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A modified network component analysis (NCA) methodology for the decomposition of X-ray scattering signatures / Ian Tolle in Industrial & engineering chemistry research, Vol. 48 N° 13 (Juillet 2009) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 48 N° 13 (Juillet 2009) . - pp. 6137–6144 Titre : A modified network component analysis (NCA) methodology for the decomposition of X-ray scattering signatures Type de document : texte imprimé Auteurs : Ian Tolle, Auteur ; Xinqun Huang, Auteur ; Yvonne A. Akpalu, Auteur Année de publication : 2009 Article en page(s) : pp. 6137–6144 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Polymeric  materials  Optimization-based  methodology Résumé : In this work, we present an optimization-based methodology to identify network structure−process relationships in polymeric materials, to provide a robust understanding of how processing conditions affect their structural domains. The approach is largely inspired by network component analysis (NCA), which was originally developed in 2003 [Liao et al. Proc. Natl. Acad. Sci., U.S.A. 2003, 100, 15522−15527] to infer unobservable phenomena using large multivariate datasets. The fundamental assumption in NCA is that the connective relationship between desired unobservable phenomena and measured data is based on a largely known bipartite network topology. However, in many such analyses, this topology is either partially or completely unknown. To address this issue, the original NCA problem is reformulated as a mixed-integer nonlinear program (MINLP). Optimal solutions of the MINLP formulation provide network topologies and physically meaningful component signatures that correspond to the best possible data reconstruction. We demonstrate this approach for the analysis of wide-angle X-ray scattering (WAXS) data from a branched copolymer system. The copolymer samples vary in regard to side-chain length and isothermal crystallization temperature. Using our analysis, we isolate crystalline and amorphous components and observe their temperature-dependent variation. Good agreement is achieved between the degree of crystallinity calculated by this NCA-based decomposition and the experimentally reported values. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie8012715 [article] A modified network component analysis (NCA) methodology for the decomposition of X-ray scattering signatures [texte imprimé] / Ian Tolle, Auteur ; Xinqun Huang, Auteur ; Yvonne A. Akpalu, Auteur . - 2009 . - pp. 6137–6144. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N° 13 (Juillet 2009) . - pp. 6137–6144 Mots-clés : Polymeric  materials  Optimization-based  methodology Résumé : In this work, we present an optimization-based methodology to identify network structure−process relationships in polymeric materials, to provide a robust understanding of how processing conditions affect their structural domains. The approach is largely inspired by network component analysis (NCA), which was originally developed in 2003 [Liao et al. Proc. Natl. Acad. Sci., U.S.A. 2003, 100, 15522−15527] to infer unobservable phenomena using large multivariate datasets. The fundamental assumption in NCA is that the connective relationship between desired unobservable phenomena and measured data is based on a largely known bipartite network topology. However, in many such analyses, this topology is either partially or completely unknown. To address this issue, the original NCA problem is reformulated as a mixed-integer nonlinear program (MINLP). Optimal solutions of the MINLP formulation provide network topologies and physically meaningful component signatures that correspond to the best possible data reconstruction. We demonstrate this approach for the analysis of wide-angle X-ray scattering (WAXS) data from a branched copolymer system. The copolymer samples vary in regard to side-chain length and isothermal crystallization temperature. Using our analysis, we isolate crystalline and amorphous components and observe their temperature-dependent variation. Good agreement is achieved between the degree of crystallinity calculated by this NCA-based decomposition and the experimentally reported values. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie8012715