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dc.contributor.authorChabalenge, Billy
dc.contributor.authorKorde, Sachin A.
dc.contributor.authorKelly, Adrian L.
dc.contributor.authorNeagu, Daniel
dc.contributor.authorParadkar, Anant R.
dc.date.accessioned2020-07-27T11:48:27Z
dc.date.accessioned2020-08-12T09:24:15Z
dc.date.available2020-07-27T11:48:27Z
dc.date.available2020-08-12T09:24:15Z
dc.date.issued2020-07
dc.identifier.citationChabalenge B, Korde S, Kelly AL et al (2020) Understanding matrix-assisted continuous co-crystallization using a data mining approach in Quality by Design (QbD). Crystal Growth & Design. 20(7): 4540-4549.en_US
dc.identifier.urihttp://hdl.handle.net/10454/17941
dc.descriptionYesen_US
dc.description.abstractThe present study demonstrates the application of decision tree algorithms to the co-crystallization process. Fifty four (54) batches of carbamazepine-salicylic acid co-crystals embedded in poly(ethylene oxide) were manufactured via hot melt extrusion and characterized by powder X-ray diffraction, differnetial scanning calorimetry, and near-infrared spectroscopy. This dataset was then applied in WEKA, which is an open-sourced machine learning software to study the effect of processing temperature, screw speed, screw configuration, and poly(ethylene oxide) concentration on the percentage of co-crystal conversion. The decision trees obtained provided statistically meaningful and easy-to-interpret rules, demonstrating the potential to use the method to make rational decisions during the development of co-crystallization processes.en_US
dc.description.sponsorshipCommonwealth Scholarship Commission in the UK (ZMCS-2018-783) and Engineering and Physical Sciences Research Council (EPSRC EP/J003360/1 and EP/L027011/1)en_US
dc.language.isoenen_US
dc.rights©2020 ACS. This document is the Accepted Manuscript version of a Published Work that appeared in final form in Crystal Growth & Design, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see https://doi.org/10.1021/acs.cgd.0c00338.en_US
dc.subjectCo-crystallization processen_US
dc.subjectDecision tree algorithmsen_US
dc.subjectHot melt extrusionen_US
dc.subjectQuality by Design (QbD)en_US
dc.titleUnderstanding matrix-assisted continuous co-crystallization using a data mining approach in Quality by Design (QbD)en_US
dc.status.refereedYesen_US
dc.date.Accepted2020-05
dc.date.application2020-06-08
dc.typeArticleen_US
dc.type.versionAccepted manuscripten_US
dc.identifier.doihttps://doi.org/10.1021/acs.cgd.0c00338
dc.date.updated2020-07-27T10:48:30Z
refterms.dateFOA2020-08-12T09:25:21Z


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