Prediction of the lower critical solution temperature of poly(N-isopropylacrylamide-co-methoxy triethyleneglycol acrylate) in aqueous salt solutions using support vector regression

Degradable Nanogels Based on Poly[Oligo(Ethylene Glycol) Methacrylate] (POEGMA) Derivatives through Thermo-Induced Aggregation of Polymer Chain and Subsequent Chemical Crosslinking

Polymer nanogels-considered as nanoscale hydrogel particles-are attractive for biological and biomedical applications due to their unique physicochemical flexibility. However, the aggregation or accumulation of nanoparticles in the body or the occurrence of the body's defense reactions still pose a research challenge. Here, we demonstrate the fabrication of degradable nanogels using thermoresponsive, cytocompatible poly[oligo(ethylene glycol) methacrylate]s-based copolymers (POEGMA). The combination of POEGMA's beneficial properties (switchable affinity to water, nontoxicity, non-immunogenicity) along with the possibility of nanogel degradation constitute an important approach from a biological point of view. The copolymers of oligo(ethylene glycol) methacrylates were partially modified with short segments of degradable oligo(lactic acid) (OLA) terminated with the acrylate group. Under the influence of temperature, copolymers formed self-assembled nanoparticles, so-called mesoglobules, with sizes of 140-1000 nm. The thermoresponsive behavior of the obtained copolymers and the nanostructure sizes depended on the heating rate and the presence of salts in the aqueous media. The obtained mesoglobules were stabilized by chemical crosslinking via thiol-acrylate Michael addition, leading to nanogels that degraded over time in water, as indicated by the DLS, cryo-TEM, and AFM measurements. Combining these findings with the lack of toxicity of the obtained systems towards human fibroblasts indicates their application potential.

Thermosensitive Shape-Memory Poly(stearyl acrylate-co-methoxy poly(ethylene glycol) acrylate) Hydrogels

Stimuli-sensitive hydrogels are highly desirable candidates for application in intelligent biomaterials. Thus, a novel thermosensitive hydrogel with shape-memory function was developed. Hydrophobic stearyl acrylate (SA), hydrophilic methoxy poly(ethylene glycol) acrylate (MPGA), and a crosslinking monomer were copolymerized to prepare poly(SA-co-MPGA) gels with various mole fractions of SA (xSA) in ethanol. Subsequently, the prepared gels were washed, dried, and re-swelled in water at 50 °C. Differential scanning calorimetric (DSC) and compression tests at different temperatures revealed that poly(SA-co-MPGA) hydrogels with xSA > 0.5 induce a crystalline-to-amorphous transition, which is a hard-to-soft transition at ~40 °C that is based on the formation/non-formation of a crystalline structure containing stearyl side chains. The hydrogels stored in water maintained an almost constant volume, independent of the temperature. The poly(SA-co-MPGA) hydrogel was soft, flexible, and deformed at 50 °C. However, the hydrogel stiffened when cooled to room temperature, and the deformation was reversible. The shape-memory function of poly(SA-co-MPGA) hydrogels is proposed for potential use in biomaterials; this is partially attributed to the use of MPGA, which consists of relatively biocompatible poly(ethylene glycol).

Swelling of Thermo-Responsive Gels in Aqueous Solutions of Salts: A Predictive Model

The equilibrium degree of swelling of thermo-responsive (TR) gels is strongly affected by the presence of ions in an aqueous solution. This phenomenon plays an important role in (i) the synthesis of multi-stimuli-responsive gels for soft robotics, where extraordinary strength and toughness are reached by soaking of a gel in solutions of multivalent ions, and (ii) the preparation of hybrid gels with interpenetrating networks formed by covalently cross-linked synthetic chains and ionically cross-linked biopolymer chains. A model is developed for equilibrium swelling of a TR gel in aqueous solutions of salts at various temperatures T below and above the critical temperature at which collapse of the gel occurs. An advantage of the model is that it involves a a small (compared with conventional relations) number of material constants and allows the critical temperature to be determined explicitly. Its ability (i) to describe equilibrium swelling diagrams on poly(N-isopropylacrylamide) gels in aqueous solutions of mono- and multivalent salts and (ii) to predict the influence of volume fraction of salt on the critical temperature is confirmed by comparison of observations with results of numerical simulation.

