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  • Open Access

    ARTICLE

    Sustainable Particleboards Based on Sugarcane Bagasse and Bonded with a Waste-Grown Black Soldier Fly Larvae Commercial Flour-Based Adhesive: Rheological, Physical, and Mechanical Properties

    Francisco Daniel García1,2, Solange Nicole Aigner1,2, Natalia Raffaeli3, Antonio José Barotto3, Eleana Spavento3, Mariano Martín Escobar1,4, Marcela Angela Mansilla1,4, Alejandro Bacigalupe1,4,*

    Journal of Renewable Materials, Vol.14, No.1, 2026, DOI:10.32604/jrm.2025.02025-0181 - 23 January 2026

    Abstract This study explores the use of black soldier fly larvae protein as a bio-based adhesive to produce particleboards from sugarcane bagasse. A comprehensive evaluation was conducted, including rheological characterization of the adhesive and physical–mechanical testing of the panels according to European standards. The black soldier fly larvae-based adhesive exhibited gel-like viscoelastic behavior, rapid partial structural recovery after shear, and favorable application properties. Particleboards manufactured with this adhesive and sugarcane bagasse achieved promising mechanical performance, with modulus of rupture and modulus of elasticity values of 30.2 and 3500 MPa, respectively. Internal bond strength exceeded 0.4 MPa,… More > Graphic Abstract

    Sustainable Particleboards Based on Sugarcane Bagasse and Bonded with a Waste-Grown Black Soldier Fly Larvae Commercial Flour-Based Adhesive: Rheological, Physical, and Mechanical Properties

  • Open Access

    REVIEW

    Thermal Insulation Performance of Natural Fibre-Reinforced Composites—A Comprehensive Review

    Raviduth Ramful*

    Journal of Renewable Materials, Vol.14, No.1, 2026, DOI:10.32604/jrm.2025.02025-0116 - 23 January 2026

    Abstract Typically used thermal insulation materials such as foam insulation and fibreglass may pose notable health risks and environmental impacts thereby resulting in respiratory irritation and waste disposal issues, respectively. While these materials are affordable and display good thermal insulation, their unsustainable traits pertaining to an intensive manufacturing process and poor disposability are major concerns. Alternative insulation materials with enhanced sustainable characteristics are therefore being explored, and one type of material which has gained notable attention owing to its low carbon footprint and low thermal conductivity is natural fibre. Among the few review studies conducted on… More > Graphic Abstract

    Thermal Insulation Performance of Natural Fibre-Reinforced Composites—A Comprehensive Review

  • Open Access

    ARTICLE

    Data-Driven Prediction and Optimization of Mechanical Properties and Vibration Damping in Cast Iron–Granite-Epoxy Hybrid Composites

    Girish Hariharan1, Vinyas1, Gowrishankar Mandya Chennegowda1, Nitesh Kumar1, Shiva Kumar1, Deepak Doreswamy2, Subraya Krishna Bhat1,*

    CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.073772 - 12 January 2026

    Abstract This study presents a framework involving statistical modeling and machine learning to accurately predict and optimize the mechanical and damping properties of hybrid granite–epoxy (G–E) composites reinforced with cast iron (CI) filler particles. Hybrid G–E composite with added cast iron (CI) filler particles enhances stiffness, strength, and vibration damping, offering enhanced performance for vibration-sensitive engineering applications. Unlike conventional approaches, this work simultaneously employs Artificial Neural Networks (ANN) for high-accuracy property prediction and Response Surface Methodology (RSM) for in-depth analysis of factor interactions and optimization. A total of 24 experimental test data sets of varying input… More >

  • Open Access

    ARTICLE

    Machine Learning Based Simulation, Synthesis, and Characterization of Zinc Oxide/Graphene Oxide Nanocomposite for Energy Storage Applications

    Tahir Mahmood1,*, Muhammad Waseem Ashraf1,*, Shahzadi Tayyaba2, Muhammad Munir3, Babiker M. A. Abdel-Banat3, Hassan Ali Dinar3

    CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072436 - 12 January 2026

    Abstract Artificial intelligence (AI) based models have been used to predict the structural, optical, mechanical, and electrochemical properties of zinc oxide/graphene oxide nanocomposites. Machine learning (ML) models such as Artificial Neural Networks (ANN), Support Vector Regression (SVR), Multilayer Perceptron (MLP), and hybrid, along with fuzzy logic tools, were applied to predict the different properties like wavelength at maximum intensity (444 nm), crystallite size (17.50 nm), and optical bandgap (2.85 eV). While some other properties, such as energy density, power density, and charge transfer resistance, were also predicted with the help of datasets of 1000 (80:20). In… More >

  • Open Access

    ARTICLE

    Numerical Simulation of Damage Behavior in Graphene-Reinforced Aluminum Matrix Composite Armatures under Multi-Physical Field Coupling

    Junwen Huo, Haicheng Liang, Weiye Dong, Xiaoming Du*

    CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-20, 2026, DOI:10.32604/cmc.2025.073285 - 09 December 2025

