Solubility Parameters Of Polymers Research Articles

Revisiting experimental techniques and theoretical models for estimating the solubility parameter of rubbery and glassy polymer membranes

Estimation and correlation of the Hildebrand solubility parameter (δ) of polymers and small molecules is a common practice in membrane material science and is accomplished by experimental and numerical routes. In this paper, we revisit, update, and compare both routes to enhance the accuracy in the determination of δ. Best practices for the experimental determination of polymer solubility parameters are provided, and the viability of Dynamic Light Scattering (DLS) was demonstrated as an alternative to conventional time- and material-consuming techniques, such as Ubbelohde viscometry and swelling measurements. Glassy and rubbery polymers, including high fractional free volume (FFV) microporous polymers such as PIM-1 and poly(1-trimethylsilyl-1-propyne) (PTMSP), are among the samples included in this study with great relevance to membrane science. In an attempt to enhance the accuracy of numerical estimate of polymer solubility parameters via the group contribution method, we provide updated group contribution parameters, along with their uncertainty, according to the technique recently reported by Smith et al. These updated group contribution parameters result in a mean absolute relative error of 9.0% in predicting the solubility parameter on a test set of 40 polymers, which is on par with the average 10% error reported previously. We also show, using machine learning techniques, that augmenting the group contribution model with extra parameters or non-linear relationships does not improve its accuracy. Results of the updated group contribution technique and dynamic light scattering measurements were compared to experimental viscometry on four test polymers, and the difference between the three techniques is compared.

A simple correlation for reliable prediction of intrinsic viscosity (limiting viscosity number) of different polymer-solvent combinations

Intrinsic viscosity (limiting viscosity number) of a dilute polymer solution is one of the most fundamental properties of polymers for solubility parameters calculation, determining the molecular weight, size, and topological structure of polymers, and other physicochemical property characterization. The purpose of this work is to introduce a core correlation for predicting intrinsic viscosity of different polymer-solvent combinations from only the structure of the repeating unit structure of polymer and solubility parameter of polymer. Model's reliability can be enhanced by considering two correcting functions based on the effective dispersion, polar, and hydrogen bonding components of solubility for some solvents. The largest available experimental data including intrinsic viscosity of 74 polymer-solvent combinations are used to derive and test the improved model. The predicted intrinsic viscosity of the new model show higher reliability as compared with the results of one empirical and two quantitative structure-property relationships (QSPR) approaches for 65 polymer-solvent combinations. The value of the Root Mean Square Error (RMSE) of the improved model is 24.05 cm3/g, which is less than three comparative models, i.e. 86.08, 37.79, and 34.63 cm3/g. The new method also gives good results as compared to two further complex QSPR models for nine extra polymer-solvent combinations. Moreover, different statistical parameters confirm excellent reliability of the improved correlation as compared to the best accessible predictive methods.

Simple Approach for Reliable Prediction of Solubility of Polymers in Environmentally Compatible Solvents

This work presents a simple approach for predicting the solubility parameter (δ) of polymers. It is based on the number of moles of hydrogen, nitrogen, oxygen, sulfur, fluorine, and silicon atoms per mole of repeating unit structures as well as two correcting functions based on the contributions of hydrogen bonding and polar groups. The largest available experimental data including the δ values of 163 amorphous polymers and amorphous phases of semicrystalline polymers are used to derive and test the new model. Different statistical parameters are used to confirm the high reliability of the novel method through internal and external validations as well as by comparing with two of the best existing quantitative structure–property relationship (QSPR) approaches containing complex descriptors. Since the available QSPR methods are restricted to polymers with repeating unit structures −(C1H2–C2R3R4)–, the results of the new method are compared with the outputs of QSPR methods for 97 polymers. The value of root-mean-square error (RMSE) of the novel approach for external validation of test data (57 different polymers) is 0.91 MPa1/2, which is close to RMSE values of the computed outputs of two complex QSPR methods, that is, 0.90 and 0.88 MPa1/2.

