International audienceIn order to handle a tractable network for complex reaction systems such as oligomerization, stochastic tools are applied to reduce the reaction mechanism. The particularity of this work is that quantitative single event kinetic modeling constants are used to generate a network which correctly describes the dynamic behavior of the studied reacting system. By using the stochastic method, which is based on a probabilistic approach, we can avoid the generation of improbable reaction paths in order to reduce the network expansion. Comparison with a classical limited network shows that the proposed network generation technique can be more reliable. Alongside, the stochastic simulation algorithm can be used as a method of simulation instead of the deterministic method because of the huge size of the oligomerization network
In
this work, the ability of four nanoparticles including Al2O3, TiO2, graphene, and graphene oxide
(GO) as additives for extraction of cephalexin in aqueous two-phase
systems composed of polyethylene glycol (PEG) and three sodium-based
organic salts was investigated. For this purpose, phase diagrams and
the liquid–liquid equilibrium (LLE) data for the {PEG 6000
+ sodium citrate, sodium tartrate, sodium succinate + H2O} and partition coefficient for cephalexin were determined. The
addition of 0.001 wt % graphene oxide increased the partition coefficient
by 9% for the {PEG + sodium citrate + water} system, while other nanoparticles
had no effect or decreased the partition coefficient. To investigate
the effect of GO concentration on the partition coefficient, various
concentrations ranging from 0.005 to 0.04 wt % was examined. It is
observed that at a GO concentration equal to 0.01 wt %, the maximum
partition coefficient improvement is achievable to be 59%. The highest
partition coefficient 5.74 was obtained for the system {PEG6000 19
wt %, sodium citrate 10 wt %, and GO 0.01 wt %}. It can be concluded
that a trace amount of nanoparticles can change the partition coefficient
either by increasing or decreasing. It seems that the concentration
of nanoparticle also has a significant effect on the partition coefficient.
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