Arooshi Kumar, Jens Witsch, Jennifer Frontera, Adnan I Qureshi, Eric Oermann, Shadi Yaghi, Kara R Melmed
Predicting hematoma expansion using machine learning: An exploratory analysis of the ATACH 2 trial
Journal of the neurological sciences
paper provides study liquidphase polypropylene polymerization heterogeneous titaniummagnesium zieglernattatype catalyst kinetic model developed included activation potential active centers chain growth transferring chains hydrogen monomer deactivation active centers model created predict polymerization rate polymer yield average molecular weights polymer chains polymerization temperature changes 40 90 c developing polycentric kinetic models difficulty associated evaluating kinetic constants rates elementary reactionsstages polymerization heterogeneous titaniummagnesium catalyst tmc including cocatalyst well internal external electron donor set kinetic parameters therefore kinetic parameters must defined new catalyst presented algorithm identifying kinetic constants rates starts kinetic model considers one type active centers second stage deconvolutional analysis used molecular weight distribution mwd gel permeation chromatography gpc data polypropylene samples probable distribution flory chain lengths found type active centers third stage singlecenter model transformed polycentric kinetic model catalyst system five types active centers identified together mass fraction numberaverage molecular weight active center type catalyst consistent published results similar tibased zieglernatta catalysts