On the evaluation of the carbon dioxide solubility in polymers using gene expression programming

Evaluation, prediction, and measurement of carbon dioxide (CO2) solubility in different polymers are crucial for engineers in various chemical applications, such as extraction and generation of novel materials. In this paper, correlations based on gene expression programming (GEP) were generated to predict the value of carbon dioxide solubility in three polymers. Results showed that the generated correlations could represent an outstanding efficiency and provide predictions for carbon dioxide solubility with satisfactory average absolute relative errors of 9.71%, 5.87%, and 1.63% for polystyrene (PS), polybutylene succinate-co-adipate (PBSA), and polybutylene succinate (PBS), respectively. Trend analysis based on Henry's law illustrated that increasing pressure and decreasing temperature lead to an increase in carbon dioxide solubility. Finally, outlier discovery was applied using the leverage approach to detect the suspected data points. The outlier detection demonstrated the statistical validity of the developed correlations. William's plot of three generated correlations showed that all of the data points are located in the valid zone except one point for PBS polymer and three points for PS polymer.

A double-population chaotic self-adaptive evolutionary dynamics model for the prediction of supercritical carbon dioxide solubility in polymers

Solubility of gas in polymers is an important physico-chemical property of foam materials and widely used in the preparation and modification of new materials. Under the conditions of high temperature and high pressure, the dissolution process is a nonlinear, non-equilibrium and dynamic process, so it is difficult to establish an accurate solubility calculation model. Inspired by particle dynamics and evolutionary algorithm, this paper proposes a hybrid model based on chaotic self-adaptive particle dynamics evolutionary algorithm (CSA-PD-EA), which can use the iterative process of particles in evolutionary algorithms at the dynamic level to simulate the mutual diffusion process of molecules during dissolution. The predicted solubility of supercritical CO₂ in poly(d,l-lactide-co-glycolide), poly(l-lactide) and poly(vinyl acetate) indicated that the comprehensive prediction performance of the CSA-PD-EA model was high. The calculation error and correlation coefficient were, respectively, 0.3842 and 0.9187. The CSA-PD-EA model showed prominent advantages in accuracy, efficiency and correlation over other computational models, and its calculation time was 4.144-15.012% of that of other dynamic models. The CSA-PD-EA model has wide application prospects in the computation of physical and chemical properties and can provide the basis for the theoretical calculation of multi-scale complex systems in chemistry, materials, biology and physics.

New Design Method for Fabricating Multilayer Membranes Using CO2-Assisted Polymer Compression Process

It was verified that deep learning can be used in creating multilayer membranes with multiple porosities using the CO2-assisted polymer compression (CAPC) method. To perform training while reducing the number of experimental data as much as possible, the experimental data of the compression behavior of two layers were expanded to three layers for training, but sufficient accuracy could not be obtained. However, the accuracy was dramatically improved by adding the experimental data of the three layers. The possibility of only simulating process results without the necessity for a model is a merit unique to deep learning. Overall, in this study, the results show that by devising learning data, deep learning is extremely effective in designing multilayer membranes using the CAPC method.

Correlation between the Porosity and Permeability of a Polymer Filter Fabricated via CO2-Assisted Polymer Compression

A porous filter was fabricated by plasticizing polymer fibers with CO2, followed by pressing and adhering; then, its gas permeability, a basic physical property of filters, was measured using N2. The as-obtained filter was well compressed and expected to approximate a sintered porous material. Therefore, the fabricated filter was analyzed by applying the Darcy law, and the correlation between its gas permeability and porosity was clarified. The gas permeability decreased owing to both pore size and porosity reduction upon increasing the degree of compression, which is a feature of the CO2-assisted polymer compression method. In particular, without any contradiction of pore size data previously reported, the gas permeability was clearly determined by the filter porosity and pore size. This study can serve as a guide for designing filters via CO2-assisted polymer compression.

Process Development of CO2-Assisted Polymer Compression for High Productivity: Improving Equipment and the Challenge of Numbering-Up

The CO2-assisted polymer compression method is used herein to prepare porous polymer materials by bonding laminated polymer fiber sheets using a piston in the presence of CO2. In this work, the CO2 flow line connections were moved from the pressure vessel to the piston to increase productivity, which makes the pressure vessel free-moving and the processing time of sample introduction and removal seemingly zero. In addition, a numbering-up method suitable for CO2-assisted polymer compression is proposed and verified based on the variability of the products. The variability of the product was evaluated using porosity, which is one of the most important properties of a porous material. It is found that the CO2 exhaust process, specific to this method, that uses high-pressure CO2, causes product variation, which can be successfully suppressed by optimizing the CO2 exhaust process.

Analysis of Sustained Release Behavior of Drug-Containing Tablet Prepared by CO₂-Assisted Polymer Compression

A controlled-release system for drug delivery allows the continuous supply of a drug to the target region at a predetermined rate for a specified period of time. Herein, the sustained release behavior of a drug-containing tablet fabricated through CO₂-assisted polymer compression (CAPC) was investigated. CAPC involves placing the drug in the center of a nonwoven fabric, sandwiching this fabric between an integer number of nonwoven fabrics, and applying pressure bonding. An elution test, in which the drug-carrying tablet was immersed in water, showed that sustained-release performance can be controlled by the number of nonwoven fabrics covering the top and bottom of the drug-loaded fabric and compression conditions. A model of sustained drug release was formulated to estimate the effective diffusion coefficient in the porous material. Comparative analysis of the bulk diffusion coefficient revealed that the change in diffusion volume due to change in porosity predominates. The tortuosity of the diffusion path was 3⁻4, and tended to remain almost constant or increase only slightly when the compression rate was increased. These findings show that sustained drug release can be controlled by incorporating the drug into a nonwoven fabric and using the same raw material to encapsulate it.

