RESUMEN
This study presents a solvent-free enzymatic approach for the synthesis of fatty acid methyl esters (FAMEs), such as methyl oleate, for their application as adjuvant in plant protection products (PPP) formulations. The direct esterification between free fatty acid and methanol was optimized to achieve 98% acid conversion. The kinetics of this conversion was accurately described by a simple second order mechanism and non-linear regression was applied to calculate the rate constants of the forward and backward reactions based on full progress curves data. The rate constant of the forward reaction (synthesis) was one order of magnitude higher than the backward reaction (hydrolysis) and favored formation of the target methyl ester product, rendering the removal of water unnecessary. Enzymatically synthesized methyl oleate was benchmarked against the chemically synthesized compound, showing matching results in terms of stability, spreadability and emulsifying capacity in plant care formulations. The enzymatic synthesis of FAMEs under solvent free conditions allows to achieve a safer and more sustainable character for carrier solvents in PPP formulations.
Asunto(s)
Ésteres , Lipasa , Lipasa/química , Esterificación , Hidrólisis , Ácidos Grasos , Solventes/química , Cinética , Enzimas Inmovilizadas/químicaRESUMEN
Although most of the work concerned with reaction kinetics concentrates on empirical findings, stochastic models, and differential equations, a growing number of researchers is exploring other methods to elucidate reaction kinetics. In this work, the parameterization of an utter discrete spatio-temporal model, more specifically, a cellular automaton (CA), describing the reaction of HCl with CaCO(3) , is suggested. Furthermore, a system of partial differential equations (PDE), deduced from a set of CA rules, is implemented to compare both modeling paradigms. In this article, the experimental setup to acquire time series of data is explained, a stochastic CA-based model and a continuous PDE-based model capable of describing the reaction are proposed, the models are parameterized using the experimental data and, finally, the relationship between a discrete time step of the CA-based model and the physical time is studied. Essentially, the parameterization of both models can be traced back to the quest for a solution of the inverse problem in which a (set of) rule(s), respectively a system of PDE, is deduced starting from the observed data. It is demonstrated that the proposed CA- and PDE-based models are capable of describing the considered chemical reaction with a high accuracy, which is confirmed by a root mean squared error between the simulated and observed data of 0.388 and 0.869 g CO(2) , respectively. Further, it is shown that an exponential or linear relationship can be used to link the physical time to a discrete time step of the CA-based model.