【新文速递】2024年1月25日复合材料SCI期刊最新文章

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今日更新：Composite Structures 1 篇，Composites Part B: Engineering 4 篇

Composite Structures

Multi-objective optimization for designing porous scaffolds with controllable mechanics and permeability: a case study on triply periodic minimal surface scaffolds

Zhitong Li, Zhaobo Chen, Xiongbiao Chen, Runchao Zhao

doi:10.1016/j.compstruct.2024.117923

力学和渗透率可控多孔支架的多目标优化设计——以三周期最小表面支架为例

Design of tissue scaffolds in terms of multiple objectives such as mechanical and biological performances simultaneously remains challenging. In this paper, we present the development of a topology optimization approach that enables to design porous scaffolds with consideration of both mechanical and fluid properties simultaneously. The relationship between structural parameters, mechanical and mass transport properties was investigated, and then the scaffolds were optimized under the constraint of given pore size, porosity and specific surface area. Case studies on the optimized design of triply periodic minimal surface (TPMS) scaffolds were performed to illustrate the effective of our approach. Simulation and experimental results demonstrated that the proposed optimization approach fully decouples the mechanical and mass transport properties, specifically, under given porosities, the elastic modulus and permeability of optimized scaffolds ranged from 1.33 GPa to 16.7 GPa and 0.07×10−8 m2 to 1.62×10−8 m2, respectively. Three types of optimized gradient scaffolds were designed and they all exhibited significant functional gradients and high adjustability. The proposed approach decouples synergy and enables individual customization of multi-physics properties, providing practical guidance for gradient scaffolds with desired performance.

同时兼顾机械和生物性能等多种目标的组织支架设计仍然具有挑战性。在本文中，我们提出了一种拓扑优化方法的发展，该方法可以同时考虑机械和流体特性来设计多孔支架。研究了结构参数、力学性能和传质性能之间的关系，并在给定孔径、孔隙率和比表面积的约束下对支架进行了优化。以三周期最小表面(TPMS)支架的优化设计为例，验证了该方法的有效性。仿真和实验结果表明，所提出的优化方法完全解耦了支架的力学性能和质量传递性能，特别是在给定孔隙率下，优化后支架的弹性模量和渗透率分别在1.33 ~ 16.7 GPa和0.07×10 ~ 1.62×10 ~ 8 m2之间。设计了3种优化后的梯度支架，均具有显著的功能梯度和较高的可调节性。所提出的方法解耦了协同作用，实现了多物理场属性的个性化定制，为具有理想性能的梯度支架提供了实用指导。

Composites Part B: Engineering

A 3D printable gelatin methacryloyl/chitosan hydrogel assembled with conductive PEDOT for neural tissue engineering

Ying Han, Mouyuan Sun, Xingchen Lu, Kailei Xu, Mengfei Yu, Huayong Yang, Jun Yin

doi:10.1016/j.compositesb.2024.111241

一种可3D打印的明胶甲基丙烯酰/壳聚糖水凝胶与导电PEDOT组装用于神经组织工程

In neural tissue engineering, biomaterial scaffolds that have high conductivity and customized structures are crucial in promoting nerve regeneration. Poly(3,4-ethylenedioxythiophene) (PEDOT) has emerged as a promising conductive polymer with excellent chemical stability and biocompatibility. However, traditional three-dimensional (3D) printing of PEDOT-based conductive scaffolds faces challenges in limited printing resolution, poor solubility, and brittleness of conductive materials. Herein, digital light processing (DLP) printing was used to fabricate complex hydrogel structures using gelatin methacryloyl (GelMA) and chitosan (CS) while incorporating PEDOT nanoparticles through interfacial polymerization to create conducting pathways within a hydrogel structure. The integration of PEDOT significantly enhanced the electrical conductivity and mechanical properties of the GelMA/CS hydrogel while preserving printed details. The GelMA/CS-PEDOT hydrogel promoted cell proliferation and facilitated axon outgrowth of PC12 cells and Schwann cells during in vitro culture. Moreover, in vitro direct current electrical stimulation promoted axon elongation of PC12 cells cultured on a conductive substrate. In vivo studies used a conductive nerve conduit to repair a 10-mm rat sciatic nerve defect, validating the efficacy of GelMA/CS-PEDOT scaffold in peripheral nerve injury repair. These findings highlight the significant potential of conductive GelMA/CS-PEDOT hydrogel in the field of neural tissue engineering.

