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This study intends to effectively forecast solubility parameter of diverse polymers by creating machine learning models that can grasp the complex relationships between essential input factors like molecular weight, melting point, boiling point, liquid molar volume, radius of gyration, dielectric constant, dipole moment, refractive index, van der Waals area and reduced volume, and parachor, alongside the target variable, which is solubility coefficient of polymers. The goal is to create strong models that accurately capture these intricate relationships to facilitate accurate forecasts of the solubility parameter for polymers. Multiple machine learning algorithms, ranging from basic methods like Linear Regression to advanced techniques such as Artificial Neural Networks (ANNs), Ridge Regression, Lasso Regression, Support Vector Machines (SVMs), Linear Regression, Random Forests (RFs), Gradient Boosting Machines (GBM), K-Nearest Neighbors (KNN), Elastic Net, Decision Trees, Light Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), Convolutional Neural Networks (CNNs), and Extreme Gradient Boosting (XGBoost) were utilized. These methods were utilized to create data-driven models that adeptly seize the intricate connections between input characteristics and output variable, facilitating precise predictions of the solubility parameter for polymers. The efficacy of the developed models was rigorously evaluated using statistical metrics such as R², RMSE, and MRD%, along with visual tools including cross-plots, deviation plots, and SHAP analysis to enhance interpretability and predictive reliability. To guarantee the dataset’s reliability, consisting of 1,799 datapoints on the solubility parameter of polymers, the Monte Carlo outlier detection algorithm was utilized. This stage verified the dataset’s accuracy and appropriateness for model training and evaluation. Results indicated that the models CatBoost, ANN, and CNN surpassed other techniques, attaining superior accuracy shown by the highest R-squared values and the lowest error rates. Sensitivity analysis showed that every input feature impacted the target variable, while SHAP analysis determined that dielectric constant was the most significant factor influencing the solubility parameter of polymers. These results highlight the efficiency of the utilized machine learning methods and emphasize the vital importance of these input parameters in establishing the solubility parameter of polymers. This method not only verifies that the models can make accurate predictions but also provides valuable insights into the impact of input features on solubility parameters of polymers, enhancing algorithm interpretability and scientific understanding.

This study investigates the performance of the Pebax membrane in CO2 adsorption and permeation processes. The adsorption tests were conducted using a gas adsorption reactor based on the solubility approach, with adsorption capacity determined from system pressure variations. SEM characterization revealed a smooth and homogeneous surface with the presence of microvoids that contribute to an enhanced CO2 adsorption capacity of up to 397.53 mg/g. The linear relationship between adsorption capacity and pressure variation was used to determine the solubility coefficient, while the slope of the qt versus t1/2 plot provided the diffusion constant. Based on the obtained solubility and diffusion values, the calculated CO2 permeability of the Pebax membrane indicates strong gas transport properties, confirming its potential as one of the most promising adsorbent materials for smart decarbonization and sustainable carbon capture applications.

Vacuum Insulation Panels (VIPs) are highly efficient thermal insulation materials with extremely low thermal conductivity based on the vacuum principle. With the sealing properties of the gas barrier envelopes, a long service life of the VIP is obtained. The mechanism and influence factors of gas and water vapor permeability were mathematically analyzed to explore the influence of gas barrier envelopes on the thermal performance of VIPs. Three typical gas barriers were studied, and the selection of the gas barrier and other aspects of optimization were involved. The relationships among temperature, humidity, solubility coefficient, diffusion coefficient, and permeability were concluded, which shows that temperature has a much greater effect on the permeability of the gas barrier relative to humidity. The numerical analysis and influencing factors of VIPs’ service life were also exemplified with three different types of gas barrier envelopes. The experimental results show that depending on the environment, the temperature has a major impact on the effective thermal conductivity and service life of VIP. The research was significant in the selection of gas barriers, the optimization of the performance, and the development of vacuum insulation material.

Presently, the power generation industry has grown in response to the rising demand for electricity, resulting in higher reliance on the combustion of fossil fuels and coals. As a consequence, this has greatly impacted global warming causing an increase in the emission of carbon dioxide (CO2), which in turn has affected the sustainable development goals globally. There are several carbon capture methods used in the industries with the aim of reducing the emission of CO2. One of the most popular membrane technology methods is Mixed Matrix Membranes (MMMs). In this research, the blend MMMs were synthesised by embedding the functionalised multi-walled carbon nanotubes (MWCNTs-F) using Chen’s Soft-Cutting method along with β-cyclodextrin (β-CD) into the blend cellulose acetate butyrate (CAB) polymer, which had different molecular weights of 12,000, 16,000 and 70,000, mixed in a ratio of 1:2:2. To date, the Hansen Solubility Parameters together with the CO2 solubility and diffusivity coefficients have not been explored for this combination of MMMs. However, this work demonstrated the CO2 solubility coefficient directly correlate to the CO2 permeance of MMM. Additionally, the CO2 coefficient was influenced by chain spacing and the amorphous fraction, which in turn affected the CO2 affinity of the membrane.