Atomistic Descriptors for Machine Learning Models of Solubility Parameters for Small Molecules and Polymers.

Descriptors derived from atomic structure and quantum chemical calculations for small molecules representing polymer repeat elements were evaluated for machine learning models to predict the Hildebrand solubility parameters of the corresponding polymers. Since reliable cohesive energy density data and solubility parameters for polymers are difficult to obtain, the experimental heat of vaporization of a set of small molecules was used as a proxy property to evaluate the descriptors. Using the atomistic descriptors, the multilinear regression model showed good accuracy in predicting of the small-molecule set, with a mean absolute error of 2.63 kJ/mol for training and 3.61 kJ/mol for cross-validation. Kernel ridge regression showed similar performance for the small-molecule training set but slightly worse accuracy for the prediction of of molecules representing repeating polymer elements. The Hildebrand solubility parameters of the polymers derived from the atomistic descriptors of the repeating polymer elements showed good correlation with values from the CROW polymer database.

Design of Over-1000 nm Near-Infrared Fluorescent Polymeric Micellar Nanoparticles by Matching the Solubility Parameter of the Core Polymer and Dye.

Polymeric micellar nanoparticles (PNPs) encapsulating over-thousand-nanometer (OTN) near-infrared (NIR) fluorescent dye molecules in block polymers having hydrophobic and hydrophilic chains are promising agents for the dynamic imaging of deep tissue. To achieve OTN-NIR fluorescent PNPs (OTN-PNPs) having high brightness, it is crucial to increase the affinity between the core polymer and dye molecules by matching their polarities; thus, criteria and methods to evaluate the affinity are required. In this study, we used the Hansen solubility parameter (HSP), including the polarity term, to evaluate the affinity between the two substances. HSP values of the OTN-NIR fluorescent dye IR-1061 and four core polymers, poly(lactic-co-glycolic acid) (PLGA), poly(lactic acid) (PLA), poly(ε-caprolactone) (PCL), and polystyrene (PSt), were calculated using the Hansen solubility sphere method and molecular group contribution method, respectively. The relative energy density between IR-1061 and each core polymer calculated using their HSP values revealed that the affinities of PLGA and PLA for IR-1061 are higher than those of PCL and PSt. Therefore, OTN-PNPs composed of PLGA, PLA, and PCL core polymers were prepared and compared. The OTN-PNPs having PLGA and PLA cores could be loaded with larger amounts of IR-1061, had higher photoluminescence intensities, and showed higher stability in phosphate buffered saline than those having PCL cores. Moreover, the OTN-PNPs having PLGA or PLA cores were used for the dynamic imaging of live mice. Thus, matching the solubility parameters of the core polymer and dye molecule is a useful approach for designing high-performance OTN-NIR fluorescent probes.

Compatibility between Drugs and Polymer in Nanoparticles Produced by the Miniemulsion‐Solvent Evaporation Technique

AbstractEncapsulation of poorly soluble drugs in polymer nanoparticles is a common strategy to increase bioavailability of drugs. The miniemulsion‐solvent evaporation technique is widely used for encapsulating drugs in polymer nanoparticles because it is a versatile process, allowing many drug–polymer pair combinations. However, above a critical concentration of drug, which depends on the drug and polymer, nanoparticles tend to precipitate. Herein, the importance of drug solubility and miscibility in the polymer phase for selecting the optimal polymer matrix is investigated. Ibuprofen, naproxen methyl ester, and naproxen, as models for poorly soluble drugs, are encapsulated with various loadings in polycaprolactone nanoparticles by the miniemulsion‐solvent evaporation method. The miscibility between drug and polymer is estimated by calculating Flory–Huggins interaction parameters (χ) from differential scanning calorimetry measurements and calculating the difference in Hansen solubility parameter of drugs and polymer. Both values can be used for determining the feasibility of the drug encapsulation in polymer nanoparticles.