在神经组织工程中，具有高导电性和定制化结构的生物材料支架是促进神经再生的关键。聚(3,4-乙烯二氧噻吩)(PEDOT)是一种具有良好化学稳定性和生物相容性的导电聚合物。然而，传统的基于pedot的导电支架三维打印面临着打印分辨率有限、导电材料溶解度差、脆性等问题。在此，数字光处理(DLP)印刷被用于制造复杂的水凝胶结构，使用明胶甲基丙烯酰(GelMA)和壳聚糖(CS)，同时通过界面聚合结合PEDOT纳米粒子在水凝胶结构中创建导电途径。PEDOT的集成显著提高了GelMA/CS水凝胶的导电性和机械性能，同时保留了打印细节。GelMA/CS-PEDOT水凝胶在体外培养过程中促进细胞增殖，促进PC12 细胞和雪旺细胞轴突的生长。此外，体外直流电刺激促进在导电基质上培养的PC12 细胞轴突伸长。体内研究采用导电神经导管修复10mm大鼠坐骨神经缺损，验证了GelMA/CS-PEDOT支架修复周围神经损伤的有效性。这些发现突出了导电GelMA/CS-PEDOT水凝胶在神经组织工程领域的巨大潜力。

A combined engineering of hollow and core-shell structures for C@MoS2 microcapsules toward high-efficiency electromagnetic absorption

Yonglei Liu, Fengyuan Wang, Yahui Wang, Bo Hu, Ping Xu, Xijiang Han, Yunchen Du

doi:10.1016/j.compositesb.2024.111244

面向高效电磁吸收的C@MoS2微胶囊的中空和核壳结构组合工程

Rational design of profitable structure is widely considered as an effective strategy for electromagnetic wave absorbing materials (EWAMs) to improve their performance. Herein, we conduct a combined engineering of hollow and core-shell structures, in which surface shell is formed by a number of MoS2 nanosheets crossing each other and internal core is hollow carbon microcapsule (HC). HC has strong dielectric loss ability, and its hollow structure facilitates the multiple reflection of incident EM wave. The construction of MoS2 shells not only improves impedance matching but also introduces heterogeneous interfaces that result in interfacial polarization loss. It is found that the synergistic effect of intrinsic loss mechanisms and structural advantages endow C@MoS2 composites (HCM-x) with excellent EM wave absorption performance, and especially for HCM-2, whose minimum reflection loss (RL) intensity and effective absorption bandwidth (EAB) are −63.8 dB and 6.0 GHz, respectively. It is believed that this work is valuable for the enhancement of performance for EWAMs through the upgrade of microstructure and regulation of chemical composition.

合理设计有利结构被广泛认为是提高电磁波吸收材料性能的有效策略。在此，我们进行了空心和核壳结构的结合工程，其中表面壳是由多个MoS2纳米片相互交叉形成的，内部核心是空心碳微胶囊(HC)。HC具有较强的介质损耗能力，其中空结构有利于入射电磁波的多次反射。二硫化钼壳层的结构不仅改善了阻抗匹配，而且引入了导致界面极化损耗的非均质界面。研究发现，内在损耗机制和结构优势的协同作用使C@MoS2复合材料(HCM-x)具有优异的电磁波吸收性能，特别是HCM-2，其最小反射损耗(RL)强度和有效吸收带宽(EAB)分别为- 63.8 dB和6.0 GHz。研究结果表明，该研究对通过结构优化和化学成分调控来提高ewam的性能具有重要意义。

Nacre-inspired hierarchical framework enables tough and impact-monitoring epoxy nanocomposites

Da Li, Peng E, Yibo Shen, Yueshan Li, Li Liu, Yudong Huang, Zhen Hu

doi:10.1016/j.compositesb.2024.111246

受丙烯酸树脂启发的分层框架使坚固和冲击监测环氧纳米复合材料成为可能

Multifunctional epoxy nanocomposites hold great promise for artificial structural materials across civil, industrial, and aerospace fields but were severely impeded by catastrophic brittleness under damage and lack of chemical activity resulting from highly cross-linked networks, leading to poor toughness and absent functionality. Herein, we report a facile yet efficient strategy for fabrication of graphene-encapsulated natural rubber frameworks through nanogroove-based freeze-casting technology, leading to highly tough hierarchically biomimetic epoxy nanocomposites. As judiciously elucidated by physicochemical and morphological characterizations, graphene sheets encapsulated natural rubber were bidirectionally aligned through ice crystals expelled from oriented nanogrooved surface to build hierarchical layered architecture with nanoasperities. Moreover, notable crack deflection derived from the “brick-mortar” structure in epoxy composite and nanoasperities debonding at the interlocking “hard-soft-hard” interface dissipate large amount of energy during fracture process, resulting in boosted fracture toughness. Accordingly, a synergistic performance enhancement is achieved, i.e., more than 3.32-fold increase in fracture toughness at low rubber content (4.3 wt%), and efficient real-time impact self-monitoring and evaluation. This work investigated for the first time the role of graphene-encapsulated natural rubber framework with nanoasperities structure in the reinforcement of epoxy nanocomposites. The design approach outlines a way for high-toughness, multifunctional and long-lasting materials for various engineering applications.