This work illustrates the potential of using atomistic molecular dynamics (MD) and grand-canonical Monte Carlo (GCMC) simulations prior to experiments in order to pre-screen candidate membrane structures for gas separation, under harsh conditions of temperature and pressure. It compares at 300 °C and 400 °C the CO2/CH4 and CO2/N2 sieving properties of a series of hybrid networks based on inorganic silsesquioxanes hyper-cross-linked with small organic PMDA or 6FDA imides. The inorganic precursors are the octa(aminopropyl)silsesquioxane (POSS), which degrades above 300 °C, and the octa(aminophenyl)silsesquioxane (OAPS), which has three possible meta, para or ortho isomers and is expected to resist well above 400 °C. As such, the polyPOSS-imide networks were tested at 300 °C only, while the polyOAPS-imide networks were tested at both 300 °C and 400 °C. The feed gas pressure was set to 60 bar in all the simulations. The morphologies and densities of the pure model networks at 300 °C and 400 °C are strongly dependent on their precursors, with the amount of significant free volume ranging from ~2% to ~20%. Since measurements at high temperatures and pressures are difficult to carry out in a laboratory, six isomer-specific polyOAPS-imides and two polyPOSS-imides were simulated in order to assess their N2, CH4 and CO2 permselectivities under such harsh conditions. The models were first analyzed under single-gas conditions, but to be closer to the real processes, the networks that maintained CO2/CH4 and CO2/N2 ideal permselectivities above 2 were also tested with binary-gas 90%/10% CH4/CO2 and N2/CO2 feeds. At very high temperatures, the single-gas solubility coefficients vary in the same order as their critical temperatures, but the differences between the penetrants are attenuated and the plasticizing effect of CO2 is strongly reduced. The single-gas diffusion coefficients correlate well with the amount of available free volume in the matrices. Some OAPS-based networks exhibit a nanoporous behavior, while the others are less permeable and show higher ideal permselectivities. Four of the networks were further tested under mixed-gas conditions. The solubility coefficient improved for CO2, while the diffusion selectivity remained similar for the CO2/CH4 pair and disappeared for the CO2/N2 pair. The real separation factor is, thus, mostly governed by the solubility. Two polyOAPS-imide networks, i.e., the polyorthoOAPS-PMDA and the polymetaOAPS-6FDA, seem to be able to maintain their CO2/CH4 and CO2/N2 sieving abilities above 2 at 400 °C. These are outstanding performances for polymer-based membranes, and consequently, it is important to be able to produce isomer-specific polyOAPS-imides for use as gas separation membranes under harsh conditions.

Liquid forging alias squeeze casting gives the combined advantage of casting and forging. Optimum process parameters are important to get a cost-efficient process. In this study, four materials have been identified, which are extensively used in industries. These materials are commercially pure Al and three Al-alloys namely, 2124, 2218 and 6063. The pouring temperature and the mold temperature is maintained at 700oC and 250oC respectively. The materials were developed at seven pressure variations from 0 to 150 MPa. The effect of the pressure on the microstructures, porosity, and hardness has been reported. The coefficient of solubility is estimated for all materials and a polynomial relationship is found to be the best fit with the applied pressure. The pressure of 100 MPa gives better increment in hardness. The melting point and the freezing coefficient of the materials under study have been determined. A linear relationship between the pressure and the freezing time is deduced. It is observed that the solubility and the freezing coefficients depend on the pressure as well, in addition to the composition and temperature.

Membrane separation technology is applied in natural gas processing, while a high-performance membrane is highly in demand. This paper considers the bright future of functionalized graphene oxide (GO) membranes in acid gas removal from natural gas. By molecular simulations, the adsorption and diffusion behaviors of several unary gases (N2, CH4, CO2, H2S, and SO2) are explored in the 1,4-phenylenediamine-2-sulfonate (PDASA)-doped GO channels. Molecular insights show that the multilayer adsorption of acid gases evaluates well by the Redlich-Peterson model. A tiny amount of PDASA promotes the solubility coefficient of CO2 and H2S, respectively, up to 4.5 and 5.3 mmol·g−1·kPa−1, nearly 2.5 times higher than those of a pure GO membrane, which is due to the improved binding affinity, great isosteric heat, and hydrogen bonds, while N2 and CH4 only show single-layer adsorption with solubility coefficients lower than 0.002 mmol·g−1·kPa−1, and their weak adsorption is insusceptible to PDASA. Although acid gas diffusivity in GO channels is inhibited below 20 × 10−6 cm2·s−1 by PDASA, the solubility coefficient of acid gases is certainly high enough to ensure their separation efficiency. As a result, the permeabilities (P) of acid gases and their selectivities (α) over CH4 are simultaneously improved (PCO2 = 7265.5 Barrer, αCO2/CH4 = 95.7; P(H2S+CO2) = 42075.1 Barrer, αH2S/CH4 = 243.8), which outperforms most of the ever-reported membranes. This theoretical study gives a mechanistic understanding of acid gas separation and provides a unique design strategy to develop high-performance GO membranes toward efficient natural gas processing.

