Marko Rogošić

Polydispersity index and molecular weight distributions of polymers

The polydispersity index as a measure of the width of molecular weight distributions (MWD) is theoretically very important. However, considering MWDs of real polymers, its importance is partly lost. Consequently, several problems arise. The most common one is the misinterpretation of polydispersity index as a single and unique measure of the MWD width. In this work, the origin and solutions of the problem are clearly described via several theoretical distribution functions, i.e. those which are most widely applied for the description of polymer MWDs. The conclusions are confirmed by the analysis of MWDs of real samples, namely styrene-acrylonitrile copolymers. In addition, a complete procedure for the MWD data analysis is presented. Such a procedure is necessary for the correct interpretation and comparison of different, experimentally obtained, molecular weight distributions of polymers.

Thermal Stability of Lubricating Oil Additives Based on Styrene and n-Alkyl Methacrylate Terpolymers

In this work the thermal stability of polymeric additives for the improvement of rheological behavior of mineral lubricating oils was investigated. The systems studied comprised methyl methacrylate (MMA)/dodecyl methacrylate (DDMA)/octadecyl methacrylate (ODMA) and styrene (Sty)/DDMA/ODMA terpolymers. The composition of the terpolymers was determined by the 1H nuclear magnetic resonance spectroscopy and molar mass distribution by the size exclusion chromatography. The thermal degradation of terpolymers was studied by the thermogravimetric analysis. Sty/DDMA/ODMA terpolymers exhibited an improved thermal stability in comparison with MMA/DDMA/ODMA terpolymers of the corresponding compositions. Thus, the temperatures of 50% weight loss were found to be 313°C and 363°C for MMA terpolymer and Sty terpolymer, respectively, where x (MMA) = x (Sty) = 30 mol%.

Dilute solution properties of cellulose diacetate in mixed solvents

In this paper the dilute solution properties of cellulose diacetate (CDA) in single solvents (acetone (A), N,N-dimethylformamide (DMF)) and in mixed solvents (acetone with DMF, N-methyl formamide (NMF) and formamide (F), respectively) were investigated by the low-angle laser light scattering method, as well as by the dilute solution viscometry method. The main experimental results comprised apparent weight average molar mass values, M2∗ and limiting viscosity number values, [η]H. The results were interpreted in terms of specific interactions of solution components. Basic solvents (acetone and DMF) primarily solvate hydroxyl groups of CDA and both the intensity and the direction of preferential solvation are determined by the preferred heteroassociation of solvent molecules (dipole interactions). Acidic solvents (formamide, NMF) primarily solvate acetyl groups of CDA. In this case the preferential solvation behaviour is determined by the autoassociation properties of acidic solvents (H-bonding). The autoassociaton of acidic solvent molecules is responsible for the intramolecular and intermolecular bridging of CDA-molecules as well. This phenomenon gives rise to the structuring of more concentrated CDA-solutions, which is of key importance when technical CDA-membranes are to be prepared. Within the framework of this paper it is proved that preliminary studies of the structuring of CDA-solutions may be performed in dilute solutions. On the basis of preliminary studies a suitable range of solvent compositions may be chosen and necessary but rather complex investigations in mid-concentration range may then be performed only in that, quite restricted composition range.

Prediction of copolymer miscibility by the viscometric method

Abstract The theoretical aspects of the polymer miscibility study by the viscometric method are reviewed. The miscibility criteria of Bohmer as well as of Opalicki and Mencer are modified by incorporating the Catsiff and Hewett definition of ideal behaviour instead of that of Krigbaum and Wall, and thus novel miscibility criterion variables are defined. Furthermore, a viscometric study was used to estimate the miscibility limit of copolymers of styrene and acrylonitrile differing in composition. Problems concerning the transfer of results and conclusions from diluted ternary polymer solutions (polymer 1 + polymer 2 + solvent) to the solid polymer blends (polymer 1 + polymer 2) are recognized and discussed.