Prediction of pKa Values for Neutral and Basic Drugs based on Hybrid Artificial Intelligence Methods

The pKa value of drugs is an important parameter in drug design and pharmacology. In this paper, an improved particle swarm optimization (PSO) algorithm was proposed based on the population entropy diversity. In the improved algorithm, when the population entropy was higher than the set maximum threshold, the convergence strategy was adopted; when the population entropy was lower than the set minimum threshold the divergence strategy was adopted; when the population entropy was between the maximum and minimum threshold, the self-adaptive adjustment strategy was maintained. The improved PSO algorithm was applied in the training of radial basis function artificial neural network (RBF ANN) model and the selection of molecular descriptors. A quantitative structure-activity relationship model based on RBF ANN trained by the improved PSO algorithm was proposed to predict the pKa values of 74 kinds of neutral and basic drugs and then validated by another database containing 20 molecules. The validation results showed that the model had a good prediction performance. The absolute average relative error, root mean square error, and squared correlation coefficient were 0.3105, 0.0411, and 0.9685, respectively. The model can be used as a reference for exploring other quantitative structure-activity relationships.

Fabrication of porosity-controlled polyethylene terephthalate porous materials using a CO2-assisted polymer compression method

The objective of this study is to fabricate porosity-controlled polyethylene terephthalate porous materials using a CO₂-assisted polymer compression (CAPC) method. In a previous study, the CAPC method was used to fabricate porous polymer materials by compressing fabric sheets in the presence of CO₂. However, the controllability of the porosity was not clear in the previous study. In this study, it is shown that the porosity of porous polymer materials could be easily controlled by adjusting the operating conditions of the CAPC method, using polyethylene terephthalate (PET) nonwoven fabric sheets. Using mercury porosimetry, a decrease in the porosity induced by compression accompanied by a decrease in the pore size is demonstrated. Scanning electron micrographs strongly indicate the plasticization of PET fibers by CO₂.

Porosity-controlled polyethylene terephthalate porous materials can be easily fabricated by using a CO₂-assisted polymer compression (CAPC) method.

A method to improve dimensional accuracy and mechanical properties of injection molded polypropylene parts

Gas counter pressure (GCP) technology can impose a reverse pressure to melt and thereby effectively increase the pressure acting on the melt at flow front. Theoretically, it has a potential to solve some defects often occurring in conventional injection molding (CIM) process. This paper designed and manufactured a GCP injection mold. GCP injection molding experiments were conducted. Effects of GCP process on melt flow and density, dimensional accuracy, and mechanical properties of molded samples were investigated. The results showed that GCP process can effectively inhibit the “fountain effect” in melt filling process, decrease the dimensional shrinkage of molded samples, increase dimensional accuracy of samples, and effectively improve impact property of samples. For the samples without weld line, tensile strength and flexural strength of GCP injection molded samples are slightly increased in comparison with those of CIM samples, but for the samples with weld line, GCP process can greatly improve the tensile strength and flexural strength of molded samples. When GCP is 9 MPa and GCP holding time is 10 s, the dimensional accuracy of molded samples without weld line, the tensile strength and flexural strength of the molded samples with weld line all increase up to maximum values. In comparison with CIM samples, the dimensional shrinkage of samples without weld line decreases by 17.2%, the tensile strength and flexural strength of samples with weld line increase by 30.51% and 23.69%, respectively. The impact value of the samples molded by process parameter combination of GCP 9 MPa and GCP holding time 20 s is the highest, and the impact value increases by 18.65%.

Solubility prediction of gases in polymers based on an artificial neural network: a review

Solubility prediction model based on a hybrid artificial neural network.

Prediction of supercritical carbon dioxide solubility in polymers based on hybrid artificial intelligence method integrated with the diffusion theory

A solubility prediction model based on a hybrid artificial intelligence method integrated with diffusion theory is proposed.

Investigation into the pharmacokinetic–pharmacodynamic model of Zingiberis Rhizoma/Zingiberis Rhizoma Carbonisata and contribution to their therapeutic material basis using artificial neural networks

A PK/PD model of ZR/ZRC based on ANN was utilized to evaluate relative contribution of concentration to its drug efficacy.

Experimental determination and model correlation for the solubilities of trialkyl phosphates in supercritical carbon dioxide

The solubilities of a series of trialkyl phosphates in supercritical carbon dioxide have been investigated.

Predictive calculation of carbon dioxide solubility in polymers

Novel calculation model of CO₂ solubility in polymers using a hybrid intelligence algorithm.

Solubility and solution thermodynamics of rhein in eight pure solvents from (288.15 to 313.15) K

Solubility of rhein in water, dichloroethane, glycol, ethanol, methanol, ethyl acetate, propanol, and butanol was measured. Two equations including the modified Apelblat equation, and λh equation can correlate the solubilities of rhein.

Prediction of the stress relaxation property of diene rubber composites by artificial neural network approaches

An artificial neural network was established to predict the stress relaxation property of diene rubber composites during ozone aging.