TiO2 Nanoparticle-Loaded Poly(NIPA-co-NMA) Fiber Web for the Adsorption and Photocatalytic Degradation of 4-Isopropylphenol

A TiO₂ nanoparticle-loaded polymer fiber web was developed as a functional material with the ability to adsorb and photo-catalytically degrade organic pollutants in aquatic media. A linear copolymer of N-isopropylacrylamide (primary component) and N-methylol acrylamide (poly(NIPA-co-NMA)) was prepared, and composite fibers were fabricated by electrospinning a methanol suspension containing the copolymer and commercially available TiO₂ nanoparticles. The crosslinking of the polymer via the formation of methylene bridges between NMA units was accomplished by heating, and the fiber morphology was analyzed by electron microscopy. 4-Isopropylphenol generated by the degradation of bisphenol A—one of the endocrine-disrupting chemicals—was used as the model organic pollutant. As poly(NIPA) is a thermosensitive polymer that undergoes hydrophilic/hydrophobic transition in water, the temperature-dependence of the adsorption and photocatalytic degradation of 4-isopropylphenol was investigated. The degradation rate was analyzed using a pseudo-first-order kinetic model to obtain the apparent reaction rate constant, k_app. The enhancement of the photocatalytic degradation rate owing to the adsorption of 4-isopropylphenol onto thermosensitive poly(NIPA)-based fibers is discussed in terms of the ratio of the k_app of the composite fiber to that of unsupported TiO₂ nanoparticles. Based on the results, an eco-friendly wastewater treatment process involving periodically alternated adsorption and photocatalytic degradation is proposed.

Modeling Superconducting Critical Temperature of 122-Iron-Based Pnictide Intermetallic Superconductor Using a Hybrid Intelligent Computational Method

Structural transformation and magnetic ordering interplays for emergence as well as suppression of superconductivity in 122-iron-based superconducting materials. Electron and hole doping play a vital role in structural transition and magnetism suppression and ultimately enhance the room pressure superconducting critical temperature of the compound. This work models the superconducting critical temperature of 122-iron-based superconductor using tetragonal to orthorhombic lattice (LAT) structural transformation during low-temperature cooling and ionic radii of the dopants as descriptors through hybridization of support vector regression (SVR) intelligent algorithm with particle swarm (PS) parameter optimization method. The developed PS-SVR-RAD model, which utilizes ionic radii (RAD) and the concentrations of dopants as descriptors, shows better performance over the developed PS-SVR-LAT model that employs lattice parameters emanated from structural transformation as descriptors. Using the root mean square error (RMSE), coefficient of correlation (CC) and mean absolute error as performance measuring criteria, the developed PS-SVR-RAD model performs better than the PS-SVR-LAT model with performance improvement of 15.28, 7.62 and 72.12%, on the basis of RMSE, CC and Mean Absolute Error (MAE), respectively. Among the merits of the developed PS-SVR-RAD model over the PS-SVR-LAT model is the possibility of electrons and holes doping from four different dopants, better performance and ease of model development at relatively low cost since the descriptors are easily fetched ionic radii. The developed intelligent models in this work would definitely facilitate quick and precise determination of critical transition temperature of 122-iron-based superconductor for desired applications at low cost with experimental stress circumvention.

Modeling the Optical Properties of a Polyvinyl Alcohol-Based Composite Using a Particle Swarm Optimized Support Vector Regression Algorithm

We developed particle swarm optimization-based support vector regression (PSVR) and ordinary linear regression (OLR) models for estimating the refractive index (n) and energy gap (E) of a polyvinyl alcohol composite. The n-PSVR model, which can estimate the refractive index of a polyvinyl alcohol composite using the energy gap as a descriptor, performed better than the n-OLR model in terms of root mean square error (RMSE) and mean absolute error (MAE) metrics. The E-PSVR model, which can predict the energy gap of a polyvinyl alcohol composite using its refractive index descriptor, outperformed the E-OLR model, which uses similar descriptor based on several performance measuring metrics. The n-PSVR and E-PSVR models were used to investigate the influences of sodium-based dysprosium oxide and benzoxazinone derivatives on the energy gaps of a polyvinyl alcohol polymer composite. The results agreed well with the measured values. The models had low mean absolute percentage errors after validation with external data. The precision demonstrated by these predictive models will enhance the tailoring of the optical properties of polyvinyl alcohol composites for the desired applications. Costs and experimental difficulties will be reduced.