设计具有高强度、高韧性和低磁滞的珍珠项链结构的新型全聚合物纳米复合材料

IF 5.1 1区 化学 Q1 POLYMER SCIENCE Macromolecules Pub Date : 2024-10-19 DOI:10.1021/acs.macromol.4c01486
Tongkui Yue, Xin Zou, Hengheng Zhao, Yulong Chen, Liqun Zhang, Jun Liu
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Primitive path analysis revealed that the pearl necklace chains endow a greater degree of penetration between SNPs and polymer. The confinement effects of cross-linking networks alter the diffusion dynamics of SNPs embedded within polymer chains. The restricted displacement fluctuation distance <i></i><span style=\"color: inherit;\"></span><span data-mathml='&lt;math xmlns=\"http://www.w3.org/1998/Math/MathML\" display=\"inline\"&gt;&lt;msubsup&gt;&lt;mrow&gt;&lt;mi&gt;d&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mrow&gt;&lt;mi&gt;fluct&lt;/mi&gt;&lt;/mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mrow&gt;&lt;mi&gt;SNP&lt;/mi&gt;&lt;/mrow&gt;&lt;/mrow&gt;&lt;/msubsup&gt;&lt;/math&gt;' role=\"presentation\" style=\"position: relative;\" tabindex=\"0\"><nobr aria-hidden=\"true\"><span style=\"width: 2.513em; display: inline-block;\"><span style=\"display: inline-block; position: relative; width: 2.052em; height: 0px; font-size: 122%;\"><span style=\"position: absolute; clip: rect(1.13em, 1002.05em, 2.615em, -999.997em); top: -2.2em; left: 0em;\"><span><span><span style=\"display: inline-block; position: relative; width: 2.052em; height: 0px;\"><span style=\"position: absolute; clip: rect(3.128em, 1000.51em, 4.152em, -999.997em); top: -3.993em; left: 0em;\"><span><span style=\"font-family: MathJax_Math-italic;\">d<span style=\"display: inline-block; overflow: hidden; height: 1px; width: 0.003em;\"></span></span></span><span style=\"display: inline-block; width: 0px; height: 3.998em;\"></span></span><span style=\"position: absolute; clip: rect(3.332em, 1001.49em, 4.152em, -999.997em); top: -4.403em; left: 0.566em;\"><span><span><span style=\"font-size: 70.7%; font-family: MathJax_Main;\">SNP</span></span></span><span style=\"display: inline-block; width: 0px; height: 3.998em;\"></span></span><span style=\"position: absolute; clip: rect(3.332em, 1001.49em, 4.152em, -999.997em); top: -3.737em; left: 0.515em;\"><span><span><span style=\"font-size: 70.7%; font-family: MathJax_Main;\">fluct</span></span></span><span style=\"display: inline-block; width: 0px; height: 3.998em;\"></span></span></span></span></span><span style=\"display: inline-block; width: 0px; height: 2.205em;\"></span></span></span><span style=\"display: inline-block; overflow: hidden; vertical-align: -0.372em; border-left: 0px solid; width: 0px; height: 1.566em;\"></span></span></nobr><span role=\"presentation\"><math display=\"inline\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><msubsup><mrow><mi>d</mi></mrow><mrow><mrow><mi>fluct</mi></mrow></mrow><mrow><mrow><mi>SNP</mi></mrow></mrow></msubsup></math></span></span><script type=\"math/mml\"><math display=\"inline\"><msubsup><mrow><mi>d</mi></mrow><mrow><mrow><mi>fluct</mi></mrow></mrow><mrow><mrow><mi>SNP</mi></mrow></mrow></msubsup></math></script> of SNPs in the S<sub>N</sub> was obtained by the van Hove function <i>G</i><sub><i>s</i></sub>(<i>r</i>, Δ<i>t</i>), a typical linear correlation between the <i></i><span style=\"color: inherit;\"></span><span data-mathml='&lt;math