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dc.contributor.authorAli, Hany S.M.
dc.contributor.authorBlagden, Nicholas
dc.contributor.authorYork, Peter
dc.contributor.authorAmani, Amir
dc.contributor.authorBrook, Toni
dc.date.accessioned2011-03-23T16:15:13Z
dc.date.available2011-03-23T16:15:13Z
dc.date.issued28/06/2009
dc.identifier.citationAli, H.S.M., Blagden, N., York, P., Amani, A. and Brook, T. (2009). Artificial neural networks modelling the prednisolone nanoprecipitation in microfluidic reactors. European Journal of Pharmaceutical Sciences. Vol. 37, No. 3-4, pp. 514-522.en_US
dc.identifier.urihttp://hdl.handle.net/10454/4850
dc.descriptionnoen_US
dc.description.abstractThis study employs artificial neural networks (ANNs) to create a model to identify relationships between variables affecting drug nanoprecipitation using microfluidic reactors. The input variables examined were saturation levels of prednisolone, solvent and antisolvent flowrates, microreactor inlet angles and internal diameters, while particle size was the single output. ANNs software was used to analyse a set of data obtained by random selection of the variables. The developed model was then assessed using a separate set of validation data and provided good agreement with the observed results. The antisolvent flow rate was found to have the dominant role on determining final particle size.en_US
dc.language.isoenen_US
dc.relation.isreferencedbyhttp://dx.doi.org/10.1016/j.ejps.2009.04.007en_US
dc.subjectArtificial neural networksen_US
dc.subjectCrystal engineeringen_US
dc.subjectModellingen_US
dc.subjectMicrofluidicsen_US
dc.subjectNanoprecipitationen_US
dc.subjectPrednisoloneen_US
dc.titleArtificial neural networks modelling the prednisolone nanoprecipitation in microfluidic reactorsen_US
dc.status.refereedYesen_US
dc.typeArticleen_US


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