多功能环氧纳米复合材料在民用、工业和航空航天领域的人造结构材料中具有很大的前景，但由于高度交联网络造成的破坏和缺乏化学活性，导致韧性差和功能缺失，严重阻碍了这种材料的发展。在此，我们报告了一种简单而有效的策略，通过基于纳米沟槽的冷冻铸造技术制造石墨烯封装的天然橡胶框架，从而产生高韧性的分层仿生环氧纳米复合材料。物理化学和形态学表征表明，包裹天然橡胶的石墨烯薄片通过定向纳米沟槽表面排出的冰晶双向排列，形成具有纳米颗粒的分层结构。此外，环氧复合材料中“砖-砂浆”结构引起的显著裂缝挠度和“硬-软-硬”互锁界面处的纳米颗粒脱粘，在断裂过程中耗散了大量能量，从而提高了断裂韧性。因此，实现了协同性能增强，即在低橡胶含量(4.3 wt%)下，断裂韧性提高了3.32倍以上，并实现了高效的实时冲击自我监测和评估。本文首次研究了具有纳米颗粒结构的石墨烯包封天然橡胶骨架在环氧纳米复合材料中的增强作用。该设计方法为各种工程应用提供了高韧性、多功能和持久的材料。

Polysaccharide hybrid scaffold encapsulated endogenous factors for microfracture enhancement by sustainable release and cell recruitment

Zhulian Li, Yuxiang Wang, Chengkun Zhao, Xing Li, Manyu Chen, Zhiwei Liu, Junli Liu, Yun Xiao, Yujiang Fan, Qing Jiang, Yong Sun, Xingdong Zhang

doi:10.1016/j.compositesb.2024.111235

多糖复合支架包封内源性因子，通过持续释放和细胞募集增强微断裂

Autologous matrix-induced chondrogenesis improves microfracture (MF), but the lack of functional cells and factors inhibit a curative effect. Here, inspired by the healing process of wounds, we prepared a polysaccharide hybrid scaffold (HDCP) to immobilize endogenous growth factors (eGFs), which were derived from platelet-rich plasma (PRP). Compared with the absence of PRP, the hybrid cross-linking network endowed this scaffold with a denser pore structure, enhanced mechanical properties, and slower protein mass transfer rate, thus ensuring the long-term sustainable release of eGFs. In vitro studies confirmed that HDCP promoted bone marrow mesenchymal stem cell recruitment, proliferation, and chondrogenic differentiation. In vivo implantation indicated that HDCP regenerated a smooth articular cartilage surface with a regular fibrous matrix arrangement structure, and the mechanical strength of the regenerated cartilage considerably improved. The obvious MF enhancement implied that PRP and polysaccharide scaffold hybridization could be used as an implantable matrix for cartilage repair.

自体基质诱导的软骨形成可改善微骨折(MF)，但缺乏功能性细胞和因子抑制其疗效。在这里，受伤口愈合过程的启发，我们制备了一种多糖杂交支架(HDCP)来固定来自富血小板血浆(PRP)的内源性生长因子(eGFs)。与不含PRP相比，混合交联网络使该支架的孔隙结构更致密，力学性能增强，蛋白质传质速率更慢，从而保证了eGFs的长期可持续释放。体外研究证实，HDCP促进骨髓间充质干细胞募集、增殖和软骨分化。体内植入实验表明，HDCP再生的关节软骨表面光滑，纤维基质排列结构规则，再生软骨的机械强度明显提高。明显的MF增强提示PRP与多糖支架杂交可作为软骨修复的植入基质。

