Characterization of Modified PVDF Membranes Using Fourier Transform Infrared and Raman Microscopy and Infrared Nanoimaging: Challenges and Advantages of Individual Methods.

Polymer materials are integral to diverse scientific fields, including chemical engineering and biochemical research, as well as analytical and physical chemistry. This study focuses on the characterization of modified poly(vinylidene fluoride) (PVDF) membranes from both physical and chemical perspectives. Unfortunately, current surface characterization methods face various challenges when simultaneously measuring diverse material properties such as morphology and chemical composition. Addressing this issue, we introduce infrared scattering scanning near-field optical microscopy (IR-sSNOM), a modern technique with the ability to overcome limitations and provide simultaneous topographical, mechanical, and chemical information. We demonstrate the capabilities of IR-sSNOM for investigation of four samples of PVDF membranes modified with 2-(methacryloyloxyethyl)trimethylammonium iodide and/or methacryloyloxyethyl phosphorylcholine in various ratios. These membranes, desirable for their specific properties, represent a challenging material for analysis due to their thermal instability and mechanical vulnerability. Employing Fourier transform infrared (FTIR) microscopy, IR-sSNOM, and Raman microscopy, we successfully overcame these challenges by carefully selecting the experimental parameters and performing detailed characterization of the polymer samples. Valuable insights into morphological and chemical homogeneity, the abundance of modifying side chains, and the distribution of different crystal phases of PVDF were obtained. Most notably, the presence of modifying side chains was confirmed by FTIR microscopy, the Raman spectral mapping revealed the distribution of crystalline phases of the studied polymer, and the IR-sSNOM showed the abundance of chemically diverse aggregates on the surface of the membranes, thanks to the unique nanometer-scale resolution and chemical sensitivity of this technique. This comprehensive approach represents a powerful toolset for characterization of polymeric materials at the nano- and microscale. We believe that this methodology can be applied to similar samples, provided that their thermal stability is considered, opening avenues for detailed exploration of physical and chemical properties in various scientific applications.

Unveiling the Crucial Role of Chemical Enhancement in the SERS Analysis of Amphetamine-Metal Interactions on Gold and Silver Surfaces: Importance of Selective Amplification of the Narrow Interval of Vibrational Modes.

The use of addictive substances, including drugs, poses significant health risks and contributes to various social problems, such as increased crime rates associated with substance-induced aggressive behavior. To address these challenges, possession of addictive substances is legally prohibited. However, detecting and analyzing these substances remain a complex task for law enforcement, primarily due to the presence of adulterants or limited sample quantities. In response to the evolving illicit market, continuous development and adaptation of analytical techniques are essential. One approach is the utilization of surface-enhanced Raman scattering (SERS) spectroscopy, which involves adsorbing the analyte onto nanostructured plasmonic surfaces. This study explores the potential of SERS in detecting amphetamine-based addictive stimulants with a specific focus on the properties of enhancing surfaces chosen. Comparative investigations were performed using silver and gold surfaces, with gold colloidal systems demonstrating a favorable performance. Moreover, to provide a comprehensive interpretation of the measured spectra, extensive density functional theory (DFT) calculations were conducted, allowing for a deeper understanding of the observed spectral features and molecular interactions with the metal surfaces. This review presents insights into the role of chemical enhancement in SERS analysis of amphetamine-metal interactions, shedding light on the selective amplification of vibrational modes. These findings, supported by DFT calculations, have implications in the fields of spectroscopy, physical chemistry, and drug analysis, providing valuable contributions to forensic applications and a deeper understanding of chemical enhancement phenomena. We present the impact of the secondary resonances of Stokes-scattered photons. This illustrates the significance of recognizing the constraints of the frequently employed "E4" approximation, even in measurements involving multiple molecules rather than single molecules.

Laser-Induced Reactions of 4-Aminobenzenthiol Species Adsorbed on Ag, Au, and Cu Plasmonic Structures Followed by SERS Spectroscopy. The Role of Substrate and Excitation Energy - Surface-Complex Photochemistry and Plasmonic Catalysis.