Mathematical model of adhesion junction of layers during coextrusion

Nine theories of polymer adhesion during coextrusion are researched. Two approaches to describing adhesion are presented – thermodynamic and molecular-kinetic ones. The factors, determining the durability of adhesion junction, are analyzed. The stages of adhesion connection formation are presented. The adhesion junction emergence model as a result of intermolecular interaction is described. As a result of calculations the following dependencies are got: the dependence of ply separation energy on the contact length of polyisobutylene adhesion junction of different molecular weight, the dependence of ply separation energy on molecular weight of polyisobutylene adhesion junctions got during different time, the dependence of adhesion durability on squared difference of polymer solubility parameters, the dependence of interdiffused zone width in the time.

The role of porogen-polymer complexation in atrazine imprinted polymer to work as an electrochemical sensor in water

An electrochemical sensor was successfully assembled by adopting the optimum molecularly imprinted polymers together with an electrochemical system. The sensor utilizes the molecular imprinted polymer (MIP) synthesized for atrazine detection in aqueous solutions. The effect of three different co-porogen in the polymer formulation were evaluated for atrazine binding capacity, imprinting factor and by using scanning electron microscopy (SEM). The imprinting factor for the polymer was the highest for the smallest difference of the polymer solubility parameter with the porogen mixture. Among all polymers, the polymer with 10% of DMSO gives the highest imprinting factor of 1.18 and binding capacity of 0.016 mmol.g−1 of polymer. The optimum formulation of imprinted polymer was assembled with graphite felt electrode as the transduction for electrochemical analysis. Electrochemical impedance spectrum (EIS) was used to characterize the sensor and investigate the electrochemical response of the sensor. An equivalent circuit was suggested to quantitatively analyze each component of the sensor system. All EIS curves fit well with three different circuits corresponding to the presence or absent of PVC and polymers in the blend, as well as the presence of the atrazine in the electrolyte solution with the accuracy of the fits χ2 value between 0.6298 and 1.475. The variation of individual values of parameters deduced from the impedance fits were scrutinized.

Conductive graphite nanoplatelets (GNPs)/polyethersulfone (PES) composites with inter-connective porous structure for chemical vapor sensing

Conductive polymer composites (CPCs) are good candidates as chemical vapor sensors. However, it is still challenging to develop CPC-based vapor sensors with low percolation thresholds and excellent sensing property (for example, high response intensity, low detection limit and good recyclability). Herein, electrically conductive and porous graphite nanoplatelets (GNPs)/polyethersulfone (PES) composites were prepared using vapor induced phase separation (VIPS) and freeze drying. VIPS was not only responsible for forming the inter-connective porous structure but also promoting the distribution of GNPs on the surfaces of PES skeletons, yielding a low percolation threshold of 0.52 wt%. When the conductive composites were used to detect chemical vapors, the response rate and vapor sensing intensity were controlled by the vapor pressure and the solubility parameters of solvent and polymer. It was found that the response intensities of the composites for saturated vapors of dichloromethane, acetone, tetrahydrofuran and ethanol were ~8.4 × 103, 5.6 × 102, 10.3 and 3.5, respectively. The detection limit for acetone vapor was as low as 30 ppm. Excellent recyclability for the conductive composites as a chemical vapor sensor was also achieved.

Critical Assessment of the Hildebrand and Hansen Solubility Parameters for Polymers.

