Stereospecific Polymerization of Conjugated Diolefins. Note Iv: Preparation of Syndiotactic 1,2-Polybutadiene

Natta, G.; Porri, L.; Zanini, G.; Fiore, L.

doi:10.1016/b978-1-4831-9882-8.50045-2

Cited by 8 publications

(8 citation statements)

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“…It is interesting that, even when V(acac),-MA0 and Cr(acac),-MAO catalysts were used, the cis-1,4 content was 65% and 63%, respectively, in contrast to the previously reported results that Cr(acac),-A1(C2H5), 22) and V(acac),-A1(CZH5), 23) catalysts produce polymers with predominant 1,2 microstructure. These results indicate that M A 0 plays an important role in producing cis-1,4 microstructure of the polymers.…”

Section: Electronegativitycontrasting

confidence: 50%

Polymerizations of butadiene with Ni(acac)₂‐methylaluminoxane catalysts

Endo

Uchida

Matsuda

1996

Macro Chemistry & Physics

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Polymerizations of butadiene (BD) with transition metals (MtX) and methylaluminoxane (MAO) catalysts were studied. Among the catalysts examined, nickel(I1) acetylacetonate [Ni(acach] in combination with MA0 revealed the highest catalytic activities for the polymerization of BD, giving high molecular weight polymers consisting of mainly cis-1,4-units in the microstructure. With Ni(aca~)~-MA0 catalyst, the overall activation energy for the polymerization of BD between 0°C and 60°C was estimated to be 18 kJ/mol. From a kinetic study, the rate equation for the polymerization of BD with the Ni(acac),-MA0 catalyst at 30°C can be expressed as follows: R, = [BD]'.* [Ni(a~ac)~-MA0]'.'. The polymerization mechanism of the polymerization of BD with the Ni(acac),-MA0 catalyst is discussed.

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Section: Electronegativitycontrasting

confidence: 50%

Polymerizations of butadiene with Ni(acac)₂‐methylaluminoxane catalysts

Endo

Uchida

Matsuda

1996

Macro Chemistry & Physics

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“…[87][88][89][90] Table IV summarizes the typical examples of stereospecific polymerization of 1,3-butadiene to give highly controlled stereoregular polymers. [91][92][93][94][95][96][97][98][99][100] The stereochemical structure of polymers produced from 1,4-disubstituted butadienes is represented not only by cis-trans isomerism but also by isotactic-syndiotactic and erythro-threo (or mesoracemo) relationships. 48 Stereoregular polymers from 1,4-disubstituted butadienes are referred to as tritactic polymers due to the presence of three elements of stereoisomerism for each monomer unit, two pseudoasymmetric carbon centers and a double bond.…”

Section: Tacticitymentioning

confidence: 99%

Polymer Structure Control Based on Crystal Engineering for Materials Design

Matsumoto

2003

Polym J

111

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ABSTRACT:In this review article, polymer structure control and organic material design based on polymer crystal engineering are described. The structures and properties of crystalline materials are designed using pre-organized molecules through various intermolecular interactions such as hydrogen bonds, π · · · π, CH/π, CH/O, and halogen interactions. Here, we describe the features and mechanisms of the topochemical polymerization of 1,3-diene monomers including some ester, ammonium, and amide derivatives of muconic and sorbic acids, which are 1,3-diene di-and monocarboxylic acid derivatives, respectively. We have proposed the topochemical polymerization principles for diene monomers on the basis of the crystallographic data accumulated for various kinds of diene monomers. The combination of several intermolecular interactions is useful for the construction of molecular packing appropriate for 5 Å stacking in order to facilitate the topochemical polymerization in the crystalline state. We refer to the control of polymer chain structure including tacticity, molecular weight, and ladder structure, and also polymer crystal structures, as well as the organic intercalation system using layered polymer crystals obtained by the topochemical polymerization. A totally solvent-free system for the synthesis of layered polymer crystals is also described.KEY WORDS Topochemical Polymerization / Solid-State Reaction / Crystal Engineering / Supramolecular Synthon / Stereoregular Polymer / Controlled Radical Polymerization / X-Ray Single Crystal Structure Analysis / Intercalation / Controlled polymer synthesis, including the control of polymer chain structures such as molecular weight, molecular weight distribution, chain-end structure, branching, regioselectivity, and stereoselectivity, has great potential for the design of macromolecular architecture leading to advanced nano-materials and devices. [1][2][3][4][5][6][7][8][9][10][11] All the features and functions of polymers importantly depend on primary chain structures and the manner of polymer chain assembly in crystalline and non-crystalline solids, or in a condensed state. Therefore, the control of polymer chain structures in polymer synthesis can hardly be overestimated for the architecture of advanced polymeric materials.Especially, the stereochemistry of polymers has received significant attention in both fundamental and applied fields ever since the first discoveries of stereoregular polymers in the 1950s. 12-14 Highly controlled stereospecific polymerization has been well established by coordination polymerization of olefins and diene monomers and by anionic polymerization of certain types of polar monomers. Radical polymerization is the most important and convenient process for the production of various kinds of vinyl and diene polymers due to recent achievements in well-controlled polymerizations in addition to the classical advantages of the radical process. [15][16][17][18][19][20][21][22] There are two fundamentally different ways to control of the chain structu...