An electron spin resonance study of molecular dynamics and heterogeneity in the styrene-acrylonitrile copolymers

Abstract The spin-probed copolymers of styrene and acrylonitrile differing in monomer content and chain structure were examined by the electron spin resonance (e.s.r.) methods in a wide temperature range. The measurements of spin–lattice relaxation time, T1DM, in slow motional region by the double modulation e.s.r. technique reveal two distinct relaxation regions in copolymers and pure polyacrylonitrile matrices. At very low temperatures (region I) a very distinct difference between T1DM values of pure polyacrylonitrile, alternating and statistical copolymers and polystyrene-rich copolymer is ascribed to the effect of packing density of polymer chains. The density modulates the electron spin–lattice relaxation rates and other vibrational modes contributing to the total relaxation rate. At higher temperatures (region II), where the skeleton of a spin probe begins to move, alternating and statistical copolymers of a similar composition show different relaxation rate. The data are reconciled on the basis of the sequence structure of copolymer chains. The difference in glass transition temperature between alternating and statistical copolymers as determined by the e.s.r. measurements reflects local motional heterogeneity as a consequence of chain microstructure. The presence of local motional heterogeneity of a probe near and above the glass transition results from the styrene-rich sequences contributing to the structural heterogeneity in statistical copolymer.

Application of Different Artificial Neural Networks Retention Models for Multi-Criteria Decision-Making Optimization in Gradient Ion Chromatography

In this work, the principles of multi-criteria decision-making were used to develop an efficient optimization strategy in gradient elution ion chromatographic analysis. Two different artificial neural network retention models (multi-layer perceptron and radial basis function), three different separation criterion functions (chromatography response function, separation factor product and normalized retention difference product), and four different robustness criterion functions (CR1-CR4) were examined. The shape of the calculated separation vs the robustness response surface was used as principal criterion. Analysis time and minimum separation of adjacent peaks were additional criteria. The results showed that the radial basis artificial neural network retention model in combination with normalized retention difference product separation criterion function and CR3 robustness criterion function provided the optimal gradient ion chromatographic analysis.

Application of a Gradient Retention Model Developed by Using Isocratic Data for the Prediction of Retention, Resolution, and Peak Asymmetry in Ion Chromatography

Abstract In this work a model was developed for the prediction of retention time, resolution, and peak asymmetry in gradient elution mode by using isocratic experimental data. The predictive performance and generalization ability of the developed model was extensively tested by using an external experimental data set. The analysis of errors was performed in order to discuss and explain characteristics of the model. It was shown that the model performed satisfactorily and that it could be used for a modeling procedure in the optimization part of the ion chromatography method development.

Evaluation of separation in gradient elution ion chromatography by combining several retention models and objective functions.

In this work, three different methods for modeling of gradient retention were combined with several optimization objective functions in order to find the most appropriate combination to be applied in ion chromatography method development. The system studied was a set of seven inorganic anions (fluoride, chloride, nitrite, sulfate, bromide, nitrate, and phosphate) with a KOH eluent. The retention modeling methods tested were multilayer perceptron artificial neural network (MLP-ANN), radial-basis function artificial neural network (RBF-ANN), and retention model based on transfer of data from isocratic to gradient elution mode. It was shown that MLP retention model in combination with the objective function based on normalized retention difference product was the most adequate tool for optimization purposes.

Separation of Hydrocarbons by Means of Liquid- Liquid Extraction with Deep Eutectic Solvents

Abstract Liquid-liquid equilibria for six ternary systems with choline chloride urea or choline chloride glycerol (molar ratio, 1:2) as selective solvent were experimentally determined at atmospheric pressure and 25°C. Equilibrium data were presented with tie lines. Extraction experiments with three-component systems were performed. The suitability of deep eutectic solvents for the separation of pyridine and toluene from n-hexane, and n-butanol from toluene was evaluated in terms of properties of solvents, solute distribution ratio, and extraction efficiency. Choline chloride glycerol has a better potential for separation of pyridine from its mixture with n-hexane. The equilibrium data were well described with the NRTL model.

Prediction of the chromatographic signal in gradient elution ion chromatography

This study describes the development of a signal prediction model in gradient elution ion chromatography. The proposed model is based on a retention model and generalized logistic peak shape function which guarantees simplicity of the model and its easy implementation in method development process. Extensive analysis of the model predictive ability has been performed for ion chromatographic determination of bromate, nitrite, bromide, iodide, and perchlorate, using KOH solutions as eluent. The developed model shows good predictive ability (average relative error of gradient predictions 1.94%). The developed model offers short calculation times as well as low experimental effort (only nine isocratic runs are used for modeling).