This study focuses on investigating the laser-induced reactions of various surface complexes of 4-aminobenzenethiol on Ag, Au, and Cu surfaces. By utilizing different excitation wavelengths, the distinct behavior of the molecule species on the plasmonic substrates was observed. Density functional theory (DFT) calculations were employed to establish the significant role of chemical enhancement mechanisms in determining the observed behavior. The interaction between 4-aminobenzenethiol (4-ABT) molecules and plasmonic surfaces led to the formation of surface complexes with absorption bands red-shifted into the visible and near-infrared regions. Photochemical transformations were induced by excitation wavelengths from these regions, with the nature of the transformations varying based on the excitation wavelength and the plasmonic metal. Resonance with the electronic absorption transitions of these complexes amplifies surface-enhanced Raman scattering (SERS), enabling the detailed examination of ongoing processes. A kinetic study on the Ag surface revealed processes governed by both first- and second-order kinetics, attributed to the dimerization process and transformation processes of individual molecules interacting with photons or plasmons. The behavior of the molecules was found to be primarily determined by the position and variability of the band between 1170 and 1190 cm-1, with the former corresponding to molecules in the monomer state and the latter to dimerized molecules. Notably, laser-induced dimerization occurred most rapidly on the Cu surface, followed by Ag, and least on Au. These findings highlight the influence of plasmonic surfaces on molecular behavior and provide insights into the potential applications of laser-induced reactions for surface analysis and manipulation.

Intelligent Modelling of the Real Dynamic Viscosity of Rubber Blends Using Parallel Computing.

Modelling the flow properties of rubber blends makes it possible to predict their rheological behaviour during the processing and production of rubber-based products. As the nonlinear nature of such complex processes complicates the creation of exact analytical models, it is appropriate to use artificial intelligence tools in this modelling. The present study was implemented to develop a highly efficient artificial neural network model, optimised using a novel training algorithm with fast parallel computing to predict the results of rheological tests of rubber blends performed under different conditions. A series of 120 real dynamic viscosity-time curves, acquired by a rubber process analyser for styrene-butadiene rubber blends with varying carbon black contents vulcanised at different temperatures, were analysed using a Generalised Regression Neural Network. The model was optimised by limiting the fitting error of the training dataset to a pre-specified value of less than 1%. All repeated calculations were made via parallel computing with multiple computer cores, which significantly reduces the total computation time. An excellent agreement between the predicted and measured generalisation data was found, with an error of less than 4.7%, confirming the high generalisation performance of the newly developed model.

Structural and Mechanical Changes of AlMgSi0.5 Alloy during Extrusion by ECAP Method.

SPD (several plastic deformations) methods make it possible to obtain an ultrafine-grained structure (UFG) in larger volumes of material and thus improve its mechanical properties. The presented work focuses on the structural and mechanical changes of aluminium alloy AlMgSi0.5 (EN AW 6060) during processing by repeated extrusion through the ECAP rectangular channel. After a four-pass extrusion, the samples' microstructures were observed using an optical microscope, where refinement of the material grains was confirmed. Tensile tests determined the extrusion forces and allowed interpretation of the changes in the mechanical properties of the stressed alloy. The grain size was refined from 28.90 µm to 4.63 µm. A significant improvement in the strength of the material (by 45%) and a significant deterioration in ductility (to 60%) immediately after the first extrusion was confirmed. The third pass through the die appeared to be optimal for the chosen deformation path, while after the fourth pass, micro-cracks appeared, significantly reducing the strength of the material. Based on the measurement results, new analytical equations were formulated to predict the magnitude or intensity of the volumetric and shape deformations of the structural grain size and, in particular, the adequate increase in the strength and yield point of the material.

A Generalized Regression Neural Network Model for Predicting the Curing Characteristics of Carbon Black-Filled Rubber Blends.