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“…Ziegler-Natta催化剂是以IV-VIIB族过渡金属为主催化剂, I-IIIA族烷基金属为助催化剂, 用于α-烯烃聚合的一类高效催化剂. 1953年, 德国有机金属化学家Karl Ziegler发现利用TiCl 4 /AlEt 3 催化剂可以催化乙烯聚合 [26] , 随后意大利化学家Giulio Natta很快启动丙烯聚合研究 [27,28] 泛函理论(Density Functional Theory, DFT)的方法考察Ziegler-Natta催化剂的催化聚合反应机理. Cavallo研究组 [29] 对非均相Ziegler-Natta催化剂的DFT研究进展进行了详细的综述, 包括α-烯烃聚合中立体选择性的起源、反应链终止的机理以及杂质在Ziegler-Natta催化剂中的作用等.…”

Section: 机器学习在Ziegler-natta催化剂中的应用unclassified

Application and prospect of machine learning in polyolefin catalysts

Yang¹,

Sun²

2022

Chin. Sci. Bull.

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Industrial and academic research has been extensively inspired by the ever-growing demand of polyolefin with high performance due to its special physical and mechanical properties, which has been widely applied in the area of engineering plastics, elastomer and high grade lubricants. Transition metal complex catalysts, which can make the olefin polymerization reaction feasible, have been one of the key techniques to produce polyolefin with various structures and properties. Although many fruitful reports are available describing different attempts on enhancing the performance of polyolefin catalyst by the means of the alteration of the ligand framew orks, shuffling the substituents as well as introducing of new ligands. Nevertheless, the traditional process of catalyst development, using the trial-and-error method, usually needs long experimental steps and period s. Meanwhile, the measurement of catalytic performance is high cost and needs a lot of resources as well. Machine learning, as the core strategy of artificial intelligence, has shown strong predictive power in many fields of science and technology. However, the application in chemistry, especially in catalysis, is still in its infancy. Relying on the rapid development of different a lgorithms and computer hardware, it is the right time to harvest the potential of machine learning in the field of catalysis across academy and industry. Herein, we discuss the recent advances in the field of polyolefin catalysts by using machine learning methods, including Ziegler-Natta catalysts, phosphine monocyclic imine Cr(P,N) catalysts, ansa zirconocene catalysts, and late-transition metal complex catalysts. The catalytic performance is well predicted, providing the insight into the underlying mechanism of relationship between the micro-structure of catalyst and its macro-performance at the molecule level.Tracing the recent progress, the report of machine learning in polyolefin catalysts is relatively few. One of the main reasons is that the experimental study of catalytic performance is time -consuming and labor-intensive and it is difficult to establish a big data set from kinetic studies. However, the recent work by Cavallo suggests that the model with good prediction and validation results can still be obtained, even though the model catalysts in data set are selected from different laboratories. This may inspire researchers from different groups to contribute their experimental data together and share with each other. On the other hand, compared with supervised machine learning, the unsupervised methods have the advantage of low dependence on experimental data and may effectively solve the problem of building large data set. To the best of our knowledge, modern statistical learning techniques will be a strong tool for computational optimization and discovery. This perspective provides a platform for an integrate d machine learning technique toward new design in the field of polyolefin catalysts, which is expected to change the traditional way by lowering the cost ...

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Stereospecific Polymerization of Conjugated Diolefins. Note Iv: Preparation of Syndiotactic 1,2-Polybutadiene

Cited by 8 publications

References 0 publications

Polymerizations of butadiene with Ni(acac)₂‐methylaluminoxane catalysts

Polymerizations of butadiene with Ni(acac)₂‐methylaluminoxane catalysts

Polymer Structure Control Based on Crystal Engineering for Materials Design

Application and prospect of machine learning in polyolefin catalysts

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Stereospecific Polymerization of Conjugated Diolefins. Note Iv: Preparation of Syndiotactic 1,2-Polybutadiene

Cited by 8 publications

References 0 publications

Polymerizations of butadiene with Ni(acac)2‐methylaluminoxane catalysts

Polymerizations of butadiene with Ni(acac)2‐methylaluminoxane catalysts

Polymer Structure Control Based on Crystal Engineering for Materials Design

Application and prospect of machine learning in polyolefin catalysts

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Product

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Polymerizations of butadiene with Ni(acac)₂‐methylaluminoxane catalysts

Polymerizations of butadiene with Ni(acac)₂‐methylaluminoxane catalysts