Solubility parameter models are widely used to select suitable solvents/nonsolvents for polymers in a variety of processing and engineering applications. In this study, we focus on two well-established models, namely, the Hildebrand and Hansen solubility parameter models. Both models are built on the basis of the notion of "like dissolves like" and identify a liquid as a good solvent for a polymer if the solubility parameters of the liquid and the polymer are close to each other. Here we make a critical and quantitative assessment of the accuracy/utility of these two models by comparing their predictions against actual experimental data. Using a data set of 75 polymers, we find that the Hildebrand model displays a predictive accuracy of 60% for solvents and 76% for nonsolvents. The Hansen model leads to a similar performance; on the basis of a data set of 25 polymers for which Hansen parameters are available, we find that it has an accuracy of 67% for solvents and 76% for nonsolvents. The availability of the Hildebrand parameters for a large polymer data set makes it a widely applicable capability, as the Hildebrand parameter for a new polymer may be determined using this data set and machine learning methods as we have done before; the predicted Hildebrand parameter for a new polymer may then be used to determine suitable solvents and nonsolvents. Such predictions are difficult to make with the Hansen model, as the data set of Hansen parameters for polymers is rather small. Nevertheless, the Hildebrand approach must be used with caution. Our analysis shows that while the Hildebrand model has a predictive accuracy of 70-75% for nonpolar polymers, it performs rather poorly for polar polymers (with an accuracy of 57%). Going forward, determination of solvents and nonsolvents for polymers may benefit by developing classification models built directly on the basis of available experimental data sets rather than utilizing the solubility parameter approach, which is limited in versatility and accuracy.

Cohesive energy density and solubility parameter evolution during the curing of thermoset

This paper develops an approach for predicting cohesive energy density and solubility parameter of thermoset polymers directly based on atomistic simulations. The cohesive energy density and solubility parameter are obtained from the intermolecular nonbond energy, after excluding the intramolecular nonbond energy from the total nonbond energy by computationally characterizing the detailed network structure of a thermoset polymer. The effects of conversion degree and temperature on the cohesive energy density and solubility parameter are systematically investigated. It is concluded that a thermoset polymer with a higher conversion degree has a lower cohesive energy density and solubility parameter. The cohesive energy density shows a linearly decreasing relationship with the crossslink density. These properties also decrease with increasing temperature and thermoset polymers with lower conversion degrees show a more significant temperature effect.

Effect of API-Polymer Miscibility and Interaction on the Stabilization of Amorphous Solid Dispersion: A Molecular Simulation Study

In this study, a molecular dynamics simulation technique was employed to predict miscibility and interaction of Active Pharmaceutical Ingredient (API) with polymer carriers in solid dispersion system based on Hansen solubility parameter and hydrogen bond formation, respectively. Several APIs with and without hydrogen bonding tendency were studied. The Hansen solubility parameters of APIs and polymers calculated by molecular dynamic simulation were similar to reported values in the literature. Our simulation results were able to determine the interactions between APIs and various polymers (ionic and nonionic) and also predict the hydrogen bond interaction energy and hydrogen bond lifetime. The simulation results were verified by preparing solid dispersions using hot melt extrusion. As predicted by our simulation, clear and colorless extrudates were obtained for ibuprofen/PVP-VA 64, ibuprofen/Eudragit EPO, and fenofibrate/PVP-VA 64, which confirmed the miscibility between APIs (ibuprofen, fenofibrate) and p...

Investigation of polymer–solvent interactions in poly(styrene sulfonate) thin films

ABSTRACTThe swelling behavior of acid form poly(styrene sulfonate) (PSS‐H) thin films were investigated using in situ spectroscopic ellipsometry (SE) to probe the polymer–solvent interactions of ion‐containing polymers under interfacial confinement. The interaction parameter (χ), related to the polymer and solvent solubility parameters in the Flory–Huggins theory, describes the polymer‐solvent compatibility. In situ SE was used to measure the degree of polymer swelling in various solvent vapor environments, to determine χ for the solvent‐PSS‐H system. The calculated solubility parameter of 40–44 MPa1/2 for PSS‐H was determined through measured χ values in water, methanol, and formamide environments at a solvent vapor activity of 0.95. Flory–Huggins theory was applied to describe the thickness‐dependent swelling of PSS‐H and to quantify the water‐PSS‐H interactions. Confinement had a significant influence on polymer swelling at low water vapor activities expressed as an increased χ between the water and polymer with decreasing film thickness. As the volume fraction of water approached ∼0.3, the measured χ value was ∼0.65, indicating the water interacted with the polymer in a similar manner, regardless of thicknesses. © 2017 Wiley Periodicals, Inc. J. Polym. Sci., Part B: Polym. Phys. 2017, 55, 1365–1372