In this study, a new generalized regression neural network model for predicting the curing characteristics of rubber blends with different contents of carbon black filler cured at various temperatures is proposed for the first time The carbon black contents in the rubber blend and cure temperature were used as input parameters, while the minimum and maximum elastic torque, scorch time, and optimal cure time, obtained from the analysis of 11 rheological cure curves registered at 10 various temperatures, were considered as output parameters of the model. A special pre-processing procedure of the experimental input and target data and the training algorithm is described. Less than 55% of the experimental data were used to significantly reduce the total number of input and target data points needed for training the model. Satisfactory agreement between the predicted and experimental data, with a maximum error in the prediction not exceeding 5%, was found. It is concluded that the generalized regression neural network is a powerful tool for intelligently modelling the curing process of rubber blends even in the case of a small dataset, and it can find a wide range of practical applications in the rubber industry.

A New Method of Predicting the Structural and Mechanical Change of Materials during Extrusion by the Method of Multiple Plastic Deformations.

The formulation of the Hall-Petch relationship in the early 1950s has raised immense interest in studying the influence of the grain size of solid materials on their properties. Grain refinement can be achieved through extreme deformation. In the presented study, Equal-Channel Angular Pressing (ECAP) was successfully applied to produce an ultrafine-grained microstructure in a pure commercial Cu of 99.9 wt%. Samples were processed by ECAP at 21 °C for six passes via route A. A new equation of equilibrium that allows the exact determination of the number of extrusions and other technological parameters required to achieve the desired final grain size has been developed. The presented research also deals, in a relatively detailed and comparative way, with the use of ultrasound. In this context, a very close correlation between the process functions of extrusion and the speed of longitudinal ultrasonic waves was confirmed.

Modelling the Stiffness-Temperature Dependence of Resin-Rubber Blends Cured by High-Energy Electron Beam Radiation Using Global Search Genetic Algorithm.

Modelling the influence of high-energy ionising radiation on the properties of materials with polymeric matrix using advanced artificial intelligence tools plays an important role in the research and development of new materials for various industrial applications. It also applies to effective modification of existing materials based on polymer matrices to achieve the desired properties. In the presented work, the effects of high-energy electron beam radiation with various doses on the dynamic mechanical properties of melamine resin, phenol-formaldehyde resin, and nitrile rubber blend have been studied over a wide temperature range. A new stiffness-temperature model based on Weibull statistics of the secondary bonds breaking during the relaxation transitions has been developed to quantitatively describe changes in the storage modulus with temperature and applied radiation dose until the onset of the temperature of the additional, thermally-induced polymerisation reactions. A global search real-coded genetic algorithm has been successfully applied to optimise the parameters of the developed model by minimising the sum-squared error. An excellent agreement between the modelled and experimental data has been found.

Radial Basis Function Neural Network-Based Modeling of the Dynamic Thermo-Mechanical Response and Damping Behavior of Thermoplastic Elastomer Systems.

The presented work deals with the creation of a new radial basis function artificial neural network-based model of dynamic thermo-mechanical response and damping behavior of thermoplastic elastomers in the whole temperature interval of their entire lifetime and a wide frequency range of dynamic mechanical loading. The created model is based on experimental results of dynamic mechanical analysis of the widely used thermoplastic polyurethane, which is one of the typical representatives of thermoplastic elastomers. Verification and testing of the well-trained radial basis function neural network for temperature and frequency dependence of dynamic storage modulus, loss modulus, as well as loss tangent prediction showed excellent correspondence between experimental and modeled data, including all relaxation events observed in the polymeric material under study throughout the monitored temperature and frequency interval. The radial basis function artificial neural network has been confirmed to be an exceptionally high-performance artificial intelligence tool of soft computing for the effective predicting of short-term viscoelastic behavior of thermoplastic elastomer systems based on experimental results of dynamic mechanical analysis.

The Effect of High-Energy Ionizing Radiation on the Mechanical Properties of a Melamine Resin, Phenol-Formaldehyde Resin, and Nitrile Rubber Blend.