xmlns=\"http://www.w3.org/1998/Math/MathML\" display=\"inline\"&gt;&lt;msubsup&gt;&lt;mrow&gt;&lt;mi&gt;d&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mrow&gt;&lt;mi&gt;fluct&lt;/mi&gt;&lt;/mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mrow&gt;&lt;mi&gt;SNP&lt;/mi&gt;&lt;/mrow&gt;&lt;/mrow&gt;&lt;/msubsup&gt;&lt;/math&gt;' role=\"presentation\" style=\"position: relative;\" tabindex=\"0\"><nobr aria-hidden=\"true\"><span style=\"width: 2.513em; display: inline-block;\"><span style=\"display: inline-block; position: relative; width: 2.052em; height: 0px; font-size: 122%;\"><span style=\"position: absolute; clip: rect(1.13em, 1002.05em, 2.615em, -999.997em); top: -2.2em; left: 0em;\"><span><span><span style=\"display: inline-block; position: relative; width: 2.052em; height: 0px;\"><span style=\"position: absolute; clip: rect(3.128em, 1000.51em, 4.152em, -999.997em); top: -3.993em; left: 0em;\"><span><span style=\"font-family: MathJax_Math-italic;\">d<span style=\"display: inline-block; overflow: hidden; height: 1px; width: 0.003em;\"></span></span></span><span style=\"display: inline-block; width: 0px; height: 3.998em;\"></span></span><span style=\"position: absolute; clip: rect(3.332em, 1001.49em, 4.152em, -999.997em); top: -4.403em; left: 0.566em;\"><span><span><span style=\"font-size: 70.7%; font-family: MathJax_Main;\">SNP</span></span></span><span style=\"display: inline-block; width: 0px; height: 3.998em;\"></span></span><span style=\"position: absolute; clip: rect(3.332em, 1001.49em, 4.152em, -999.997em); top: -3.737em; left: 0.515em;\"><span><span><span style=\"font-size: 70.7%; font-family: MathJax_Main;\">fluct</span></span></span><span style=\"display: inline-block; width: 0px; height: 3.998em;\"></span></span></span></span></span><span style=\"display: inline-block; width: 0px; height: 2.205em;\"></span></span></span><span style=\"display: inline-block; overflow: hidden; vertical-align: -0.372em; border-left: 0px solid; width: 0px; height: 1.566em;\"></span></span></nobr><span role=\"presentation\"><math display=\"inline\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><msubsup><mrow><mi>d</mi></mrow><mrow><mrow><mi>fluct</mi></mrow></mrow><mrow><mrow><mi>SNP</mi></mrow></mrow></msubsup></math></span></span><script type=\"math/mml\"><math display=\"inline\"><msubsup><mrow><mi>d</mi></mrow><mrow><mrow><mi>fluct</mi></mrow></mrow><mrow><mrow><mi>SNP</mi></mrow></mrow></msubsup></math></script> and the mesh size of the polymer matrix cross-linked network ⟨<i>L</i><sub>c</sub>⟩. The distinctive structural and dynamic behaviors of the S<sub>N</sub> are prominently reflected in the macroscopic mechanical properties. Stress decomposition analysis reveals that SNPs predominantly bear stress at low strain, whereas at high strain polymer dominates, akin to the reinforcement mechanism observed in dual polymer network of hydrogels. Moreover, SNPs within the S<sub>N</sub> exhibit greater deformation and slower recovery rates compared to S<sub>T</sub>, resulting in a 21.3% reduction in hysteresis loss. The toughness of the composites was evaluated through triaxial stretching. The S<sub>N</sub> exhibits more uniform distribution of fibrils along the stretching direction, thereby enhancing crack resistance and increasing dissipated work by approximately 50% compared to S<sub>T</sub>. In summary, this proposed novel pearl necklace structure opens a new avenue to balance the strength–toughness–hysteresis of polymer nanocomposites.","PeriodicalId":51,"journal":{"name":"Macromolecules","volume":null,"pages":null},"PeriodicalIF":5.1000,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Designing