Supercritical Solvent Research Articles

Experimental Study of the Accumulation Mechanism of Styrene in Activated Carbon with the Aid of Supercritical Solvent Extraction

Experimental Study of the Accumulation Mechanism of Styrene in Activated Carbon with the Aid of Supercritical Solvent Extraction

Development, characterization, and comparison of chitosan microparticles as a carrier system for black bean protein hydrolysates with antioxidant capacity.

Peptides in black bean protein hydrolysates (BPHs) exert antioxidant capacity. However, peptides are prone to degradation during processing and digestion. Chitosan (Ch) can protect them and provide a delayed release. This work develops and compares two drying methods producing porous structured Ch microparticles (MPs) as carriers for antioxidant BPH. Ch gels were obtained by ionic gelation and dried by supercritical CO2 solvent displacement or fast-freeze-drying methods. The resulting aerogels and fast-freeze-dried MPs were structurally characterized, and their swelling and release profiles were obtained at pH 1.2 and 7.4. The antioxidant capacity of systems was determined by 2,2'-azino-bis(3-ethyl-benzthiazoline-6-sulphonic acid) (ABTS) and superoxide radical assays. The results showed BPH-Ch best complexation conditions occurring at a pH of 4.5 and a 4:1 BPH/Ch ratio. The particle size of the complex was 1047.6nm, and the entrapment efficiency and loading capacity were 28.2% and 54.3%, respectively. At pH 1.2 and 7.4, the release rate of BPH was lower in aerogel than in fast-freeze-dried MPs. Besides, entrapment BPH in Ch significantly reduced the ABTS antioxidant activity IC50 from 35.1µM Trolox equivalents (TE)/mg to 250.7 and 406.2µM TE/mg for Ch fast-freeze-dried and aerogels, respectively. Superoxide radical inhibition IC50 ranged from 74.6 to 92.9mM ascorbic acid equivalents/mg in the different samples. BPH-loaded aerogels presented lower specific surface area (94.7 vs. 138.6m2/g, p<0.05) and higher average pore size (26.4 vs. 19.8nm) than Ch aerogels. Ch aerogel is a promising carrier for delaying the release of common bean antioxidant peptides useful for developing functional foods. PRACTICAL APPLICATION: This novel system could act as an ingredient to incorporate antioxidant compounds in different formats to develop delayed-release nutraceuticals and functional foods, such as bakery, dairy products, or beverages. Along, antioxidant peptide-loaded aerogels could be used as a slow-release system for compounds acting as natural preserving antioxidants for food applications such as raw meat products or high-fat foods.

Supercritical CO2 green solvent extraction of Nepeta crispa: Evaluation of process optimization, chemical analysis, and biological activity

This research investigates the extraction of compounds from Nepeta crispa (N. crispa), utilizing supercritical carbon dioxide green solvent, for the first time. Using RSM, the study examines the impact of pressure (15-25MPa), temperature (313-333K), and co-solvent (0.5- 3.5%) on the extraction yield. The antioxidant activity of the essential oil is assessed through Ferric Reducing Antioxidant Power (FRAP) assay, while its antibacterial properties are evaluated against four bacterial strains. The SCFE method achieved a maximum yield of 1.812% at optimum conditions (P= 25MPa, T= 313K and co-solvent= 3.5%). GC-Mass analysis revealed that the primary constituents of N. crispa essential oil which are 1,8-cineol and 4aα,7α,7aα-nepetalactone in both extraction methods. The antioxidant and antibacterial results of N. crispa showed the superiority of SC-CO2 over hydrodistillation method. These observed antioxidant and antibacterial attributes highlight the plant’s potential suitability for applications within the food and pharmaceutical industries.

Assessment of Solubility Behavior of a Copolymer in Supercritical CO2 and Organic Solvents: Neural Network Prediction and Statistical Analysis.