来源：复合材料力学仿真Composites FEM

【新文速递】2024年1月14日复合材料SCI期刊最新文章

今日更新：Composite Structures 1 篇，Composites Part A: Applied Science and Manufacturing 1 篇，Composites Part B: Engineering 1 篇，Composites Science and Technology 1 篇Composite StructuresvAnalysis of cure kinetics of CFRP composites molding process using incremental thermochemical information aggregation networksBo Yang, Haoping Huang, Fengyang Bi, Liqiong Yin, Qi Yang, Hang Shendoi:10.1016/j.compstruct.2024.117904基于增量热化学信息聚合网络的CFRP复合材料成型过程固化动力学分析This work focuses on addressing the pain points of poor generalization performance and difficulty in continuous learning that exist in the phenomenological and neural network surrogate models. Therefore, this study proposes a lightweight adaptive thermochemical information aggregation networks (ATANets) to overcome the gradient conflict challenge, and combines the generative knowledge distillation (GKD) algorithm to compress the model to capture finer-grained and enriched information on kinetics behaviors, which yields a thermochemical feature information extraction networks (FENets) with incremental learning capability. The experimental results demonstrated that as the complexity of the learning task deepened, the FENets models obtained by incremental training with ATANets and GKD still had excellent continuous learning capability, with the relative variations of the coefficients of determination and the mean square error being smaller than 6.7×10-3 and 0.3860×10-3, respectively. Meanwhile, the accurate characterization of cure kinetics behaviors was achieved in the thermochemical coupling analysis of CFRP, with the maximum values of the average and maximum temperature differences of 0.0176 °C and 0.2538 °C, respectively. Overall results show that our proposed incremental model is remarkably preferable to existing models and is beneficial in promoting the widespread reuse of the existing knowledge of cure kinetics behavior of resins in this domain.这项工作的重点是解决现象学和神经网络代理模型中存在的泛化性能差和持续学习困难的痛点。因此，本研究提出了一种轻量级的自适应热化学信息聚合网络(ATANets)来克服梯度冲突挑战，并结合生成知识蒸馏(GKD)算法对模型进行压缩，以捕获更细粒度和更丰富的动力学行为信息，从而产生具有增量学习能力的热化学特征信息提取网络(FENets)。实验结果表明，随着学习任务复杂度的加深，使用ATANets和GKD增量训练得到的fenet模型仍然具有优异的连续学习能力，决定系数和均方误差的相对变化量分别小于6.7×10-3和0.3860×10-3。同时，在CFRP的热化学偶联分析中，实现了固化动力学行为的准确表征，平均温差最大值为0.0176℃，最大温差最大值为0.2538℃。总体结果表明，我们提出的增量模型明显优于现有模型，有利于促进该领域树脂固化动力学行为现有知识的广泛重用。Composites Part A: Applied Science and ManufacturingA self-supervised learning framework based on physics-informed and convolutional neural networks to identify local anisotropic permeability tensor from textiles 2D images for filling pattern predictionJohn M. Hanna, José V. Aguado, Sebastien Comas-Cardona, Yves Le Guennec, Domenico Borzacchiellodoi:10.1016/j.compositesa.2024.108019基于物理信息和卷积神经网络的自监督学习框架，从纺织品二维图像中识别局部各向异性渗透率张量，用于填充图案预测In liquid composite molding processes, variabilities in material and process conditions can lead to distorted flow patterns during filling. These distortions appear not only within the same part but also from one part to another. Notably, minor deviations in the dry fibrous textiles cause local permeability changes, resulting in flow distortions and potential defects. Traditional permeability models fall short in predicting these localized fluctuations, especially for anisotropic textiles, whereas reliance on homogeneous permeability models creates substantial discrepancies between forecasted and observed filling patterns. This study presents a self-supervised framework that determines in-plane permeability tensor field of textiles from an image of that textile in dry state. Data from central injection experiments is used for training, including flow images and pressure inlet data. This work demonstrates that this model proficiently predicts flow patterns in unobserved experiments and captures local flow distortions, even when trained on a relatively small dataset of experiments.在液体复合成型过程中，材料和工艺条件的变化会导致填充过程中流动模式的扭曲。这些扭曲不仅出现在同一部分，而且从一个部分到另一个部分。