Wenfeng Hao^1,*, Hao Hao², Humaira Kanwal², Shiping Jiang^2,*

Journal of Renewable Materials, Vol.11, No.4, pp. 1687-1697, 2023, DOI:10.32604/jrm.2022.023795

Abstract Microcapsule self-healing technology is one of the effective methods to solve the durability problem of cement-based composites. The evaluation method of the self-healing efficiency of microcapsule self-healing cement-based composites is one of the difficulties that limits the self-healing technology. This paper attempts to characterize the self-healing efficiency of microcapsule self-healing cement-based composites by acoustic emission (AE) parameters, which provides a reference for the evaluation of microcapsule self-healing technology. Firstly, a kind of self-healing microcapsules were prepared, and the microcapsules were added into the cement-based composites to prepare the compression samples. Then, the specimen with certain pre damage was obtained by… More >

Junlong Yao^1,2, Wei Nie¹, Zhengguang Sun², Huan Yang^1,3,*, Yu Guan¹, Lin Gao⁴, Xueliang Jiang¹, Mujie Guo¹, Chuanxi Xiong^5,*

Journal of Renewable Materials, Vol.10, No.9, pp. 2355-2364, 2022, DOI:10.32604/jrm.2022.019339

Abstract In general, self-healing dielectric composites are mainly composed of polar hydrogen bonds, which have high hydrophilicity and are unsuitable for humid environment. Dielectric composite with Diels-Alder (D-A) bond contains covalent bonds, it can be adopted as an efficient self-healing material. Here, we construct self-healing barium titanate (BT)/polyurethane (PU) dielectric composites by adopting PU with D-A bond as matrix (BT/ PU-DA). The prepared 10% BT/PU-DA composite exhibits superior self-healing ability than that of PU-DA. Moreover, its dielectric constant can reach 9.3 with a loss of only 0.04 at 1000 Hz and maintain 93% repair effi- ciency of tensile strength. The experimental… More >

Yuhao Jiang, Jialan Yao, Chengfei Zhu^*

Journal of Renewable Materials, Vol.10, No.1, pp. 135-148, 2022, DOI:10.32604/jrm.2021.016304

Abstract Poly(urea-formaldehyde) (PUF) microcapsules were prepared by in-situ polymerization with four different processes in this paper. The chemical composition, surface morphology, particle size distribution, and thermal stability were characterized by FTIR, SEM, particle size analyzer, and TGA, respectively. The results demonstrated that the agglomeration of the PUF microcapsules was related to the agglomeration of the emulsion particles caused by the changes of emulsion interface during the shell polymerization. Due to the slow deposition of the shell material, the PUF microcapsules with the core-shell structure prepared by the process with ammonium chloride as the last additive showed good dispersibility with an average… More > Graphic Abstract

Benan Shu^1,*, Lixian Guo¹, Bing Qiu¹, Tengyu Yang¹, Tao Sun¹, Wenjun Qiu¹, Min Zhou¹, Putao Song², Yuanyuan Li³, Diego Maria Barbieri⁴, Shaopeng Wu^5,*

Journal of Renewable Materials, Vol.9, No.10, pp. 1781-1794, 2021, DOI:10.32604/jrm.2021.015659

Abstract As an innovative maintenance technology of asphalt pavement, encapsulated rejuvenator used to improve its self-healing performance has been widely investigated by researchers in recent years. In this work, the selfhealing properties of asphalt mixture with and without encapsulations were comparatively studied considering these parameters: Healing time, healing cycles and microwave heating. Three-point bending strength recovery test and fatigue loading cycles recovery test were conducted for two kinds of encapsulations containing the healing agents present inside the asphalt mixture, namely compartmented Ca-alginate/SiO2 fiber and compartmented Ca-alginate/graphene oxide fiber. The results showed that the optimum healing time was three days. After the… More >

Fuquan Huang¹, Zijun Liu¹, Tinghuang Wang¹, Haitai Zhang^{2, *}, Tony Yip³

CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 865-878, 2020, DOI:10.32604/cmc.2020.010724

Abstract In order to quickly and accurately locate the fault location of the distribution network and increase the stability of the distribution network, a fault recovery method based on multi-objective optimization algorithm is proposed. The optimization of the power distribution network fault system based on multiagent technology realizes fast recovery of multi-objective fault, solve the problem of network learning and parameter adjustment in the later stage of particle swarm optimization algorithm falling into the local extreme value dilemma, and realize the multi-dimensional nonlinear optimization of the main grid and the auxiliary grid. The system proposed in this study takes power distribution… More >

Haiyang Ding^1,2,3,4,5, Xiaohua Yang^1,2,3,4,5, Lina Xu^1,2,3,4,5, Shouhai Li^1,2,3,4,5, Jianling Xia^1,2,3,4,5, Mei Li^1,2,3,4,5,*

Journal of Renewable Materials, Vol.8, No.1, pp. 1-11, 2020, DOI:10.32604/jrm.2020.08165

Abstract Thermosetting polyurethanes are widely used in various fields owing to their excellent elasticity, strength and solvent resistance. Three environmental friendly propyl gallate-based self-healing polyurethanes were prepared from polyurethane prepolymers with varying isocyanate content. The thermal stabilities of the polyurethanes were tested using thermogravimetric analysis. Their self-healing and mechanical properties were analyzed using a universal testing machine and dynamic thermomechanical analysis. The polyurethanes were found with high self-healing ability and excellent mechanical properties due to the absence of phenolic carbamate. These qualities improved with increased isocyanate content and the prolonged selfhealing time. We found, therefore, that the propyl gallate-based polyurethane has… More >

Xiaoying Zhuang^1,2,*, Shuai Zhou^3,4

CMC-Computers, Materials & Continua, Vol.59, No.1, pp. 57-77, 2019, DOI:10.32604/cmc.2019.04589

Abstract Advances in machine learning (ML) methods are important in industrial engineering and attract great attention in recent years. However, a comprehensive comparative study of the most advanced ML algorithms is lacking. Six integrated ML approaches for the crack repairing capacity of the bacteria-based self-healing concrete are proposed and compared. Six ML algorithms, including the Support Vector Regression (SVR), Decision Tree Regression (DTR), Gradient Boosting Regression (GBR), Artificial Neural Network (ANN), Bayesian Ridge Regression (BRR) and Kernel Ridge Regression (KRR), are adopted for the relationship modeling to predict crack closure percentage (CCP). Particle Swarm Optimization (PSO) is used for the hyper-parameters… More >