In the industrial sector, understanding the behavior of block copolymers in supercritical solvents is crucial. While qualitative agreement with polymer solubility curves has been evaluated using complex theoretical models in many cases, quantitative predictions remain challenging. This study aimed to create a rapid and accurate artificial neural network (ANN) model to predict the lower critical solubility and upper critical solubility space of an atypical block copolymer, poly(styrene-co-octafluoropentyl methacrylate) (PSOM), in different supercritical solvent systems over a wide range of temperatures (51.75-182.05 °C) and high pressure (3.28-200.86 MPa). The experimental data set used in this study included one copolymer, five supercritical solvents, one cosolvent, and one initiator. It consisted of seven unique copolymer-solvent combinations (252 cloud point pressures) used to predict the model quantitatively and qualitatively. To predict the PSOM-solvent interactions, the study considered two different input systems: a six-variable system, a five-variable system, and one target output. Initially, we used a three-layer feed-forward neural network to select the best learning algorithm (Levenberg-Marquardt) from 14 different algorithms, considering one sample PSOM-solvent system. Then, the network topology was optimized by varying hidden neuron numbers from 2 to 80 for all seven PSOM-solvent combination systems. The predicted cloud point pressures were in excellent agreement with the experimental cloud point pressures, confirming the model's accuracy. It is clear from the results of a minimum mean square error (≤1.90 × 10-27) and maximum linear regression R 2 (≥0.99) during training, validation, and testing of all the data sets. Further, the ANN model accuracy was tested by statistical analysis, confirming the model's ability to accurately capture the miscibility regions of polymers, enabling efficient processing of various polymer materials. This data-driven approach facilitates the prediction of coexistence curves for other polymers and complex macromolecular systems.

Intelligence computational analysis of letrozole solubility in supercritical solvent via machine learning models

Supercritical fluids (SCFs) can be used to prepare drugs nanoparticles with improved solubility. SCFs have shown superior advantages in pharmaceutical industry as an environmentally friendly alternative to toxic/harmful organic solvents. They possess gas-like transport characteristics and liquid-like solvation power for solutes. Evaluation of chemotherapeutic drugs’ solubility in supercritical carbon dioxide (SCCO2) has been recently an attractive subject for developing this method in pharmaceutical sector. To reach this purpose, the utilization of accurate models is of great necessity to estimate experimental-based solubility data. In this paper, the authors tried to employ machine learning (ML) approaches to estimate the solubility of Letrozole (LET) drug as chemotherapeutic agent and correlate its values in wide ranges of temperature and pressure. To do this, PAR (Passive Aggressive Regression), RF (Random Forest), and RBF-SVM are the models used (Support Vector Machine with RBF kernel). These models optimized in terms of their hyper-parameters using GA algorithm. The optimized PAR, RF, RBF-SVM models obtained coefficients of determination (R-squared) of 0.8277, 0.9534, and 0.9947. Also, the MSE error rate of the models are 0.1342, 0.0305, and 0.0045, in the same order. The final result of the evaluations shows the optimized RBF-SVM model as the most appropriate model in this research. The model exhibits a maximum prediction error of 0.1289.

Reversible Addition-Fragmentation Chain-Transfer Polymerization in Supercritical CO2: A Review.

The development of cleaner, more environmentally friendly processes in polymerization technology is crucial due to the prevalent use of volatile organic solvents (VOCs), which are harmful and toxic. Future regulations are likely to limit or ban VOCs. This review explores the use of supercritical solvents, specifically supercritical CO2 (scCO2), in polymerization processes. The study focuses on reversible addition-fragmentation chain-transfer (RAFT) induced homo-polymerization of various monomers using specific chain transfer agents (CTAs) in scCO2. RAFT polymerization, a reversible deactivation radical polymerization (RDRP) polymerization, relies heavily on the choice of CTA, which significantly influences the dispersity and molar mass of the resulting polymers. Stabilizers are also crucial in controlling product specifications for polymerizations in supercritical CO2, except for fluor-based polymers, although they must be removed and preferably recycled to ensure product purity and sustainability. The review notes that achieving high molar mass through RAFT polymerization in scCO2 is challenging due to solubility limits, which lead to polymer precipitation. Despite this, RAFT polymerization in scCO2 shows promise for sustainable, circular production of low molar mass polymers, although these cannot yet be fully considered green products.

Functionalization of chitosan films using essential oils by supercritical CO2 impregnation method for various applications

Herein, chitosan films obtained by different approaches were functionalized with essential oils, i.e. mentha, clove, and cinnamon, by Supercritical Solvent Impregnation method to impart antioxidant and antimicrobial activity. The films were prepared from solutions with two different polymer concentrations (1 and 2.5 wt%) following three methodologies: casting and then drying at air (25 °C, 39 % relative humidity), casting and drying at 60 °C in humid conditions (82 % relativity humidity) and casting and water elimination by a freeze-drying process. The films were, then, characterized by scanning electron microscopy and X-ray diffraction, proving that the thickness, porosity, and crystallinity of the films are highly dependent on the preparation method. These structural variations affected the impregnation of essential oils by supercritical fluids, in terms of the amount and composition of the active compounds. Subsequently, yellowness, antioxidant and antimicrobial properties of the impregnated films were evaluated. The results demonstrated that the impregnated films retained high concentration of essential oils, especially the films impregnated with clove and produced by the freeze-drying process due to their higher porosity and lower crystallinity. These films also exhibited superior antimicrobial activity against Gram-positive and Gram-negative bacteria in comparison with the other two analyzed preparation methods.

