Carboxymethylated pulp as starting point to prepare hydroxypropylmethyl cellulose with enhanced gel rheological properties in an aqueous medium

Hydroxypropyl methylcellulose in an aqueous solution upon heating tends to undergo thermal gelation, where the polymer chains form a network and precipitate from solution. This occurs at a temperature known as thermal gelation point. Polymer precipitation causes a significant drop in the shear viscosity. This could be a disadvantage in a hot environment or in applications were heat is applied. In this work, a hydroxypropylmethyl cellulose (HPMC) was formed that undergoes thermal gelation with no polymer precipitation and with enhanced rheological properties. The target HPMC was prepared from wood pulp with a low content of carboxymethyl groups. The produced hydroxypropyl methylcellulose (CMHPMC) derivative showed unique physical properties that are not achievable with typical hydroxypropyl methylcellulose. The thermal gelation temperature of an aqueous solution of CMHPMC was increased from 55 °C for commercial HPMC to 85 °C for CMHPMC. A substitution level of carboxymethylation that led to an HPMC with a thermal gelation and with no precipitation was determined to be a 0.15 of carboxyl groups per anhydroglucose repeat unit. In addition, the carboxymethylated pulp showed an enhanced reactivity towards etherification reactions.

Numerical simulation, clustering, and prediction of multicomponent polymer precipitation

Multicomponent polymer systems are of interest in organic photovoltaic and drug delivery applications, among others where diverse morphologies influence performance. An improved understanding of morphology classification, driven by composition-informed prediction tools, will aid polymer engineering practice. We use a modified Cahn–Hilliard model to simulate polymer precipitation. Such physics-based models require high-performance computations that prevent rapid prototyping and iteration in engineering settings. To reduce the required computational costs, we apply machine learning (ML) techniques for clustering and consequent prediction of the simulated polymer-blend images in conjunction with simulations. Integrating ML and simulations in such a manner reduces the number of simulations needed to map out the morphology of polymer blends as a function of input parameters and also generates a data set which can be used by others to this end. We explore dimensionality reduction, via principal component analysis and autoencoder techniques, and analyze the resulting morphology clusters. Supervised ML using Gaussian process classification was subsequently used to predict morphology clusters according to species molar fraction and interaction parameter inputs. Manual pattern clustering yielded the best results, but ML techniques were able to predict the morphology of polymer blends with ≥90% accuracy.

Enhanced photocatalytic degradation of doxycycline using a magnetic polymer-ZnO composite

A novel magnetic polymer-ZnO composite was prepared by incorporating Fe3O4 and ZnO nano-particles in the structure of an adsorbent polymer. Precipitation polymerization was used for synthesizing the adsorbent polymer and its efficiency for extracting doxycycline from aqueous solution was optimized according to several parameters including time, pH and amount of polymer. Results showed the highest extraction efficiency at neutral pH of the doxycycline solution in 20 min, and the capacity of the polymer was about 20 mg/g. The magnetic property of a material is important for fast and facile separation of composite particles after each use. Magnetic polymer-ZnO composite was synthesized by adding Fe3O4 and ZnO nano-particles to the polymerization mixture in order to take advantage of both sorption and photocatalytic degradation mechanisms. The obtained composite was characterized using X-ray diffraction, Fourier transform infrared spectroscopy, scanning electron microscopy equipped with wavelength dispersive X-ray spectroscopy and used for enhanced photocatalytic degradation of doxycycline in aqueous solution. Results showed 76.5% degradation of doxycycline in 6 hours which was significantly higher than the degradation observed by an equivalent amount of ZnO nano-particles. Photocatalytic degradation of doxycycline fitted the pseudo first order kinetic model with a rate constant of 4 × 10−3 μg mL−1 min−1.

Characterization of the Proinflammatory Profile of Synovial Fluid-Derived Exosomes of Patients with Osteoarthritis

The purpose of this study is to characterize synovial fluid- (SF-) derived exosomes of patients with gonarthrosis comparing two methods of isolation and to investigate their immune regulatory properties. Extracellular vesicles (EVs) have been isolated from inflamed SF by polymer precipitation method and quantified by Exocet kit and by nanoparticle tracking analysis. Vesicles expressed all the specific exosomal markers by immunoblot and FACS. After isolation with Exoquick, a relevant contamination by immune complexes was detected, which required further magnetic bead-based purification to remove. SF-derived exosomes significantly stimulated the release of several inflammatory cytokines and chemokines and metalloproteinases by M1 macrophages but did not influence the expression of CD80 and CD86 costimulatory molecules. In conclusion, we characterized purified exosomes isolated from inflamed SF and demonstrate that purified exosomes are functionally active in their ability to stimulate the release of proinflammatory factors from M1 macrophages. Our data indicate that SF-derived exosomes from gonarthrosis patients play a role in disease progression.