General approach to polymer properties dependent on molecular characteristics

Dobkowski, Z.

doi:10.1016/0014-3057(81)90174-9

Cited by 21 publications

(5 citation statements)

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“…In particular, it depends on the polydispersity degree. [25][26][27] The narrower is the molecular weight distribution, the higher is the glass transition temperature for the same average molecular weight. Moreover, the difference between glass transition temperatures for samples with different polydispersities decreases with growth of the average molecular weight.…”

Section: High-temperature Relaxation Rangementioning

confidence: 99%

Molecular Mobility of Molecular Brushes With a Polyimide Backbone and Polymethylmethacrylate Side Chains of Different Lengths

Никонорова

Мелешко

Ilgach

et al. 2013

Journal of Macromolecular Science, Part B

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The molecular mobility of polyimide (PI) brushes, i.e., graft-copolymers with a polyimide backbone and PMMA side chains of different lengths, is studied by the dielectric spectroscopy method. Three dipolar polarization regions are found in the temperature range from -150 to 200 • C: β-, β 1 , and α-processes. The β-process is due to a local mobility of polar groups of the PI backbone in the glassy state. Ester groups of polymethylmethacrylate (PMMA) side chains are responsible for two forms of the molecular mobility: a local mobility in the glassy state (β 1 -process) and a cooperative segmental motion of the skeletons of the PMMA side chains (α-process). The parameters of the β-and β 1 -processes are weakly dependent on the side chain's length. For the α-process, the molecular mobility of the PMMA side chains decreases with increasing their length.

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Section: High-temperature Relaxation Rangementioning

confidence: 99%

Molecular Mobility of Molecular Brushes With a Polyimide Backbone and Polymethylmethacrylate Side Chains of Different Lengths

Никонорова

Мелешко

Ilgach

et al. 2013

Journal of Macromolecular Science, Part B

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“…The T g /molecular weight relationships discussed so far use average molecular weights but ignore the breadth of the molecular weight distribution and instead assume a monodisperse system. Dobkowski6 included the polydispersity as an additional term, q = M n / M w , in the expression where g v is a branching factor that is unity for this study, and A , a , a 2 , and a 3 are constants. Dobkowski found that for polycarbonate, a = −1, ( a 2 ) n (when M n is used) = −0.63, and ( a 2 ) w ( M w ) = 0.38.…”

Section: Discussionmentioning

confidence: 99%

“…Also discussed earlier, a further refinement of Flory's basic relationship was proposed by Dobkowski6 (eq 7), who included terms to incorporate the effects of polydispersity in the system. This equation also allows for the use of either M n or M w .…”

Section: Discussionmentioning

confidence: 99%

Development of a methodology to predict material properties from environmental exposure. II. Structural features

Robertson

Ward

2002

J Polym Sci B Polym Phys

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A general scheme is presented in the first part of this series in which degradation‐induced changes in a polymer that are produced by exposure to an aggressive environment are linked to measurable kinetic parameters and molecular weight distribution variations. Although general in nature for all polymers and environments, the data were collected on bisphenol A polycarbonate that was degraded by elevated temperatures. A parameter, τ, the product of a kinetic rate constant, k, and the environmental exposure time, provided a metric that was suitable for superposition methods to reduce the data. τ was directly related to the molecular weight distribution shifting during environmentally induced changes. This article extends the methodology to structure–property correlations such as the relationship of the glass‐transition temperature, rheology, and the tensile strength of polycarbonate after the environmental treatment. Again, in a universal fashion, the τ value (the degree of degradation) was sufficient to model the observed physical property changes with the amount of exposure to the hostile environment. As long as the kinetics of the process of change are amenable to a mathematical model and a quantitative measure of the change in a fundamental polymer parameter is available, this methodology should be applicable to any polymer in any environment. © 2002 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 40: 802–812, 2002

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“…Because polymer properties are dependent on MWD, it becomes crucial to know this feature. , At this point, it is intuitive to infer that the descriptor values of each chain length of the MWD curve are needed for QSPR modeling.…”

Section: Methodsmentioning

confidence: 99%

Feature Selection for Polymer Informatics: Evaluating Scalability and Robustness of the FS4RV_DD Algorithm Using Synthetic Polydisperse Data Sets

Cravero

Schustik

Martínez

et al. 2019

J. Chem. Inf. Model.

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The feature selection (FS) process is a key step in the Quantitative Structure-Property Relationship (QSPR) modeling of physicochemical properties in Cheminformatics. In particular, the inference of QSPR models for polymeric material properties constitutes a complex problem because of the uncertainty introduced by the polydispersity of these materials. The main challenge is how to capture the polydispersity information from the molecular weight distribution (MWD) curve to achieve a more effective computational representation of polymeric materials. To date, most of the existing QSPR techniques use only a single molecule to represent each of these materials, but polydispersity is not considered. Consequently, QSPR models obtained by these approaches are being oversimplified. For this reason, we introduced in a previous work a new FS algorithm called Feature

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General approach to polymer properties dependent on molecular characteristics

Cited by 21 publications

References 50 publications

Molecular Mobility of Molecular Brushes With a Polyimide Backbone and Polymethylmethacrylate Side Chains of Different Lengths

Molecular Mobility of Molecular Brushes With a Polyimide Backbone and Polymethylmethacrylate Side Chains of Different Lengths

Development of a methodology to predict material properties from environmental exposure. II. Structural features

Feature Selection for Polymer Informatics: Evaluating Scalability and Robustness of the FS4RV_DD Algorithm Using Synthetic Polydisperse Data Sets

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General approach to polymer properties dependent on molecular characteristics

Cited by 21 publications

References 50 publications

Molecular Mobility of Molecular Brushes With a Polyimide Backbone and Polymethylmethacrylate Side Chains of Different Lengths

Molecular Mobility of Molecular Brushes With a Polyimide Backbone and Polymethylmethacrylate Side Chains of Different Lengths

Development of a methodology to predict material properties from environmental exposure. II. Structural features

Feature Selection for Polymer Informatics: Evaluating Scalability and Robustness of the FS4RVDD Algorithm Using Synthetic Polydisperse Data Sets

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Product

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Feature Selection for Polymer Informatics: Evaluating Scalability and Robustness of the FS4RV_DD Algorithm Using Synthetic Polydisperse Data Sets