Simultaneous Deconvolution of Molecular Weight and Chemical Composition Distribution of Ethylene/1‐Olefin Copolymers: Strategy Validation and Comparison

Anantawaraskul, Siripon; Bongsontia, Warawut; Soares, João B. P.

doi:10.1002/mren.201100032

Cited by 22 publications

(6 citation statements)

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“…[ 158 ] The next logical step in microstructural modelling techniques was to deconvolute the joint MWD × CCD that can be so beautifully measured by CFC (third row, Table 1). Anantawaraskul et al [ 127 ] compared four different deconvolution strategies using digital ethylene/1‐olefin copolymer microstructures simulated with a five site‐type catalyst: (1) MWD–short chain branch (SCB) (see the next paragraph for more details), (2) simultaneous MWD and CCD, (3) simultaneous MWD–SCB and CCD, and (4) joint MWD × CCD. They found that deconvolution of the joint MWD × CCD generated the most accurate estimates of the parameters used to create the digital microstructures, but these models didn't include non‐ideal effects such as peak broadening and co‐crystallization effects.…”

Section: Looking Back At the Literaturementioning

confidence: 99%

Polyolefin microstructural deconvolution methods: The good, the bad, and the ugly

Soares

2023

Can J Chem Eng

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The deconvolution of the molecular weight distribution (MWD) of polyolefins into Schultz–Flory most probable distributions has become the standard method to identify the number of site types on multiple‐site‐type olefin polymerization catalysts such as Ziegler–Natta, Phillips, and some supported metallocenes. This method has been used to quantify the effect of polymerization conditions and catalyst formulations on polyolefin MWD and olefin polymerization kinetics. Related methods have also been developed to deconvolute other polyolefin microstructure features, such as the chemical composition and comonomer sequence length distributions. In this paper, I explain the premises behind these deconvolution models and review the publications in this area, highlighting the advantages, disadvantages, and misuses of these methods. I also propose a revised formulation on how to model the MWD of polyolefins made with multiple‐site‐type catalysts using ratio distributions for propagation and chain transfer frequencies. The main objective of this overview article is to highlight the strengths, but also show the pitfalls, of polyolefin microstructure deconvolution methods.

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Section: Looking Back At the Literaturementioning

confidence: 99%

Polyolefin microstructural deconvolution methods: The good, the bad, and the ugly

Soares

2023

Can J Chem Eng

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“…Therefore, the deconvolution of the measured MWDs and CCDsan inverse computational techniquecan determine the number of types of active catalyst sites, that is, the catalyst active-center distribution, and model the corresponding backbone microstructures (MWD and CCD). The mathematical development of the aforementioned deconvolution model and the associated computational algorithm are detailed in the literature. − …”

Section: Theoretical Backgroundmentioning

confidence: 99%

Effects of Supported (ⁿBuCp)₂ZrCl₂Catalyst Active-Center Distribution on Ethylene–1-Hexene Copolymer Backbone Heterogeneity and Thermal Behaviors

Atiqullah¹,

Anantawaraskul

Emwas

et al. 2013

Ind. Eng. Chem. Res.

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Two catalysts, denoted as catalyst 1 [silica/MAO/( n BuCp)2ZrCl2] and catalyst 2 [silica/ n BuSnCl3/MAO/( n BuCp)2ZrCl2] were synthesized and subsequently used to prepare, without separate feeding of methylaluminoxane (MAO), ethylene homopolymer 1 and homopolymer 2, respectively, and ethylene–1-hexene copolymer 1 and copolymer 2, respectively. Gel permeation chromatography (GPC), Crystaf, differential scanning calorimetry (DSC) [conventional and successive self-nucleation and annealing (SSA)], and 13C nuclear magnetic resonance (NMR) polymer characterization results were used, as appropriate, to model the catalyst active-center distribution, ethylene sequence (equilibrium crystal) distribution, and lamellar thickness distribution (both continuous and discrete). Five different types of active centers were predicted in each catalyst, as corroborated by the SSA experiments and complemented by an extended X-ray absorption fine structure (EXAFS) report published in the literature. 13C NMR spectroscopy also supported this active-center multiplicity. Models combined with experiments effectively illustrated how and why the active-center distribution and the variance in the design of the supported MAO anion, having different electronic and steric effects and coordination environments, influence the concerned copolymerization mechanism and polymer properties, including inter- and intrachain compositional heterogeneity and thermal behaviors. Copolymerization occurred according to the first-order Markovian terminal model, producing fairly random copolymers with minor skewedness toward blocky character. For each copolymer, the theoretical most probable ethylene sequences, n E MPDSC‑GT and n E MPNMR‑Flory, as well as the weight-average lamellar thicknesses, L wav DSC–GT and L wav SSA DSC, were found to be comparable. To the best of our knowledge, such a match has not previously been reported. The percentage crystallinities of the homo- and copolymers increased linearly as a function of L MPDSC‑GT. This indicates that the homo- and copolymer chains folded excluding the butyl branch. The results of the present study will contribute to developing future supported metallocene catalysts that will be useful in the synthesis of new grades of ethylene−α-olefin linear low-density polyethylenes (LLDPEs).

