Modeling of ionic liquids viscosity via advanced white-box machine learning.

Ionic liquids (ILs) are more widely used within the industry than ever before, and accurate models of their physicochemical characteristics are becoming increasingly important during the process optimization. It is especially challenging to simulate the viscosity of ILs since there is no widely agreed explanation of how viscosity is determined in liquids. In this research, genetic programming (GP) and group method of data handling (GMDH) models were used as white-box machine learning approaches to predict the viscosity of pure ILs. These methods were developed based on a large open literature database of 2813 experimental viscosity values from 45 various ILs at different pressures (0.06-298.9 MPa) and temperatures (253.15-573 K). The models were developed based on five, six, and seven inputs, and it was found that all the models with seven inputs provided more accurate results, while the models with five and six inputs had acceptable accuracy and simpler formulas. Based on GMDH and GP proposed approaches, the suggested GMDH model with seven inputs gave the most exact results with an average absolute relative deviation (AARD) of 8.14% and a coefficient of determination (R2) of 0.98. The proposed techniques were compared with theoretical and empirical models available in the literature, and it was displayed that the GMDH model with seven inputs strongly outperforms the existing approaches. The leverage statistical analysis revealed that most of the experimental data were located within the applicability domains of both GMDH and GP models and were of high quality. Trend analysis also illustrated that the GMDH and GP models could follow the expected trends of viscosity with variations in pressure and temperature. In addition, the relevancy factor portrayed that the temperature had the greatest impact on the ILs viscosity. The findings of this study illustrated that the proposed models represented strong alternatives to time-consuming and costly experimental methods of ILs viscosity measurement.

Effect of Moisture on the Mechanical Properties of Wood-Plastic Composites Hybridized with Metal Grid Layers.

Wood-plastic composites (WPCs) are some of the most common modern composite materials for interior and exterior design that combine natural waste wood properties and the molding possibility of a thermoplastic polymer binder. The addition of reinforcing elements, binding agents, pigments, and coatings, as well as changes to the microstructure and composition, can all affect the quality of WPCs for particular purposes. To improve the properties, hybrid composite panels of WPCs with 30 wt. % and 40 wt. % of wood content and reinforced with one or three metal grid layers were prepared sequentially by extrusion and hot pressure molding. The results show an average 20% higher moisture absorption for composites with higher wood content. A high impact test (HIT) revealed that the absorbed energy of deformation increased with the number of metal grid layers, regardless of the wood content, around two times for all samples before water immersion and around ten times after water absorption. Also, absorbed energy increases with raised wood content, which is most pronounced in three-metal-grid samples, from 21 J to 26 J (before swelling) and from 15 J to 24 J (after swelling). Flexural tests follow the trends observed by HIT, indicating around 65% higher strength for samples with three metal grid layers vs. samples without a metal grid before water immersion and around 80% higher strength for samples with three metal grid layers vs. samples without a grid after water absorption. The synthesis route, double reinforcing (wood and metal), applied methods of characterization, and optimization according to the obtained results provide a WPC with improved mechanical properties ready for an outdoor purpose.

Toward empirical correlations for estimating the specific heat capacity of nanofluids utilizing GRG, GP, GEP, and GMDH.

When nanoparticles are dispersed and stabilized in a base-fluid, the resulting nanofluid undergoes considerable changes in its thermophysical properties, which can have a substantial influence on the performance of nanofluid-flow systems. With such necessity and importance, developing a set of mathematical correlations to identify these properties in various conditions can greatly eliminate costly and time-consuming experimental tests. Hence, the current study aims to develop innovative correlations for estimating the specific heat capacity of mono-nanofluids. The accurate estimation of this crucial property can result in the development of more efficient and effective thermal systems, such as heat exchangers, solar collectors, microchannel cooling systems, etc. In this regard, four powerful soft-computing techniques were considered, including Generalized Reduced Gradient (GRG), Genetic Programming (GP), Gene Expression Programming (GEP), and Group Method of Data Handling (GMDH). These techniques were implemented on 2084 experimental data-points, corresponding to ten different kinds of nanoparticles and six different kinds of base-fluids, collected from previous research sources. Eventually, four distinct correlations with high accuracy were provided, and their outputs were compared to three correlations that had previously been published by other researchers. These novel correlations are applicable to various oxide-based mono-nanofluids for a broad range of independent variable values. The superiority of newly developed correlations was proven through various statistical and graphical error analyses. The GMDH-based correlation revealed the best performance with an Average Absolute Percent Relative Error (AAPRE) of 2.4163% and a Coefficient of Determination (R2) of 0.9743. At last, a leverage statistical approach was employed to identify the GMDH technique's application domain and outlier data, and also, a sensitivity analysis was carried out to clarify the degree of dependence between input and output variables.

Polysaccharide-based biomaterials in a journey from 3D to 4D printing.

3D printing is a state-of-the-art technology for the fabrication of biomaterials with myriad applications in translational medicine. After stimuli-responsive properties were introduced to 3D printing (known as 4D printing), intelligent biomaterials with shape configuration time-dependent character have been developed. Polysaccharides are biodegradable polymers sensitive to several physical, chemical, and biological stimuli, suited for 3D and 4D printing. On the other hand, engineering of mechanical strength and printability of polysaccharide-based scaffolds along with their aneural, avascular, and poor metabolic characteristics need to be optimized varying printing parameters. Multiple disciplines such as biomedicine, chemistry, materials, and computer sciences should be integrated to achieve multipurpose printable biomaterials. In this work, 3D and 4D printing technologies are briefly compared, summarizing the literature on biomaterials engineering though printing techniques, and highlighting different challenges associated with 3D/4D printing, as well as the role of polysaccharides in the technological shift from 3D to 4D printing for translational medicine.

