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Bryan S. Beckingham - One of the best experts on this subject based on the ideXlab platform.

  • Recommendation for Accurate Experimental Determination of Reactivity Ratios in Chain Copolymerization
    Macromolecules, 2019
    Co-Authors: Nathaniel A. Lynd, Robert C. Ferrier, Bryan S. Beckingham
    Abstract:

    A set of Copolymerization data at prescribed reactivity ratios was numerically generated and then fit using common methods of data analysis including the Copolymer Equation, Fineman–Ross, Kelen–Tud...

  • Recommendation for Accurate Experimental Determination of Reactivity Ratios in Chain Copolymerization
    2019
    Co-Authors: Nathaniel A. Lynd, Robert C. Ferrier, Bryan S. Beckingham
    Abstract:

    A set of Copolymerization data at prescribed reactivity ratios was numerically generated and then fit using common methods of data analysis including the Copolymer Equation, Fineman–Ross, Kelen–Tüdös, and integrated methods of data analysis, such as those reported by Beckingham, Sanoja, and Lynd, and Meyer and Lowry. Significantly, the nonintegrated approaches based on the Copolymer Equation returned systemically inaccurate reactivity ratios, whereas the integrated methods produced consistently accurate reactivity ratios across 560 calculated data sets. Hence, to determine reactivity ratios with the greatest accuracy and efficiency, we recommend that Copolymerization data be fit simultaneously to the models reported by Beckingham–Sanoja–Lynd (BSL) and Meyer–Lowry (ML). If the reactivity ratios are consistent, then a nonterminal model of Copolymerization adequately describes the Copolymerization with a single reactivity ratio parameter. If there is a difference in the reactivity ratios between BSL and ML, then the ML-derived values take precedence and a terminal model of Copolymerization describes the kinetics of the system with two independent reactivity ratios. This prescription will ensure that the model with the least complexity will be used to interpret data, and that the reactivity ratios reported are most accurate and descriptive of the underlying Copolymerization mechanism. Future use of the Copolymer Equation, Fineman–Ross, and Kelen–Tüdös to interpret Copolymerization data is strongly discouraged due to unquantifiable inaccuracy and needlessly wasted experimental effort

Nathaniel A. Lynd - One of the best experts on this subject based on the ideXlab platform.

  • Recommendation for Accurate Experimental Determination of Reactivity Ratios in Chain Copolymerization
    Macromolecules, 2019
    Co-Authors: Nathaniel A. Lynd, Robert C. Ferrier, Bryan S. Beckingham
    Abstract:

    A set of Copolymerization data at prescribed reactivity ratios was numerically generated and then fit using common methods of data analysis including the Copolymer Equation, Fineman–Ross, Kelen–Tud...

  • Recommendation for Accurate Experimental Determination of Reactivity Ratios in Chain Copolymerization
    2019
    Co-Authors: Nathaniel A. Lynd, Robert C. Ferrier, Bryan S. Beckingham
    Abstract:

    A set of Copolymerization data at prescribed reactivity ratios was numerically generated and then fit using common methods of data analysis including the Copolymer Equation, Fineman–Ross, Kelen–Tüdös, and integrated methods of data analysis, such as those reported by Beckingham, Sanoja, and Lynd, and Meyer and Lowry. Significantly, the nonintegrated approaches based on the Copolymer Equation returned systemically inaccurate reactivity ratios, whereas the integrated methods produced consistently accurate reactivity ratios across 560 calculated data sets. Hence, to determine reactivity ratios with the greatest accuracy and efficiency, we recommend that Copolymerization data be fit simultaneously to the models reported by Beckingham–Sanoja–Lynd (BSL) and Meyer–Lowry (ML). If the reactivity ratios are consistent, then a nonterminal model of Copolymerization adequately describes the Copolymerization with a single reactivity ratio parameter. If there is a difference in the reactivity ratios between BSL and ML, then the ML-derived values take precedence and a terminal model of Copolymerization describes the kinetics of the system with two independent reactivity ratios. This prescription will ensure that the model with the least complexity will be used to interpret data, and that the reactivity ratios reported are most accurate and descriptive of the underlying Copolymerization mechanism. Future use of the Copolymer Equation, Fineman–Ross, and Kelen–Tüdös to interpret Copolymerization data is strongly discouraged due to unquantifiable inaccuracy and needlessly wasted experimental effort

Robert C. Ferrier - One of the best experts on this subject based on the ideXlab platform.

  • Recommendation for Accurate Experimental Determination of Reactivity Ratios in Chain Copolymerization
    Macromolecules, 2019
    Co-Authors: Nathaniel A. Lynd, Robert C. Ferrier, Bryan S. Beckingham
    Abstract:

    A set of Copolymerization data at prescribed reactivity ratios was numerically generated and then fit using common methods of data analysis including the Copolymer Equation, Fineman–Ross, Kelen–Tud...

