Kinetic Model for Off-Stoichiometric Cross-Linking Reactions of End-Linked Polymer Networks
Kinetic Model for Off-Stoichiometric Cross-Linking Reactions of End-Linked Polymer Networks
复制标题
末端连接聚合物网络的非化学计量交联反应的动力学模型
DOI:
10.1021/acs.macromol.3c00849
复制
发表时间:
2023
期刊:
影响因子:
5.5
通讯作者:
Olsen, Bradley D.
中科院分区:
文献类型:
--
作者:
Beech, Haley K.;Lin, Tzyy-Shyang;Mochigase, Hidenobu;Olsen, Bradley D.
The formation of end-linked polymer networks is commonly modeled as idealized chemical reactions, resulting in defect-free networks. However, many widely used industrial processes including platinum-catalyzed vinyl-silane cross-linking of poly(dimethylsiloxane) (PDMS) are mechanistically complex and involve a variety of side reactions. Here, a kinetic graph theory (KGT) model was updated to account for off-stoichiometric reactive groups and side reactions by adding two fitting parameters representing the relative rate of competing side reactions and the probability of side cross-linking events. The updated KGT outputs the population of each junction type from which the reaction fates of both starting materials are calculated. The elastic effectiveness of the resulting network is calculated with the nonlinear Miller–Macosko theory (MMT), updated to account for side reactions and side cross-linking. The MMT was validated on off-stoichiometric data and was chosen here for its ability to account for a range of effective junction functionalities. Combined, the updated KGT and MMT provide elasticity estimates that capture the experimental peak in elastic modulus observed at an off-stoichiometric silane/alkene ratio in PDMS networks. Both the Lake Thomas and micronetwork fracture theories were subsequently used to estimate the tearing energy, showing a similar peak at off-stoichiometric ratios in qualitative agreement with experimental data. This model is useful in systems where the cross-linking chemistry yields more complex reaction networks, making it relevant to many classes of polymer network chemistry where classical theories may not adequately capture network behavior.
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DOI:
--
发表时间:
1985
期刊:
影响因子:
--
作者:
C. Macosko;J. Saam
通讯作者:
J. Saam
影响因子:
5.5
作者:
PATEL, SK;MALONE, S;COLBY, RH
通讯作者:
COLBY, RH
影响因子:
5.5
作者:
Ben Xu;Jinrong Wu;G. McKenna
通讯作者:
Ben Xu;Jinrong Wu;G. McKenna
影响因子:
5.5
作者:
A. Sawvel;S. Chinn;Matthew Gee;C. Loeb;A. Maiti;H. Mason;R. Maxwell;J. Lewicki
通讯作者:
J. Lewicki
DOI:
--
发表时间:
1989
期刊:
影响因子:
--
作者:
X. Quan
通讯作者:
X. Quan