On the feasibility of chemical reactions in the presence of siloxane-based surfactants

From Amorphous Silicones to Si-Containing Highly Ordered Polymers: Some Romanian Contributions in the Field

Polydimethylsiloxane (PDMS), in spite of its well-defined helical structure, is an amorphous fluid even at extremely high molecular weights. The cause of this behavior is the high flexibility of the siloxane backbone and the lack of intermolecular interactions attributed to the presence of methyl groups. These make PDMS incompatible with almost any organic or inorganic component leading to phase separation in siloxane-siloxane copolymers containing blocks with polar organic groups and in siloxane-organic copolymers, where dimethylsiloxane segments co-exist with organic ones. Self-assembly at the micro- or nanometric scale is common in certain mixed structures, including micelles, vesicles, et cetera, manifesting reversibly in response to an external stimulus. Polymers with a very high degree of ordering in the form of high-quality crystals were obtained when siloxane/silane segments co-exist with coordinated metal blocks in the polymer chain. While in the case of coordination of secondary building units (SBUs) with siloxane ligands 1D chains are formed; when coordination is achieved in the presence of a mixture of ligands, siloxane and organic, 2D structures are formed in most cases. The Romanian research group's results regarding these aspects are reviewed: from the synthesis of classic, amorphous silicone products, to their adaptation for use in emerging fields and to new self-assembled or highly ordered structures with properties that create perspectives for the use of silicones in hitherto unexpected areas.

Highly stretchable composites from PDMS and polyazomethine fine particles

Polyazomethine fine particles are obtained in reverse micelles of a siloxane surfactant and used as a dispersed phase in PDMS to afford materials with improved and tunable electric and mechanical properties.

Polydimethylsiloxane-indomethacin blends and nanoparticles

A series of blends of polydimethylsiloxane (PDMS) and indomethacin (IMC), containing 20-80 wt.% IMC were obtained and characterized by differential scanning calorimetry, Fourier transform-infrared spectroscopy, and powder X-ray diffraction in order to observe the mutual influence of the two components. The main thermal transitions of PDMS remained un-changed. Both the solvent (tetrahydrofuran, THF) and the PDMS influenced the crystalline form of IMC. The blends were subsequently re-dissolved in THF, with or without cross-linking reagents added and precipitated into diluted aqueous solutions of siloxane-based surfactants. The resulted nanoparticles were analyzed by dynamic light scattering and scanning electron microscopy. Most of the particles had diameters between 200 and 300 nm. The surfactants, the IMC content and the cross-linking influenced the particles size and polydispersity, as well as the nanoparticle yield. The maximum drug release from selected aqueous formulations was 30%.

NSGA-II-RJG applied to multi-objective optimization of polymeric nanoparticles synthesis with silicone surfactants

Polydimethylsiloxane nanoparticles were obtained by nanoprecipitation, using a siloxane surfactant as stabilizer. Two neural networks and a genetic algorithm were used to optimize this process, by minimizing the particle diameter and the polydispersity, finding in this way the optimum values for surfactant and polymer concentrations, and storage temperature. In order to improve the performance of the non-dominated sorting genetic algorithm, NSGA-II, a genetic operator was introduced in this study — the transposition operator — “real jumping genes”, resulting NSGA-II-RJG. It was implemented in original software and was applied to the multi-objective optimization of the polymeric nanoparticles synthesis with silicone surfactants. The multi-objective function of the algorithm included two fitness functions. One fitness function was calculated with a neural network modelling the variation of the particle diameter on the surfactant concentration, polymer concentration, and storage temperature, and the other was computed by a neural network modelling the dependence of polydispersity index on surfactant and polymer concentrations. The performance of the software program that implemented NSGA-II-RJG was highlighted by comparing it with the software implementation of NSGA-II. The results obtained from simulations showed that NSGA-II-RJG is able to find non-dominated solutions with a greater diversity and a faster convergence time than NSGA-II.