Irradiation by ionizing radiation is a specific type of controllable modification of the physical and chemical properties of a wide range of polymers, which is, in comparison to traditional chemical methods, rapid, non-polluting, simple, and relatively cheap. In the presented paper, the influence of high-energy ionizing radiation on the basic mechanical properties of the melamine resin, phenol-formaldehyde resin, and nitrile rubber blend has been studied for the first time. The mechanical properties of irradiated samples were compared to those of non-irradiated materials. It was found that radiation doses up to 150 kGy improved the mechanical properties of the tested materials in terms of a significant increase in stress at break, tensile strength, and tensile modulus at 40% strain, while decreasing the value of strain at break. At radiation doses above 150 kGy, the irradiated polymer blend is already degrading, and its tensile characteristics significantly deteriorate. An radiation dose of 150 kGy thus appears to be optimal from the viewpoint of achieving significant improvement, and the radiation treatment of the given polymeric blend by a beam of accelerated electrons is a very promising alternative to the traditional chemical mode of treatment which impacts the environment.

Prediction of the Tensile Response of Carbon Black Filled Rubber Blends by Artificial Neural Network.

The precise experimental estimation of mechanical properties of rubber blends can be a very costly and time-consuming process. The present work explores the possibilities of increasing its efficiency by using artificial neural networks to study the mechanical behavior of these widely used materials. A multilayer feed-forward back-propagation artificial neural network model, with a strain and the carbon black content as input parameters and stress as an output parameter, has been developed to predict the uniaxial tensile response of vulcanized natural rubber blends with different contents of carbon black in the form of engineering stress-strain curves. A novel procedure has been created for the simulation of the optimized artificial neural network model with input datasets generated by a regression model of an experimental dependence of tensile strain-at-break on the carbon black content in the investigated blends. Errors of the prediction of experimental stress-strain curves, as well as of tensile strain-at-break, tensile stress-at-break and M100 tensile modulus were estimated for all simulated stress-strain curves. The present study demonstrated that the performance of a developed neural network model to predict the stress-strain curves of rubber blends with different contents of carbon black is also exceptionally high in the case of a network that had never learned the input data, which makes it a suitable tool for extensive use in practice.

Identification of Upper and Lower Level Yield Strength in Materials.

This work evaluates the possibility of identifying mechanical parameters, especially upper and lower yield points, by the analytical processing of specific elements of the topography of surfaces generated with abrasive waterjet technology. We developed a new system of equations, which are connected with each other in such a way that the result of a calculation is a comprehensive mathematical-physical model, which describes numerically as well as graphically the deformation process of material cutting using an abrasive waterjet. The results of our model have been successfully checked against those obtained by means of a tensile test. The main prospect for future applications of the method presented in this article concerns the identification of mechanical parameters associated with the prediction of material behavior. The findings of this study can contribute to a more detailed understanding of the relationships: material properties-tool properties-deformation properties.

Modeling the Temperature Dependence of Dynamic Mechanical Properties and Visco-Elastic Behavior of Thermoplastic Polyurethane Using Artificial Neural Network.

This paper presents one of the soft computing methods, specifically the artificial neural network technique, that has been used to model the temperature dependence of dynamic mechanical properties and visco-elastic behavior of widely exploited thermoplastic polyurethane over the wide range of temperatures. It is very complex and commonly a highly non-linear problem with no easy analytical methods to predict them directly and accurately in practice. Variations of the storage modulus, loss modulus, and the damping factor with temperature were obtained from the dynamic mechanical analysis tests across transition temperatures at constant single frequency of dynamic mechanical loading. Based on dynamic mechanical analysis experiments, temperature dependent values of both dynamic moduli and damping factor were calculated by three models of well-trained multi-layer feed-forward back-propagation artificial neural network. The excellent agreement between the modeled and experimental data has been found over the entire investigated temperature interval, including all of the observed relaxation transitions. The multi-layer feed-forward back-propagation artificial neural network has been confirmed to be a very effective artificial intelligence tool for the modeling of dynamic mechanical properties and for the prediction of visco-elastic behavior of tested thermoplastic polyurethane in the whole temperature range of its service life.