  • Recommendation for Accurate Experimental Determination of Reactivity Ratios in Chain Copolymerization
    2019
    Co-Authors: Nathaniel A. Lynd, Robert C. Ferrier, Bryan S. Beckingham
    Abstract:

    A set of Copolymerization data at prescribed reactivity ratios was numerically generated and then fit using common methods of data analysis including the Copolymer Equation, Fineman–Ross, Kelen–Tüdös, and integrated methods of data analysis, such as those reported by Beckingham, Sanoja, and Lynd, and Meyer and Lowry. Significantly, the nonintegrated approaches based on the Copolymer Equation returned systemically inaccurate reactivity ratios, whereas the integrated methods produced consistently accurate reactivity ratios across 560 calculated data sets. Hence, to determine reactivity ratios with the greatest accuracy and efficiency, we recommend that Copolymerization data be fit simultaneously to the models reported by Beckingham–Sanoja–Lynd (BSL) and Meyer–Lowry (ML). If the reactivity ratios are consistent, then a nonterminal model of Copolymerization adequately describes the Copolymerization with a single reactivity ratio parameter. If there is a difference in the reactivity ratios between BSL and ML, then the ML-derived values take precedence and a terminal model of Copolymerization describes the kinetics of the system with two independent reactivity ratios. This prescription will ensure that the model with the least complexity will be used to interpret data, and that the reactivity ratios reported are most accurate and descriptive of the underlying Copolymerization mechanism. Future use of the Copolymer Equation, Fineman–Ross, and Kelen–Tüdös to interpret Copolymerization data is strongly discouraged due to unquantifiable inaccuracy and needlessly wasted experimental effort

J. S. Lin - One of the best experts on this subject based on the ideXlab platform.

  • The equilibrium melting points of random ethylene‐octene Copolymers: A test of the Flory and Sanchez–Eby theories
    Journal of Polymer Science Part B: Polymer Physics, 2000
    Co-Authors: Man-ho Kim, Paul J. Phillips, J. S. Lin
    Abstract:

    Ethylene/l-octene Copolymers produced with metallocene catalysts are believed to have a homogeneous comonomer content with respect to molecular weight. Two series of Copolymers of different molecular weights with a 1-octene content ranging from 0 to 39 branches per 1000 carbon atoms were studied. The influence of branch content on structure and melting behavior as well as on isothermal and nonisothermal bulk crystallization was studied. In this article, the equilibrium melting temperatures of ethylene/l-octene random Copolymers is the focus. The principal techniques used were thermal analysis and small-angle X-ray scattering. The use of Hoffman-Weeks plots to obtain the equilibrium melting temperatures of ethylene/l-octene random Copolymers resulted in nonsensical high values of the equilibrium melting point or showed behavior parallel to the T m = T c line, resulting in no intercept and, hence, an infinite equilibrium melting point. The equilibrium melting temperatures of linear polyethylenes and homogeneous ethylene/ l-octene random Copolymers were determined as a function of molecular weight and branch content via Thompson-Gibbs plots involving lamellar thickness data obtained from small-angle X-ray scattering. This systematic study made possible the evaluation of two equilibrium melting temperature depression Equations for olefin-type random Copolymers, the Flory Equation and the Sanchez-Eby Equation, as a function of defect content and molecular weight. The range over which the two Equations could be applied depended on the defect content after correction for the effect of molecular weight on the equilibrium melting temperature. The equilibrium melting temperature, T m 0 (n, p B ), of the ethylene/l-octene random Copolymers was a function of the molecular weight and defect content for low defect contents (p B ≤ 1.0%). T m 0 (n, p B ) was a weak function of molecular weight and a strong function of the defect content at a high defect content (p B ≥ 1.0%). The Flory Copolymer Equation could predict T m 0 (n, p B ) at p B ≤ 1.0% when corrections for the effect of molecular weight were made. The Sanchez-Eby uniform inclusion model could predict T m 0 (n, p B ) at a high defect content (1.6%≤p B ≤ 2.0%). We conclude that some defects were included in the crystalline phase and that the excess free energies (18-37 kJ/mol) estimated in this study were within the theoretical range.

Maria Świtała-Żeliazkow - One of the best experts on this subject based on the ideXlab platform.

  • Radical Copolymerization of citraconic acid with styrene in dioxane solution
    European Polymer Journal, 2002
    Co-Authors: Maria Świtała-Żeliazkow
    Abstract:

    Abstract Styrene and citraconic acid (CA) were Copolymerized in the dioxane solution ranging mole fraction of CA in feed from 0.1 to 0.9 at 70 °C. The terminal and penultimate models were used to fit the Copolymer composition Equation. Curve fitting, Mayo–Lewis, Joshi–Joshi, Fineman–Ross, Ezrielev–Brokhina–Roskin, Kellen–Tudős methods were used to solve the Copolymer Equation in terminal model. Besides these methods Solver in Excel 97 was used to solve Copolymer Equation in terminal and penultimate models of Copolymerization. Experimental mole fractions of CA and those predicted from both models are agreed within the precision of the method used for the Copolymer analysis, so the Copolymer composition does not permit a definite choice of the adequate Copolymerization model.