值得注意的是，干燥纤维纺织品中的微小偏差会导致局部渗透性变化，从而导致流动扭曲和潜在缺陷。传统渗透率模型在预测这些局部波动方面存在不足，特别是对于各向异性纺织品，而依赖均质渗透率模型会在预测和观察到的填充模式之间产生巨大差异。本研究提出了一种自监督框架，从纺织品干燥状态的图像中确定纺织品的面内渗透率张量场。中心注射实验数据用于训练，包括流动图像和入口压力数据。这项工作表明，即使在相对较小的实验数据集上训练，该模型也能熟练地预测未观察到的实验中的流动模式，并捕获局部流动扭曲。Composites Part B: EngineeringConcerns in tension-tension fatigue testing of unidirectional composites: Specimen design and test setupBabak Fazlali, Stepan V. Lomov, Yentl Swolfsdoi:10.1016/j.compositesb.2024.111213单向复合材料拉伸-拉伸疲劳试验中的问题:试样设计和试验设置Tension-tension fatigue of unidirectional (UD) composites is often used to represent the fatigue behavior of composites. The standard proposes to use end tabs for UD composites. However, obtaining a reliable S–N curve requires avoiding premature failure, which in turn requires minimizing stress concentrations near the grips and end tabs. This work examines the fatigue life of different specimen and end tab designs. Conventional end tabs and other well-known designs, along with novel arrow end tabs, are tested to identify the method that is least influenced by the stress concentrations and yields the highest and the most valid fatigue life. Rectangular specimens with rectangular and tapered end tabs, which is the standard configuration, yielded the highest fatigue life. This differs from the preferences in the quasi-static test, where arrow end tabs performed the best. Moreover, the effect of parameters in the manufacturing process and test setup on the fatigue life of two different carbon fiber/epoxy prepregs material systems are discussed. The results reveal the significant effect of the test setup on tension-tension fatigue of UD composites and will inform the community on how to perform more reliable fatigue tests.单向复合材料的拉伸-拉伸疲劳常被用来表示复合材料的疲劳行为。该标准建议在UD复合材料中使用结束标签。然而，获得可靠的S-N曲线需要避免过早失效，这反过来又需要最小化握把和末端卡箍附近的应力集中。本工作考察了不同试样和端片设计的疲劳寿命。测试了传统的端卡和其他知名的设计，以及新型的箭头端卡，以确定受应力集中影响最小的方法，并产生最高和最有效的疲劳寿命。具有矩形和锥形端片的矩形试样是标准配置，产生了最高的疲劳寿命。这与准静态测试中的首选项不同，在准静态测试中，箭头结束选项卡表现最好。此外，还讨论了制造工艺参数和试验设置对两种不同碳纤维/环氧预浸料材料体系疲劳寿命的影响。研究结果揭示了试验设置对UD复合材料拉伸-拉伸疲劳的显著影响，并将为如何进行更可靠的疲劳试验提供指导。Composites Science and TechnologyBayesian optimization-based prediction of the thermal properties from fatigue test IR imaging of composite couponsMartin Demleitner, Rodrigo Q. Albuquerque, Ali Sarhadi, Holger Ruckdäschel, Martin A. Ederdoi:10.1016/j.compscitech.2024.110439 基于贝叶斯优化的复合材料疲劳红外成像热性能预测The prediction of the prevailing self-heat transfer parameters of a glass/epoxy composite coupon during fatigue testing in general and the distinction between viscoelastic- and frictional crack growth-related energy dissipation in particular, are not trivial problems. This work investigates the feasibility of predicting the convective film coefficient, the total work loss as well as the ratio between viscoelastic and fracture-induced damping from thermal images using Bayesian optimization in conjunction with 3D FE thermal analysis. To this end, glass fiber/epoxy biax coupons are pre-damaged by means of a drop weight impact machine and subsequently tested under uniaxial tension-tension high cycle fatigue conditions. IR images are taken of the self-heating thermal profile at steady-state conditions. Synthetic surface thermal images are generated by numerical thermal analysis of the damage distribution obtained byμ-CT scanning prior to testing. Bayesian optimization of the aforementioned parameters is conducted by minimizing the loss function between the as-measured and the synthetic IR image. The predicted work-loss is consequently validated against the measured hysteretic response, from which a very good agreement is found.预测玻璃/环氧复合材料在疲劳试验中普遍存在的自传热参数，特别是区分粘弹性和摩擦裂纹扩展相关的能量耗散，不是一个简单的问题。本研究研究了利用贝叶斯优化结合三维有限元热分析，从热图像中预测对流膜系数、总功损失以及粘弹性和裂缝诱导阻尼之间的比率的可行性。为此，采用落锤冲击试验机对玻璃纤维/环氧双轴材料进行预损伤，并在单轴拉伸-拉伸高周疲劳条件下进行试验。对稳态条件下的自热热廓线进行了红外成像。对得到的损伤分布进行数值热分析，生成合成的表面热图像μ-测试前的ct扫描。上述参数的贝叶斯优化是通过最小化实测图像与合成红外图像之间的损失函数来实现的。因此，预测的工作损失与测量的滞后响应进行了验证，从中发现了非常好的一致性。来源：复合材料力学仿真Composites FEM