The effect of ethanol pre-treatment upon the mechanical, structural and surface modification of ultrafiltration membranes

ABSTRACTThe in situ ethanol pre-treatment of commercially available polysulfone (PSU) ultrafiltration (UF) membranes resulted in a threefold increase in the pure water flux (PWF) values achieved. Techniques that lead to an increase in flux are of both academic and commercial interest. It is postulated that the mechanisms for performance improvement can be attributed to swelling of membrane skin-layers, as demonstrated by changes in thickness measurements, and consideration of polymer solubility parameters, giving a degree of polymer plasticisation. The modification is accompanied by a hydrophobicity increase – this parameter is linked to a greater fouling tendency. Increases in hydrophobicity contrast with the usual effect of ethanol contact, by enhancing the removal of membrane preservatives and polyvinylpyrrolidone (PVP), a common pore-forming agent. Mechanical property changes were not readily detected, whilst the apparently unaltered sub-layer masked more subtle changes occurring within the dense skin-layer. Directing analysis specifically at the skin layer using colloidal AFM probes allowed a decoupling of changes against the support, showing that the elastic modulus was reduced as a consequence of PVP removal and plasticisation. Moreover, regional elasticity probing allowed observation of spatial inhomogeneities in elasticity, occurring due to the removal of the previously unevenly distributed PVP and leading to pitting. Consequently, the effects of pre-treatment with ethanol are shown to offer advantages by maximising the performance of commercial membranes, though such methods must be used with caution. Elasticity changes that occur may be detrimental to performance if carried out at high transmembrane pressures, where compaction could be assisted.

On accuracy of predicting densities and solubility parameters of polymers using atomistic simulations

Molecular dynamics simulation is performed to compute densities and solubility parameters of polymers. TEAM force field is validated on monomers and then employed for predictions for polymers built with different chain lengths. The calculated densities converge when the polymer chain lengths are >10 repeat units. With the consideration of polymer crystallinity, the predictions agree well with experimental data. The solubility parameters calculated from the cohesive energy densities exhibit systematical and progressive underestimates as the chain lengths increase, indicating the energy contribution from polymer chain conformation changes from bulk state to solution must be included in order to calculate the solubility parameters correctly. A model using the radiuses of gyration to represent the conformation changes is proposed to correct the systematical error.

Predicting miscibility in polymer blends using the Bagley plot: Blends with poly(ethylene oxide)

This paper explores the different methods currently available to obtain the partial solubility parameters of polymers in order to apply them for the prediction of miscibility in polymer/polymer blends. Blends based on poly(ethylene oxide) (PEO) have been chosen because its miscibility has been reported with a large number of polymeric counterparts (miscible or not), allowing the representation of its miscibility map with the required detail. The solubility parameters of PEO and 55 blending partners have been obtained using the most widely used group contribution (GC) methods, namely the ones proposed by Hoy, Hoftyzer-Van Krevelen, Stefanis-Panayiotou and Yamamoto (Y-MB), and have been used to build a Bagley plot. In addition, the miscibility map based on experimental data obtained from solubility tests has been also obtained and compared with the calculated ones. The clouds of points obtained around the point corresponding to PEO show different shapes, that can be used to obtain conclusions about the coherence and the accuracy of each of the different methods used to obtain the solubility parameters of polymers.

A New Determination Method of the Solubility Parameter of Polymer Based on AIE.

An accurate method of the fluorescence probe approach based on an aggregation-induced emission (AIE) molecule (tetraphenylethylene) for measuring the solubility parameter of the polymer is reported. This method is distinctive in that the approach can make the polymer chain conformation in solution be related to the fluorescence intensity. Since the solubility parameter of the polymer is also closely linked to its chain conformation in solution, the solubility parameter can be determined by the fluorescence intensity. The range of the solubility parameter of polymethyl methacrylate (PMMA) tested by this method was from 9.00 cal1/2cm−3/2 to 10.00 cal1/2cm−3/2. The results are more accurate than those obtained from the traditional turbidimetric titration method, ranging from 8.60 cal1/2cm−3/2 to 12.15 cal1/2cm−3/2. According to the photoluminescence (PL) intensities spectra, the solubility parameters of PMMA and polyvinyl acetate (PVAc) are 9.19 cal1/2cm−3/2 and 9.85 cal1/2cm−3/2, respectively.