The advancement of modern injection systems of diesel engines is related to a constant increase in the injection pressures generated by injection pumps. This translates into an improvement of the engine operation indexes, including the emission-related ones. Such an approach brings a series of problems related to the design, construction and durability of the injection system. Therefore, the authors asked whether the current market trend in injection systems is the only appropriate path to be taken. When searching for the answer, the authors decided to propose an innovative concept consisting of dissolving exhaust gas in diesel fuel with the use of an injection pump. Such a saturated solution, when flowing out of the injection nozzle, begins the process of releasing the gas dissolved in the fuel. This has a positive impact on the atomization process, hence the process of combustion. The aim of this paper stems from the previously performed research. Due to the nature of the phenomenon, it was necessary to propose a new design for the injection pump. For correct selection of the dimensions of the pumping section, it was of key importance to determine the coefficient of solubility and the bulk modulus of the solution of diesel fuel and exhaust gas. Aside from the description of the applied method and the results of the direct measurements, this paper presents the yet undescribed results of the measurements of the coefficient of solubility of different concentrations of exhaust gas in diesel fuel. The authors also investigated the influence of the amount of exhaust gas dissolved in the fuel on the bulk modulus of the solution. The final part of the paper is a description of a proprietary design of a hypocycloidal injection pump. The application of the innovative drive allows a correct dissolution of exhaust gas in the fuel.

Contamination of surface waters by pharmaceuticals is now widespread. There are few data on their environmental behaviour, particularly for those which are cationic at typical surface water pH. As the external surfaces of bacterio-plankton cells are hydrophilic with a net negative charge, it was anticipated that bacterio-plankton in surface-waters would preferentially remove the most extensively-ionised cation at a given pH. To test this hypothesis, the persistence of four, widely-used, cationic pharmaceuticals, chloroquine, quinine, fluphenazine and levamisole, was assessed in batch microcosms, comprising water and bacterio-plankton, to which pharmaceuticals were added and incubated for 21 days. Results show that levamisole concentrations decreased by 19 % in microcosms containing bacterio-plankton, and by 13 % in a parallel microcosm containing tripeptide as a priming agent. In contrast to levamisole, concentrations of quinine, chloroquine and fluphenazine were unchanged over 21 days in microcosms containing bacterio-plankton. At the river-water pH, levamisole is 28 % cationic, while quinine is 91–98 % cationic, chloroquine 99 % cationic and fluphenazine 72–86 % cationic. Thus, the most neutral compound, levamisole, showed greatest removal, contradicting the expected bacterio-plankton preference for ionised molecules. However, levamisole was the most hydrophilic molecule, based on its octanol–water solubility coefficient ( K ow ). Overall, the pattern of pharmaceutical behaviour within the incubations did not reflect the relative hydrophilicity of the pharmaceuticals predicted by the octanol–water distribution coefficient, D ow , suggesting that improved predictive power, with respect to modelling bioaccumulation, may be needed to develop robust environmental risk assessments for cationic pharmaceuticals.

Organic compounds such as benzene, toluene, ethyl benzene and o-, m-, and p-xylene from contaminated soil and groundwater may permeate through thermoplastic pipes which are used for the conveyance of drinking water in water distribution systems. In this study, permeation parameters of benzene in 25 mm (1 inch) standard inside dimension ratio (SIDR) 9 high density polyethylene (HDPE) pipes were estimated by fitting the measured data to a permeation model based on a combination of equilibrium partitioning and Fick's diffusion. For bulk concentrations between 6.0 and 67.5 mg/L in soil pore water, the concentration-dependent diffusion coefficients of benzene were found to range from 2.0 × 10−9 to 2.8 × 10−9cm2/s while the solubility coefficient was determined to be 23.7. The simulated permeation curves of benzene for SIDR 9 and SIDR 7 series of HDPE pipes indicated that small diameter pipes were more vulnerable to permeation of benzene than large diameter pipes, and the breakthrough of benzene into the HDPE pipe was retarded and the corresponding permeation flux decreased with an increase of the pipe thickness. HDPE pipes exposed to an instantaneous plume exhibited distinguishable permeation characteristics from those exposed to a continuous source with a constant input. The properties of aquifer such as dispersion coefficients (DL) also influenced the permeation behavior of benzene through HDPE pipes.