlated solute solubility of 18 compounds (1148 data points including 814 training data points and 334 testing data points) in SCCO2, which were collected from the published literature. A comparison of the 27 commonly used empirical models and the proposed GWO-SVM model showed that the overall average absolute relative deviation of the proposed model is the lowest (3.20%). It was also found that the overall average absolute relative deviation is less dependent on material type for the proposed GWO-SVM model.

New models for correlating and predicting the solubility of some compounds in supercritical CO2

Two new semi-empirical models were proposed to calculate and predict the solubility data of some compounds in supercritical carbon dioxide (SCCO2). The new model I was constructed based on the models of Chrastil. Its ability of calculating solubility data was evaluated and compared with the models of Chrastil, Adachi-Lu and de Valle using solubility data of 40 compounds collected from literature published in recent years, and results showed that the new model I is better than other models. However, the new model I as well as other models mentioned above can only be fitted for each solute without the ability of prediction for the unknown solubility data. Meanwhile, the molecular connectivity indices (MCIs) have been widely used as the structure descriptors encoding a large amount of structure information on an entire molecule. Therefore, the new model II was developed by combining the modified MCIs with the new model I to calculate the solubility data of fatty acids, fatty alcohols, fatty acid esters and aromatic compounds in SCCO2. The relation between the solutes' solubility data and their molecular structures was developed by a one-off correlation of experimental data and then the inductive relation was used in the new model II to predict the solubility data of homogeneous solutes successfully.