Inverse gas chromatography study of polyvinylacetate-solvent and polyethylene-solvent systems

In this article, the thermodynamic behavior of polyvinylacetate (PVAc)–solvent, and polyethylene (PE)–solvent mixtures have been studied by determining the thermodynamic sorption parameters (enthalpy, entropy, and free energy), the mass-based solvent activity coefficients (Ω) and the Flory Huggins parameters (χ), by means of inverse gas chromatography (IGC) measurements. According to the Flory Huggins parameters of the PE–solvent mixtures, determined between 40 and 60°C the compatibility (the ability to interact with each other) of this polymer with the different types of solvents follows this order: dispersion solvents > polar solvents > association solvents. In the case of PVAc mixtures, the thermodynamic parameters were determined between 60 and 80°C, only for polar-type and association-type solvents due to, in the studied temperature range, the retention diagrams of dispersion solvents show that there are not bulk interactions. The Hildebrand solubility parameters of both polymers were also determined, according to Guillet procedure. The higher values of PVAc material (14.1 MPa0.5 for PE and 19.8 MPa0.5 for PVAc, at 60°C) are related to the strong interactions of vinyl acetate monomer. POLYM. ENG. SCI., 56:36–43, 2016. © 2015 Society of Plastics Engineers

A genetic programming-based QSPR model for predicting solubility parameters of polymers

In this study, linear and nonlinear quantitative structure-property relationship (QSPR) models, respectively called the multiple linear regression based QSPR (MLR-QSPR) model and the genetic programming based QSPR (GP-QSPR) model, were built to predict the solubility parameters of polymers with structure –(C1H2–C2R3R4)–, as function of some constitutional, topological and quantum chemical descriptors. The results from the internal validation analysis indicated that the GP-QSPR model has better goodness of fit statistics. The external and overall validation measures also confirmed that the GP-QSPR model significantly outperforms the MLR-QSPR model in terms of some performance metrics over the same testing data set, and that genetic programming has good potential to obtain more accurate models in QSPR studies.

Simple measurements for prediction of drug release from polymer matrices – Solubility parameters and intrinsic viscosity

PurposeThis study describes how protein release from polymer matrices correlate with simple measurements on the intrinsic viscosity of the polymer solutions used for casting the matrices and calculations of the solubility parameters of polymers and solvents used. MethodMatrices of poly(dl-lactide-co-glycolide) (PLGA) were cast with bovine serum albumin (BSA) as a model drug using different solvents (acetone, dichloromethane, ethanol and water). The amount of released protein from the different matrices was correlated with the Hildebrand and Hansen solubility parameters of the solvents, and the intrinsic viscosity of the polymer solutions. Matrix microstructure was investigated by transmission and scanning electron microscopy (TEM and SEM). Polycaprolactone (PCL) matrices were used in a similar way to support the results for PLGA matrices. ResultsThe maximum amount of BSA released and the release profile from PLGA matrices varied depending on the solvent used for casting. The maximum amount of released BSA decreased with higher intrinsic viscosity, and increased with solubility parameter difference between the solvent and polymer used. The solvent used also had an effect on the matrix microstructure as determined by TEM and SEM. Similar results were obtained for the PCL polymer systems. ConclusionsThe smaller the difference in the solubility parameter between the polymer and the solvent used for casting a polymer matrix, the lower will be the maximum protein release. This is because of the presence of smaller pore sizes in the cast matrix if a solvent with a solubility parameter close to the one of the polymer is used. Likewise, the intrinsic viscosity of the polymer solution increases as solubility parameter differences decrease, thus, simple measurements of intrinsic viscosity and solubility parameter difference, allow the prediction of protein release profiles.