Novel All-Polymer Nanocomposites with Pearl Necklace Chain Structure with High Strength, High Toughness, and Low Hysteresis\",\"authors\":\"Tongkui Yue, Xin Zou, Hengheng Zhao, Yulong Chen, Liqun Zhang, Jun Liu\",\"doi\":\"10.1021/acs.macromol.4c01486\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Service performance can be significantly improved by adding nanofillers into polymers. However, entropy effects and enthalpic interactions between traditional inorganic fillers and polymers impede the simultaneous attainment of high strength and strong toughness. Polymer-based soft nanoparticles (SNPs) have emerged as promising candidates for achieving a balance between strength and toughness. To fully harness the deformability potential of SNPs and achieve superior mechanical performance, the pearl necklace structure was designed by employing molecular dynamics simulation. Compared to traditional all-polymer nanocomposite system (S<sub>T</sub>) composed of directly mixing polymer and SNPs, the SNPs in our novel system (S<sub>N</sub>) exhibit better dispersion and compatibility. Primitive path analysis revealed that the pearl necklace chains endow a greater degree of penetration between SNPs and polymer. The confinement effects of cross-linking networks alter the diffusion dynamics of SNPs embedded within polymer chains. The restricted displacement fluctuation distance <i></i><span style=\\\"color: inherit;\\\"></span><span data-mathml='&lt;math xmlns=\\\"http://www.w3.org/1998/Math/MathML\\\" display=\\\"inline\\\"&gt;&lt;msubsup&gt;&lt;mrow&gt;&lt;mi&gt;d&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mrow&gt;&lt;mi&gt;fluct&lt;/mi&gt;&lt;/mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mrow&gt;&lt;mi&gt;SNP&lt;/mi&gt;&lt;/mrow&gt;&lt;/mrow&gt;&lt;/msubsup&gt;&lt;/math&gt;' role=\\\"presentation\\\" style=\\\"position: relative;\\\" tabindex=\\\"0\\\"><nobr aria-hidden=\\\"true\\\"><span style=\\\"width: 2.513em; display: inline-block;\\\"><span style=\\\"display: inline-block; position: relative; width: 2.052em; height: 0px; font-size: 122%;\\\"><span style=\\\"position: absolute; clip: rect(1.13em, 1002.05em, 2.615em, -999.997em); top: -2.2em; left: 0em;\\\"><span><span><span style=\\\"display: inline-block; position: relative; width: 2.052em; height: 0px;\\\"><span style=\\\"position: absolute; clip: rect(3.128em, 1000.51em, 4.152em, -999.997em); top: -3.993em; left: 0em;\\\"><span><span style=\\\"font-family: MathJax_Math-italic;\\\">d<span style=\\\"display: inline-block; overflow: hidden; height: 1px; width: 0.003em;\\\"></span></span></span><span style=\\\"display: inline-block; width: 0px; height: 3.998em;\\\"></span></span><span style=\\\"position: absolute; clip: rect(3.332em, 1001.49em, 4.152em, -999.997em); top: -4.403em; left: 0.566em;\\\"><span><span><span style=\\\"font-size: 70.7%; font-family: MathJax_Main;\\\">SNP</span></span></span><span style=\\\"display: inline-block; width: 0px; height: 3.998em;\\\"></span></span><span style=\\\"position: absolute; clip: rect(3.332em, 1001.49em, 4.152em, -999.997em); top: -3.737em; left: 0.515em;\\\"><span><span><span style=\\\"font-size: 70.7%; font-family: MathJax_Main;\\\">fluct</span></span></span><span style=\\\"display: inline-block; width: 0px; height: 3.998em;\\\"></span></span></span></span></span><span style=\\\"display: inline-block; width: 0px; height: 2.205em;\\\"></span></span></span><span style=\\\"display: inline-block; overflow: hidden; vertical-align: -0.372em; border-left: 0px solid; width: 0px; height: 1.566em;\\\"></span></span></nobr><span role=\\\"presentation\\\"><math display=\\\"inline\\\" xmlns=\\\"http://www.w3.org/1998/Math/MathML\\\"><msubsup><mrow><mi>d</mi></mrow><mrow><mrow><mi>fluct</mi></mrow></mrow><mrow><mrow><mi>SNP</mi></mrow></mrow></msubsup></math></span></span><script type=\\\"math/mml\\\"><math display=\\\"inline\\\"><msubsup><mrow><mi>d</mi></mrow><mrow><mrow><mi>fluct</mi></mrow></mrow><mrow><mrow><mi>SNP</mi></mrow></mrow></msubsup></math></script> of SNPs in the S<sub>N</sub> was obtained by the van Hove function <i>G</i><sub><i>s</i></sub>(<i>r</i>, Δ<i>t</i>), a typical linear correlation between the <i></i><span style=\\\"color: inherit;\\\"></span><span data-mathml='&lt;math xmlns=\\\"http://www.w3.org/1998/Math/MathML\\\" display=\\\"inline\\\"&gt;&lt;msubsup&gt;&lt;mrow&gt;&lt;mi&gt;d&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mrow&gt;&lt;mi&gt;fluct&lt;/mi&gt;&lt;/mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mrow&gt;&lt;mi&gt;SNP&lt;/mi&gt;&lt;/mrow&gt;&lt;/mrow&gt;&lt;/msubsup&gt;&lt;/math&gt;' role=\\\"presentation\\\" style=\\\"position: relative;\\\" tabindex=\\\"0\\\"><nobr aria-hidden=\\\"true\\\"><span style=\\\"width: 2.513em; display: inline-block;\\\"><span style=\\\"display: inline-block; position: relative; width: 2.052em; height: 0px; font-size: 122%;\\\"><span style=\\\"position: absolute; clip: rect(1.13em, 1002.05em, 2.615em, -999.997em); top: -2.2em; left: 0em;\\\"><span><span><span style=\\\"display: inline-block; position: relative; width: 2.052em; height: 0px;\\\"><span style=\\\"position: absolute; clip: rect(3.128em, 1000.51em, 4.152em, -999.997em); top: -3.993em; left: 0em;\\\"><span><span style=\\\"font-family: MathJax_Math-italic;\\\">d<span style=\\\"display: inline-block; overflow: hidden; height: 1px; width: 0.003em;\\\"></span></span></span><span style=\\\"display: inline-block; width: 0px; height: 3.998em;\\\"></span></span><span style=\\\"position: absolute; clip: rect(3.332em, 1001.49em, 4.152em, -999.997em); top: -4.403em; left: 0.566em;\\\"><span><span><span style=\\\"font-size: 70.7%; font-family: MathJax_Main;\\\">SNP</span></span></span><span style=\\\"display: inline-block; width: 0px; 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引用次数: 0

摘要

通过在聚合物中添加纳米填料,可大大提高其使用性能。然而,传统无机填料与聚合物之间的熵效应和焓相互作用阻碍了高强度和强韧性的同时实现。基于聚合物的软纳米粒子(SNPs)已成为实现强度和韧性平衡的理想候选材料。为了充分利用 SNP 的变形潜力并实现优异的机械性能,我们通过分子动力学模拟设计了珍珠项链结构。与传统的聚合物与 SNP 直接混合的全聚合物纳米复合材料体系(ST)相比,我们的新型体系(SN)中的 SNP 具有更好的分散性和相容性。原始路径分析显示,珍珠项链使 SNP 与聚合物之间的渗透程度更高。交联网络的限制效应改变了嵌入聚合物链中的 SNP 的扩散动力学。SN 中 SNP 的限制位移波动距离 dSNPfluctdfluctSNPdfluctSNP 是由 van Hove 函数 Gs(r, Δt)求得的,dSNPfluctdfluctSNPdfluctSNP 与聚合物基体交联网络的网格尺寸⟨Lc⟩呈典型的线性相关。SN的独特结构和动态行为在宏观力学性能中得到了显著反映。应力分解分析表明,SNPs 在低应变时主要承受应力,而在高应变时则以聚合物为主,这与在水凝胶的双聚合物网络中观察到的增强机制类似。此外,与 ST 相比,SN 内的 SNP 表现出更大的变形和更慢的恢复速度,从而使滞后损失减少了 21.3%。通过三轴拉伸评估了复合材料的韧性。与 ST 相比,SN 沿着拉伸方向显示出更均匀的纤维分布,从而提高了抗裂性并增加了约 50% 的耗散功。总之,这种新型珍珠项链结构为平衡聚合物纳米复合材料的强度-韧性-滞后性开辟了一条新途径。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Designing Novel All-Polymer Nanocomposites with Pearl Necklace Chain Structure with High Strength, High Toughness, and Low Hysteresis