Artificial intelligence for computation and development of nanodrug solubility in supercritical solvent: Analysis of temperature and pressure influence

This paper presents an investigation into the modeling and prediction of the density of supercritical carbon dioxide (SC-CO2) and mole fraction of Erlotinib hydrochloride using machine learning techniques. The dataset consists of temperature and pressure as input variables, with density of SC-CO2 and mole fraction of Erlotinib hydrochloride as output variables. The process is supercritical-based operation which can be used for production of nanomedicines with improved aqueous solubility. Three models, Multilayer Perceptron (MLP), Orthogonal Matching Pursuit (OMP), and Extreme Gradient Boosting (XGB) were trained and optimized using the Firefly Optimization Algorithm (FA). The models’ performance in correlating solubility and density were evaluated via statistical analyses to measure their accuracy. The MLP model achieved high accuracy, with scores of 0.99714 and 0.97749 for density and mole fraction prediction, respectively in term of coefficient of determination (R2). The OMP and XGB models also provided reasonably accurate results. These findings demonstrate the effectiveness of machine learning in understanding and predicting SC-CO2 behavior, with potential applications in industrial processes and pharmaceutical formulations.

Composite nanocapsules of phase change materials using a supercritical carbon dioxide (SC‐CO2) assisted process

AbstractThis work reports the production of polymeric nanocapsules of polymethyl methacrylate (PMMA)/phase change materials (PCM), using continuous supercritical emulsion extraction (SEE‐C). Five fatty acids (FAs) were tested: capric (CA), lauric (LA), myristic (MA), palmitic (PA), and stearic (SA) acid, using supercritical carbon dioxide (SC‐CO2) operating at 80 bar and 38°C in a tower apparatus. The two fatty acids with lower molecular weight (CA and LA) were extracted by the supercritical solvent and capsules were not obtained. The other three FAs formed spherical, non‐coalescing nanocapsules characterized by mean diameters ranging between 134 and 252 nm, as shown by scanning electron microscope (SEM) images and dynamic light scattering (DLS) analysis, with a sharp particles size distribution and encapsulation efficiencies up to 99.8%. Differential scanning calorimetric analysis (DSC), thermogravimetric analysis (TGA) and derivative thermogravimetric analysis (DTG) analyses confirmed the successful encapsulation, allowing the measurement of the energy storage properties of produced capsules. Stability analysis performed over 5 months showed that the nanocapsules were stable in this time interval. Thermal cycles experiments confirmed the thermal stability of the capsules. The best performance was obtained for PA based nanocapsules, which showed a stability reduction of only 0.43% after 25 thermal cycles.

A comprehensive study of pharmaceutics solubility in supercritical solvent through diverse thermodynamic and hybrid Machine learning approaches

The pharmaceutical industry is increasingly drawn to the research of innovative drug delivery systems through the use of supercritical CO2 (scCO2)-based techniques. Measuring the solubility of drugs in scCO2 at varying conditions is a crucial parameter in this context. In this research, the supercritical solubility of two pharmaceutical ingredients, namely Febuxostat and Chlorpromazine, has been assessed theoretically using various thermodynamic approaches, including PR, SRK, UNIQUAC, and Wilson models. Additionally, hybrid machine learning models of PO-GPR, and PO-KNN were applied to anticipate the supercritical solubility of these medicines. Verification of the accuracy of each model for each pharmaceutical substance is conducted against previously reported experimental solubility data. In the comparison between the SRK and PR models, it is observed that the SRK model displays greater precision in correlating the solubility of both drugs. It consistently achieves a mean Radj value of 0.995 across all cases and mean AARD% values of 14.47 and 9.30 for Febuxostat and Chlorpromazine, respectively. Furthermore, the findings indicate that the UNIQUAC model surpasses the Wilson model in precisely representing the solubility of both medicines. It consistently achieves a mean Radj value higher than 0.985 across both cases and mean AARD% values of 11.39 and 7.08 for Febuxostat and Chlorpromazine, respectively. Additionally, the performance of both hybrid machine learning models proved to be excellent in anticipating the supercritical solubility of both compounds.