    Abstract With the rapid advancement of electromagnetic launch technology, enhancing the structural stability and thermal resistance of armatures has become essential for improving the overall efficiency and reliability of railgun systems. Traditional aluminum alloy armatures often suffer from severe ablation, deformation, and uneven current distribution under high pulsed currents, which limit their performance and service life. To address these challenges, this study employs the Johnson–Cook constitutive model and the finite element method to develop armature models of aluminum matrix composites with varying heterogeneous graphene volume fractions. The temperature, stress, and strain of the armatures during operation… More >

  • Open Access

    PROCEEDINGS

    Crashworthiness Design of Composite Thin-Walled Structures Manufactured by Additive Manufacturing

    Kui Wang*, Qianbing Tan, Yisen Liu

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.34, No.1, pp. 1-1, 2025, DOI:10.32604/icces.2025.012404

    Abstract To address the increasing demands for lightweight and passive safety in transportation equipment, a series of studies on the crashworthiness design of composite thin-walled structures were conducted. These investigations leveraged the high specific strength/stiffness advantages of carbon fiber-reinforced polyamide composites and the high-formability benefits of fused deposition modeling (FDM) additive manufacturing technology. Compared with traditional composite manufacturing processes, lattice-filled thin-walled structures, integrally fabricated via additive manufacturing, exhibited significant synergistic interactions between their internal lattice and outer walls during compression. This synergy effectively enhanced the energy absorption capacity of the structures and achieved a "1+1>2" synergistic… More >

  • Open Access

    PROCEEDINGS

    Spatio-Temporal Prediction of Curing-Induced Deformation for Composite Structures Using a Hybrid CNN-LSTM and Finite Element Approach

    Xiangru He1, Ying Deng1, Zefu Li1, Jie Zhi1,2, Yonglin Chen1,2, Weidong Yang1,2,3,*, Yan Li1,2,3,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.34, No.1, pp. 1-1, 2025, DOI:10.32604/icces.2025.012395

    Abstract Coordinated control of structural accuracy and mechanical properties is the key to composites manufacturing and the prerequisite for aerospace applications. In particular, accurate and efficient prediction of curing-induced deformation (CID) is of vital importance for fiber reinforced polymer composites quality control. In this study, we explored a novel spatio-temporal prediction model, which incorporates the finite element method with a deep learning framework to efficiently forecast the curing-induced deformation evolution of composite structures. Herein, we developed an integrated convolutional neural network (CNN) and long short-term memory (LSTM) network approach to capture both the space-distributed and time-resolved… More >

  • Open Access

    PROCEEDINGS

    A New Analytical Method for Strength Prediction of Injection Molded Fiber Reinforced Thermoplastics Based on Progressive Delamination Failure Principle

    Dayong Huang1,2,*, Wenjun Wang1,2, Xiaofu Tang1,2, Pengfei Zhu3, Xianqiong Zhao3,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.34, No.1, pp. 1-1, 2025, DOI:10.32604/icces.2025.012063

    Abstract Accurate prediction for the tensile properties (tensile modulus and strength) of injection molded fiber-reinforced thermoplastics (IMFT) plays an important role in the design of structures made with such composites. Based on the Laminate analogy approach (LAA), a unified distribution function (UDF) of tensile properties is derived by introducing the assumption that the fiber length distribution (FLD) and fiber orientation distribution (FOD) are independent of each other. The UDF of tensile properties is simplified by introducing the modified monotonic functions of fiber length and orientation factors (λL and λO). Compared with the tensile modulus and strength… More >

  • Open Access

    PROCEEDINGS

    High-Temperature Fracture Behavior and Toughening Mechanisms of PIP-C/SiC Composites: An Integrated Experimental and Phase-Field Study

    Kunjie Wang, Chenghai Xu*, Xinliang Zhao, Songhe Meng

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.34, No.1, pp. 1-1, 2025, DOI:10.32604/icces.2025.011534

    Abstract Considering the high-temperature application environment and quasi-brittle characteristics, the high-temperature fracture toughness of C/SiC composites is of great significance for the safety application of components in service.
    In this work, the fracture toughness of PIP-C/SiC composites at 25–1600 ℃ in inert atmosphere was tested. The test results show that the fracture toughness and modes of C/SiC composites have significant temperature dependence and difference in in-plane and out-of-plane orientations. With the rising of temperature, the carrying capacity and KIC of C/SiC composites increase first and then decrease, and an inflection point occurs near the fabrication temperature.… More >

  • Open Access

    REVIEW

    Research Progress in the Preparation of MOF/Cellulose Composites and Their Applications in Fluorescent Detection, Adsorption, and Degradation of Pollutants in Wastewater

    Zhimin Zhao, Liyun Feng, Dongsheng Song, Ming Zhang*

    Journal of Polymer Materials, Vol.42, No.4, pp. 929-957, 2025, DOI:10.32604/jpm.2025.074529 - 26 December 2025

    Abstract Global water pollution is becoming increasingly serious, and compound pollutants such as heavy metals and organic dyes pose multidimensional threats to ecology and human health. Metal-organic skeleton compounds (MOFs) have been proven to be highly efficient in capturing a variety of pollutants by virtue of their large specific surface area, adjustable pore channels, and abundant active sites. However, the easy agglomeration of powders, the difficulty of recycling, and the poor long-term stability have limited their practical applications. Cellulose, as the most abundant renewable polymer in nature, has the characteristics of a three-dimensional network, mechanical flexibility,… More >

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