Crystals of 1,6-hexanedioic acid (I) undergo a temperature-dependent reversible phase transition from monoclinic P21/c at a temperature higher than the critical temperature (Tc) 130 K to another monoclinic P21/c at temperature lower than Tc. The phase transition is of first order, involving a discontinuity and a tripling of the b-axis at Tc whereas the other unit cell parameters vary continuously. The transition is described by the phenomenological Landau theory. The crystal structure analyses for data collected at 297(2) K and 120.0(1) K show that there is half of a molecule of (I) in the asymmetric unit at 297(2) K whereas there are one and a half molecules of (I) in the asymmetric unit at 120.0(1) K. At both temperatures, 297(2) and 120.0(1) K, intermolecular O-H···O hydrogen bonds link the molecules of I into infinite 1D chains along [101] direction. However there are significantly more O-H···O hydrogen bonds presented in the 120.0(1) K polymorph, thereby indicating this phase transition is negotiated via hydrogen bonds. The relationship of the conformational changes and hydrogen bonding for these two polymorphs are explained in detail.
In this study, an artificial neural network (ANN) trained by backpropagation algorithm, Levenberg-Marquadart, was applied to predict the yield of enzymatic synthesis of dioctyl adipate. Immobilized Candida antarctica lipase B was used as a biocatalyst for the reaction. Temperature, time, amount of enzyme, and substrate molar ratio were the four input variables. After evaluating various ANN configurations, the best network was composed of seven hidden nodes using a hyperbolic tangent sigmoid transfer function. The correlation coefficient (R2) and mean absolute error (MAE) values between the actual and predicted responses were determined as 0.9998 and 0.0966 for training set and 0.9241 and 1.9439 for validating dataset. A simulation test with a testing dataset showed that the MAE was low and R2 was close to 1. These results imply the good generalization of the developed model and its capability to predict the reaction yield. Comparison of the performance of radial basis network with the developed models showed that radial basis function was more accurate but its performance was poor when tested with unseen data. In further part of the study, the feedforward backpropagation model was used for prediction of the ester yield within the given range of the main parameters.
Dimethyl adipate (DMA) was synthesized by immobilized Candida antarctica lipase B-catalyzed esterification of adipic acid and methanol. To optimize the reaction conditions of ester production, response surface methodology was applied, and the effects of four factors namely, time, temperature, enzyme concentration, and molar ratio of substrates on product synthesis were determined. A statistical model predicted that the maximum conversion yield would be 97.6%, at the optimal conditions of 58.5 degrees C, 54.0 mg enzyme, 358.0 min, and 12:1 molar ratio of methanol to adipic acid. The R(2) (0.9769) shows a high correlation between predicted and experimental values. The kinetics of the reaction was also investigated in this study. The reaction was found to obey the ping-pong bi-bi mechanism with methanol inhibition. The kinetic parameters were determined and used to simulate the experimental results. A good quality of fit was observed between the simulated and experimental initial rates.