Advanced and Potential Methods for Extraction of Bioactive Compounds from Avocado Peel—A Review

Extraction techniques are continuously developed by the scientific community. Meanwhile, avocado peel is a by-product of avocado processing and a source of bioactive compounds. The purpose of this review was to summarize the use of advanced techniques for extracting bioactive compounds from avocado peel to help understand which techniques have and have not been applied to avocado peel. Three primary databases were used to collect the information, including Google Scholar, Scopus, and Web of Science, by using the keywords “avocado”, “peel”, and “extraction”. Additional keywords related to the extraction technique were also used, including “Microwave-Assisted Extraction”, “Ultrasound-Assisted Extraction”, “Enzyme-Assisted Extraction”, “Pressurized Liquid Extraction”, “Supercritical Fluid Extraction”, “Natural Deep Eutectic Solvents”, “Three-phase partitioning (TPP)”, “Pulsed-Electric Field”, “High Voltage Electric Discharge Plasma”, “Centrifugal Partition Extraction”, and “Surfactant-Mediated Extraction”. The results show that microwave-assisted extraction, ultrasound-assisted extraction, enzyme-assisted extraction, pressurized liquid extraction, supercritical fluid extraction, TPP, and natural deep eutectic solvent extraction have been used to retrieve bioactive compounds from avocado peel. Other techniques have not yet been applied for the extraction of bioactive compounds from avocado peel. This article is the first review discussing the advanced extraction technique for retrieving bioactive compounds from avocado peel. This article creates a paradigm for future studies.

Steam distillation, supercritical fluid extraction, and anti-Trypanosoma cruzi activity of compounds from pink pepper (Schinus terebinthifolius Raddi)

Anti-Trypanosoma cruzi activity of compounds from fruits of Schinus terebinthifolius Raddi (pink pepper) were evaluated, using sustainable techniques such as steam distillation (SD) and supercritical fluid extraction (SFE). SD was optimised using a design of experiment and SFE was carried out using supercritical CO2 solvent (300 bar and 60 °C). Results of the anti-T. cruzi activity showed that the essential oil presented high activity (IC50 = 4.5 ± 0.3 μg/mL), whereas the supercritical extract had a moderate effect (IC50 = 19.7 ± 2.9 μg/mL). The differences in the anti-T. cruzi activity can be attributed to the extraction of non-volatile compounds in the SFE, such as moronic and (Z)-masticadienoic acids. In contrast, SD extracted only volatile compounds such as monoterpenes and sesquiterpenes. Therefore, these results suggest that the volatile compounds from pink pepper are involved with the anti-T. cruzi activity.

Solvation Properties of Neutral Gold Species in Supercritical Water Studied By THz Spectroscopy

AbstractSupercritical water provides distinctly different solvation properties compared to what is known from liquid water. Despite its prevalence deep in the Earth's crust and its role in chemosynthetic ecosystems in the vicinity of hydrothermal vents, molecular insights into its solvation mechanisms are still very scarce compared to what is known for liquid water. Recently, neutral metal particles have been detected in hydrothermal fluids and proposed to explain the transport of gold species to ore deposits on Earth. Using ab initio molecular dynamics, we elucidate the solvation properties of small gold species at supercritical conditions. The neutral metal clusters themselves contribute enormous THz intensity not because of their intramolecular vibrations, but due to their pronounced electronic polarization coupling to the dynamical supercritical solvent, leading to a continuum absorption up to about 1000 cm−1. On top, long‐lived interactions between the gold clusters and solvation water leads at these supercritical conditions to a sharp THz resonance that happens to be close to the one due to H‐bonding in liquid water at ambient conditions. The resulting distinct resonances can be used to analyse the solvation properties of neutral metal particles in supercritical aqueous solutions.

Solvation Properties of Neutral Gold Species in Supercritical Water Studied By THz Spectroscopy.

Supercritical water provides distinctly different solvation properties compared to what is known from liquid water. Despite its prevalence deep in the Earth's crust and its role in chemosynthetic ecosystems in the vicinity of hydrothermal vents, molecular insights into its solvation mechanisms are still very scarce compared to what is known for liquid water. Recently, neutral metal particles have been detected in hydrothermal fluids and proposed to explain the transport of gold species to ore deposits on Earth. Using ab initio molecular dynamics, we elucidate the solvation properties of small gold species at supercritical conditions. The neutral metal clusters themselves contribute enormous THz intensity not because of their intramolecular vibrations, but due to their pronounced electronic polarization coupling to the dynamical supercritical solvent, leading to a continuum absorption up to about 1000 cm-1. On top, long-lived interactions between the gold clusters and solvation water leads at these supercritical conditions to a sharp THz resonance that happens to be close to the one due to H-bonding in liquid water at ambient conditions. The resulting distinct resonances can be used to analyse the solvation properties of neutral metal particles in supercritical aqueous solutions.

