Prediction of mutual diffusion coefficients in binary liquid systems with one self-associating component from viscosity data and intra-diffusion coefficients at infinite dilution

Autor: G.D. Moggridge, Luigi Paduano, Ornella Ortona, Donato Ciccarelli, Michael D. Mantle, Mohammed Ainte, Qingyu Zhu, Carmine D'Agostino, Lynn F. Gladden
Přispěvatelé: Mantle, Mick [0000-0001-7977-3812], Gladden, Lynn [0000-0001-9519-0406], Apollo - University of Cambridge Repository, Zhu, Q., D'Agostino, C, Ainte, M., Mantle, M. D., Gladden, L. F., Ortona, O., Paduano, Luigi, Ciccarelli, Donato, Moggridge, G. D.
Jazyk: angličtina
Rok vydání: 2016
Předmět:
Popis: © 2016 Elsevier Ltd. A new model for prediction of mutual diffusion coefficients is proposed over the whole composition range for binary liquid systems of one self-associating component and one non-polar component. The model is based on the Darken equation with the knowledge of intra-diffusion coefficients at infinite dilution of both species and viscosity data for the system, and takes into account the cluster diffusion approach with a scaling power on the thermodynamic correction factor. The model was validated to show good concurrence with the experimental mutual diffusion data. Following the analysis that the mutual diffusion coefficients at infinite dilution can be identified with the molecular intra-diffusion coefficient of the species (i.e., the intra-diffusion coefficient at infinite dilution in the absence of self-association), the proposed equation was extended to binary liquid systems without significant association. The accuracy of prediction for systems of cross associating species is expected to be limited. The model relies on the knowledge of the viscosity of the mixture over the whole composition range and may be used as a valid alternative to models based on measuring intra-diffusion coefficients as a function of composition. Indeed, such data are not always available or are more difficult to obtain whereas viscosity measurements can be readily available and more easily measured.
Databáze: OpenAIRE