Insights into modeling refractive index of ionic liquids using chemical structure-based machine learning methods.

Ionic liquids (ILs) have drawn much attention due to their extensive applications and environment-friendly nature. Refractive index prediction is valuable for ILs quality control and property characterization. This paper aims to predict refractive indices of pure ILs and identify factors influencing refractive index changes. Six chemical structure-based machine learning models called eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), Convolutional Neural Network (CNN), Adaptive Boosting-Decision Tree (Ada-DT), and Adaptive Boosting-Support Vector Machine (Ada-SVM) were developed to achieve this goal. An enormous dataset containing 6098 data points of 483 different ILs was exploited to train the machine learning models. Each data point's chemical substructures, temperature, and wavelength were considered for the models' inputs. Including wavelength as input is unprecedented among predictions done by machine learning methods. The results show that the best model was CatBoost, followed by XGBoost, LightGBM, Ada-DT, CNN, and Ada-SVM. The R2 and average absolute percent relative error (AAPRE) of the best model were 0.9973 and 0.0545, respectively. Comparing this study's models with the literature shows two advantages regarding the dataset's abundance and prediction accuracy. This study also reveals that the presence of the -F substructure in an ionic liquid has the most influence on its refractive index among all inputs. It was also found that the refractive index of imidazolium-based ILs increases with increasing alkyl chain length. In conclusion, chemical structure-based machine learning methods provide promising insights into predicting the refractive index of ILs in terms of accuracy and comprehensiveness.

Magneto-Mechanical and Thermal Properties of Nd-Fe-B-Epoxy-Bonded Composite Materials.

Polymer-bonded magnets are a class of composite material that combines the magnetic properties of metal particles and the molding possibility of a polymeric matrix. This class of materials has shown huge potential for various applications in industry and engineering. Traditional research in this field has so far mainly focused on mechanical, electrical or magnetic properties of the composite, or on particle size and distribution. This examination of synthesized Nd-Fe-B-epoxy composite materials includes the mutual comparison of impact toughness, fatigue, and the structural, thermal, dynamic-mechanical, and magnetic behavior of materials with different content of magnetic Nd-Fe-B particles, in a wide range from 5 to 95 wt.%. This paper tests the influence of the Nd-Fe-B content on impacting the toughness of the composite material, as this relationship has not been tested before. The results show that impact toughness decreases, while magnetic properties increase, along with increasing content of Nd-Fe-B. Based on the observed trends, selected samples have been analyzed in terms of crack growth rate behavior. Analysis of the fracture surface morphology reveals the formation of a stable and homogeneous composite material. The synthesis route, the applied methods of characterization and analysis, and the comparison of the obtained results can provide a composite material with optimum properties for a specific purpose.

Polystyrene-b-Poly(2-(Methoxyethoxy)ethyl Methacrylate) Polymerization by Different Controlled Polymerization Mechanisms.

Functional polymers have been an important field of research in recent years. With the development of the controlled polymerization methods, block-copolymers of defined structures and properties could be obtained. In this paper, the possibility of the synthesis of the functional block-copolymer polystyrene-b-poly(2-(methoxyethoxy)ethyl methacrylate) was tested. The target was to prepare the polymer of the number average molecular weight (Mn) of approximately 120 that would contain 20-40% of poly(2-(methoxyethoxy)ethyl methacrylate) by mass and in which the polymer phases would be separated. The polymerization reactions were performed by three different mechanisms for the controlled polymerization-sequential anionic polymerization, atomic transfer radical polymerization and the combination of those two methods. In sequential anionic polymerization and in atomic transfer radical polymerization block-copolymers of the desired composition were obtained but with the Mn significantly lower than desired (up to 30). The polymerization of the block-copolymers of the higher Mn was unsuccessful, and the possible mechanisms for the unwanted side reactions are discussed. It is also concluded that combination of sequential anionic polymerization and atomic transfer radical polymerization is not suitable for this system as polystyrene macroinitiator cannot initiate the polymerization of poly(2-(methoxyethoxy)ethyl methacrylate).

The Effect of the Temperature and Moisture to the Permeation Properties of PEO-Based Membranes for Carbon-Dioxide Separation.

An increased demand for energy in recent decades has caused an increase in the emissions of combustion products, among which carbon-dioxide is the most harmful. As carbon-dioxide induces negative environmental effects, like global warming and the greenhouse effect, a decrease of the carbon-dioxide emission has emerged as one of the most urgent tasks in engineering. In this work, the possibility for the application of the polymer-based, dense, mixed matrix membranes for flue gas treatment was tested. The task was to test a potential decrease in the permeability and selectivity of a mixed-matrix membrane in the presence of moisture and at elevated temperature. Membranes are based on two different poly(ethylene oxide)-based polymers filled with two different zeolite powders (ITR and IWS). An additive of detergent type was added to improve the contact properties between the zeolite and polymer matrix. The measurements were performed at three different temperatures (30, 60, and 90 °C) under wet conditions, with partial pressure of the water equal to the vapor pressure of the water at the given temperature. The permeability of carbon-dioxide, hydrogen, nitrogen, and oxygen was measured, and the selectivity of the carbon-dioxide versus other gases was determined. Obtained results have shown that an increase of temperature and partial pressure of the vapor slightly increase both the selectivity and permeability of the synthesized membranes. It was also shown that the addition of the zeolite powder increases the permeability of carbon-dioxide while maintaining the selectivity, compared to hydrogen, oxygen, and nitrogen.