Important carotenoids derived from marine biomass: Extraction, stabilization, and potentiality in food, cosmetics, and pharmaceutical application

Marine organisms are an important source of bioactive compounds, among which marine carotenoids have garnered attention for their health benefits. Hundreds of carotenoids are extracted from different natural biomass; among them marine carotenoids always have special appeal due to their unique properties. This review discusses recent literature on marine carotenoids, extraction and stabilization methods, and their applications in various industries. Green extraction techniques that use supercritical carbon dioxide (SC–CO2) and natural deep eutectic solvents were highlighted in addition to conventional extraction solvents. As most natural carotenoids are highly unstable, this review describes recent advances to stabilize these bioactive compounds. This work offers new insights into marine carotenoids and their potential application in the food, pharmaceutical, and cosmetic industries.

Obtaining Bixin- and Tocotrienol-Rich Extracts from Peruvian Annatto Seeds Using Supercritical CO2 Extraction: Experimental and Economic Evaluation.

Currently, Bixa orellana L. extracts are used as a color source in the food, pharmaceutical, and cosmetic industries because they are important as a potential source of antioxidant activity. The extraction is carried out by conventional methods, using alkaline solutions or organic solvents. These extraction methods do not take advantage of the lipid fraction of annatto (Bixa orellana L.) seeds, and the process is not friendly to the environment. In this work, the objective was to obtain an extract rich in nutraceuticals (bixin and tocols) of high antioxidant power from Peruvian annatto seeds as a potential source for a functional food or additive in the industry using supercritical fluid extraction (SFE). Experiments related to extraction yield, bixin, tocotrienols, tocopherols, and antioxidant activity were carried out. The SFE was performed at 40 °C, 50 °C, and 60 °C, and 100, 150, and 250 bar with 0.256 kg/h carbon dioxide as the supercritical solvent (solvent-to-feed ratio of 10.2). Supercritical extraction at 60 °C and 250 bar presented the best results in terms of global extraction yield of 1.40 ± 0.01 g/100 g d.b., extract concentration of 0.564 ± 0.005 g bixin/g extract, 307.8 mg α-tocotrienol/g extract, 39.2 mg β-tocotrienol/g extract, 2 mg γ-tocopherol/g extract, and IC50 of 989.96 μg extract/mL. Economical evaluation showed that 60 °C, 250 bar, and 45 min presented the lowest cost of manufacturing (2 × 2000 L, COM of USD 212.39/kg extract). This extract is a potential source for functional food production.

Computational investigations on anti-cancer drug solubility in supercritical solvent for efficient cancer therapy

This study focuses on the computational analysis of solubility of an anti-cancer drug namely Temozolomide in SC-CO2, utilizing a dataset consisting of pressure and temperature as input parameters and Temozolomide solubility as the output. Temperature range between 308–338 K, and pressure between 120–400 bar were taken into account for building the models. Three regression models—K Nearest Neighbors (KNN) regression, Huber regression, and Support Vector Machine (SVM) regression—were employed to predict solubility, with hyper-parameter tuning accomplished using the Cuckoo Search (CS) optimization algorithm. The updated results underscored the efficacy of these models in predicting Temozolomide solubility. Notably, the SVM regression model displayed impressive performance, yielding an R-squared score of approximately 0.976, indicating a high degree of accuracy in predicting solubility. The RMSE for SVM regression was approximately 0.000152, signifying minimal prediction error, and the Mean Absolute Error (MAE) was approximately 0.000124.

Phase Behavior of the Mixtures of 2- and 3-Components for Poly(styrene-co-octafluoropentyl methacrylate) by Dispersion Polymerization under CO2.