Service performance can be significantly improved by adding nanofillers into polymers. However, entropy effects and enthalpic interactions between traditional inorganic fillers and polymers impede the simultaneous attainment of high strength and strong toughness. Polymer-based soft nanoparticles (SNPs) have emerged as promising candidates for achieving a balance between strength and toughness. To fully harness the deformability potential of SNPs and achieve superior mechanical performance, the pearl necklace structure was designed by employing molecular dynamics simulation. Compared to traditional all-polymer nanocomposite system (ST) composed of directly mixing polymer and SNPs, the SNPs in our novel system (SN) exhibit better dispersion and compatibility. Primitive path analysis revealed that the pearl necklace chains endow a greater degree of penetration between SNPs and polymer. The confinement effects of cross-linking networks alter the diffusion dynamics of SNPs embedded within polymer chains. The restricted displacement fluctuation distance dfluctSNP of SNPs in the SN was obtained by the van Hove function Gs(r, Δt), a typical linear correlation between the dfluctSNP and the mesh size of the polymer matrix cross-linked network ⟨Lc⟩. The distinctive structural and dynamic behaviors of the SN are prominently reflected in the macroscopic mechanical properties. Stress decomposition analysis reveals that SNPs predominantly bear stress at low strain, whereas at high strain polymer dominates, akin to the reinforcement mechanism observed in dual polymer network of hydrogels. Moreover, SNPs within the SN exhibit greater deformation and slower recovery rates compared to ST, resulting in a 21.3% reduction in hysteresis loss. The toughness of the composites was evaluated through triaxial stretching. The SN exhibits more uniform distribution of fibrils along the stretching direction, thereby enhancing crack resistance and increasing dissipated work by approximately 50% compared to ST. In summary, this proposed novel pearl necklace structure opens a new avenue to balance the strength–toughness–hysteresis of polymer nanocomposites.
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来源期刊
Macromolecules
Macromolecules 工程技术-高分子科学
CiteScore
9.30
自引率
16.40%
发文量
942
审稿时长
2 months
期刊介绍: Macromolecules publishes original, fundamental, and impactful research on all aspects of polymer science. Topics of interest include synthesis (e.g., controlled polymerizations, polymerization catalysis, post polymerization modification, new monomer structures and polymer architectures, and polymerization mechanisms/kinetics analysis); phase behavior, thermodynamics, dynamic, and ordering/disordering phenomena (e.g., self-assembly, gelation, crystallization, solution/melt/solid-state characteristics); structure and properties (e.g., mechanical and rheological properties, surface/interfacial characteristics, electronic and transport properties); new state of the art characterization (e.g., spectroscopy, scattering, microscopy, rheology), simulation (e.g., Monte Carlo, molecular dynamics, multi-scale/coarse-grained modeling), and theoretical methods. Renewable/sustainable polymers, polymer networks, responsive polymers, electro-, magneto- and opto-active macromolecules, inorganic polymers, charge-transporting polymers (ion-containing, semiconducting, and conducting), nanostructured polymers, and polymer composites are also of interest. Typical papers published in Macromolecules showcase important and innovative concepts, experimental methods/observations, and theoretical/computational approaches that demonstrate a fundamental advance in the understanding of polymers.
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