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“…[6] In order to better describe this broad MWD, models are applied to assign individual molar-mass fractions to active sites, and the MWD is considered as a blend of several narrow molar-mass fractions. [7,8] This inverse computational technique enables the prediction of the microstructure of the polymer, and the polymerization conditions can be adjusted accordingly. These models vary in complexity depending on whether mass and heat transfer resistance are considered.…”

Section: Introductionmentioning

confidence: 99%

Customizing the comonomer incorporation distribution in Ziegler–Natta‐based LLDPE: Harnessing the influences of the titanation temperature during catalyst synthesis and of polymerization process parameters

et al. 2023

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The microstructure of linear low‐density polyethylene (LLDPE) is strongly influenced by short‐chain branches (SCBs) incorporated into the polymer backbone. Varying the number, distribution, and length of SCBs allows the properties of the resulting polymer to be tailored to meet specific requirements. Using Ziegler–Natta (ZN) catalysts for synthesis has disadvantages in terms of the comonomer incorporation distribution (CID) compared to, for instance, metallocene and post–metallocene catalysts. Nevertheless, ZN catalysts continue to be widely used, as many of the new generations of catalysts are more difficult to handle and cannot match the cheap cost of ZN catalysts. To improve this aspect of ZN catalysts, we investigated the influence of catalyst titanation temperature and polymerization process parameters on the CID. Our results show that it is possible to manipulate the process parameters of the present ZN catalyst system to yield a desired comonomer amount and CID in the polymer. Varying the titanation temperature clearly influenced the titanium content of the catalyst. Molecular‐weight distribution analysis and deconvolution results indicate that changes in the amounts of comonomer incorporated and in the CID are directly related to the catalyst's active site that produces the lowest‐molecular‐weight fraction.

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Simultaneous Deconvolution of Molecular Weight and Chemical Composition Distribution of Ethylene/1‐Olefin Copolymers: Strategy Validation and Comparison

Cited by 22 publications

References 40 publications

Polyolefin microstructural deconvolution methods: The good, the bad, and the ugly

Polyolefin microstructural deconvolution methods: The good, the bad, and the ugly

Effects of Supported (ⁿBuCp)₂ZrCl₂Catalyst Active-Center Distribution on Ethylene–1-Hexene Copolymer Backbone Heterogeneity and Thermal Behaviors

Customizing the comonomer incorporation distribution in Ziegler–Natta‐based LLDPE: Harnessing the influences of the titanation temperature during catalyst synthesis and of polymerization process parameters

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Simultaneous Deconvolution of Molecular Weight and Chemical Composition Distribution of Ethylene/1‐Olefin Copolymers: Strategy Validation and Comparison

Cited by 22 publications

References 40 publications

Polyolefin microstructural deconvolution methods: The good, the bad, and the ugly

Polyolefin microstructural deconvolution methods: The good, the bad, and the ugly

Effects of Supported (nBuCp)2ZrCl2Catalyst Active-Center Distribution on Ethylene–1-Hexene Copolymer Backbone Heterogeneity and Thermal Behaviors

Customizing the comonomer incorporation distribution in Ziegler–Natta‐based LLDPE: Harnessing the influences of the titanation temperature during catalyst synthesis and of polymerization process parameters

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Product

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Effects of Supported (ⁿBuCp)₂ZrCl₂Catalyst Active-Center Distribution on Ethylene–1-Hexene Copolymer Backbone Heterogeneity and Thermal Behaviors