The dispersed-phase polymerization of poly(styrene-co-2,2,3,4,4,4-octafluoropentyl methacrylate), also known as p(styrene-co-OFPMA), took place in supercritical carbon dioxide (sc-CO2). The chemical and physical properties of p(styrene-co-OFPMA) were studied by varying the styrene-to-OFPMA ratios (40:1, 30:1, and 20:1) and 2,2'-azobis(isobutyronitrile) (AIBN) initiator amounts (wt %: 1.0, 2.0, 3.0). The cloud-point data were obtained for various systems, including the binary mixtures of p(styrene-co-OFPMA) (30:1 ratio, AIBN wt %: 1.0, 2.0, 3.0) with supercritical solvents such as sc-CO2, sc-CH3OCH3, sc-C3H6, sc-C4H8, and sc-CHClF2. Phase behavior (i.e., mixtures) was studied at temperatures of 324-455 K and pressure below 201 MPa. In the binary system of p(styrene-co-OFPMA) + sc-CH3OCH3, a lower critical solution temperature (LCST)-type curve was observed, characterized by a positive slope. Conversely, the binary systems of p(styrene-co-OFPMA) + (sc-C3H6, sc-C4H8, sc-CHClF2) exhibited an upper critical solution temperature (UCST) behavior with a decreasing slope. The phase equilibrium curves were obtained for p(styrene-co-OFPMA) [30:1; 1.0% (Mw = 42,400), 2.0% (Mw = 33,800), and 4.0% (Mw = 24,100); AIBN: 1.0 wt %] + sc-C3H6, sc-C4H8, and sc-CHClF2 mixtures. These curves exhibited an increasing slope for p(styrene-co-OFPMA) + sc-CH3OCH3 and a negative slope for p(styrene-co-OFPMA) + (sc-C3H6, sc-C4H8, sc-CHClF2) systems, indicating distinct phase behavior. Tetramethyl orthosilicate (TMOS) addition (0.0-68.9 wt %) to P(styrene-co-OFPMA) (30:1; AIBN wt %: 1.0) + solvents altered the phase equilibrium, switching from UCST to LCST, as evidenced by changes in the pressure-temperature slope.

Impact of cosolvent concentration of ternary and binary solution for biodegradable poly(D,L-lactic acid) under supercritical solvents

This study provides experimental cloud point data for poly(D,L-lactic acid) (PDLLA) polymer and supercritical solvent and cosolvent mixtures at ∼333 K to ∼424 K and pressures up to ∼157 MPa for PDLLA. Various solvents investigated include CO2, DME, CH2F2, CHF3, C3F8, C3H8, c-C4F8, and F-22. CO2 is a preferred solvent due to non-toxic, non-flammability, low cost but the results indicate low PDLLA solubility in CO2 at ∼100 to 110 MPa and at experimental temperature range of 333.15 K to 423.15 K. However, for DME and F-22 solvents, the cloud points appear at ∼10 to 30 MPa suggesting these are better suited to dissolve polymers for supercritical processes. The results indicate that DME is the favored solvent due to its superior performance. In the pure component system, the cloud point curve exhibited a distinctive nature, displaying behaviors such as UCST, horizontal, or LCST under the experimental conditions. However, the phase behavior of PDLLA in the presence of both solvent and co-solvent demonstrated a transition from UCST to LCST types. This observed change suggests variations in the solubility of the polymer under the same experimental conditions, highlighting the dynamic influence of co-solvents on PDLLA's solubility characteristics. For systems with higher molecular weight PDLLA, increased self-association resulted in reduced solubility and upward shift of cloud point curves. This study will be helpful in optimizing solvent + cosolvent and operating conditions involving PDLLA in supercritical processes, especially in biomedical applications.

Artificial Intelligence aided pharmaceutical engineering: Development of hybrid machine learning models for prediction of nanomedicine solubility in supercritical solvent

This study aims to model the relationship between pressure (P) and temperature (T) inputs and the corresponding outputs of supercritical carbon dioxide (SC-CO2) density and mole fraction of rivaroxaban as the solute. Three models employed in this work included Support Vector Regression (SVR), Multilayer Perceptron (MLP), and LASSO. The hyper-parameter tuning was performed using Tabu Search approach. The results revealed that MLP is the best model for predicting SC-CO2 density, achieving an R2 score of 0.9994 and demonstrating high accuracy and precision for correlating the density dataset. However, for the mole fraction of rivaroxaban (solubility), SVR outperformed the other models. It revealed an R2 value of 0.98037, confirming a strong correlation between the inputs and outputs. SVR also exhibits a lower mean squared error and maximum error compared to MLP and LASSO for the mole fraction prediction. Although MLP excels in predicting SC-CO2 density, SVR proves to be the superior model for accurately estimating the mole fraction of rivaroxaban based on the given inputs. These findings contribute to the understanding and modeling of SC-CO2 properties, particularly the mole fraction of rivaroxaban, which can be valuable in various research and practical applications.