This work focuses on determining the dissociation constants (pKa) of eight amines, namely, 3-(Diethylamino) propylamine, 1,3-Diaminopentane, 3-Butoxypropylamine, 2-(Methylamino) ethanol, Bis(2-methoxyethyl) amine, α-Methylbenzylamine, 2-Aminoheptane, and 3-Amino-1-phenylbutane, within temperatures ranging from 293.15 K to 323.15 K. The thermodynamic properties of the protonated reactions were regressed from the pKa work. In addition, the protonated order of both 3-(Diethylamino) propylamine and 1,3-Diaminopentane were determined using computational chemistry methods owing to their unsymmetrical structures. In addition to the experimental methods, the dissociation constants at the standard temperature (298.15 K) were also estimated using group functional models (paper–pencil) and computational methods. The computational methods include COSMO-RS and computational chemistry calculations. An artificial neural network (ANN) method was employed to model the data by collecting and combining the experimental properties to estimate the missing pKa values. Although the ANN models can provide acceptable results, they depend on the availability of the data. Instead of using the experimental properties, they were generated using software such as Aspen Plus or CosmothermX. The simulated ANN model can also provide very good fits to the experimental constant values.
This work focused on determining the dissociation constants (pKa) for eight amines, namely, 3-(Diethylamino) propylamine, 1,3-Diaminopentane, 3-Butoxypropylamine, 2-(Methylamino) ethanol, Bis(2-methoxyethyl) Amine, α-Methylbenzylamine, 2-Aminoheptane, and 3-Amino-1-phenylbutane at temperatures ranging from 293.15 K to 323.15 K. The protonated order of two polyamines, 3-(Diethylamino) propylamine and 1, 3-Diaminopentane, were determined using computational chemistry methods. The dissociation constants at the standard temperature of 298.15 K were estimated using group functional models (paper-pencil) and computational methods using software such as COSMO-RS and Gaussian. In addition, the pKas at various temperatures were calculated using computational methods for two different thermodynamic cycle. A simple artificial neural network (ANN) method was also employed to reduce the calculation time as well as improve the accuracy. Instead of using the experimental property data, these could be generated using Aspen Plus or CosmothermX. The simulated model provided a very good fit to the pKa values.
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