Composites Part B: Engineering最新文献

The subjects of the study were thin-walled composite columns with closed cross sections manufactured using the autoclave technique. The composite profiles were characterized by the fact that they had a constant height and arrangement of laminate layers, however, varied cross-sectional shapes. The study was conducted using several interdisciplinary experimental research methods and advanced numerical simulations. In the course of the research, both forms of structural stability loss were registered, and damage to composite structures was assessed. In the course of the research, the influence of the shape of the cross-section on the stability and load-carrying capacity of the structure was evaluated. A measurable effect of the conducted research was the determination of the structure's post-buckling equilibrium paths, which made it possible to determine the structure's behavior in the full range of loading. In addition, the author's numerical models developed enabled validation of parallel experimental studies. The developed numerical models were based on a failure criterion known as progressive failure analysis - which allowed a thorough assessment of the failure mechanism of the composite material.

Composite 3D printing is a significant engineering application owing to its robustness, ability to achieve complex geometries, and ease of use. Polycarbonate, particularly when infused with AEROSIL, is an interesting thermoplastic and potential candidate for 3D printing with enhanced properties. The primary objective of this research is to develop a new model-based deep-learning framework to classify and detect damage in this material under dynamic loading conditions. To achieve this, a FASTCAM high-speed camera was placed in front of the SHPB test setup to capture dynamic damage. The test results were then used as label inputs for training the advanced deep learning algorithms, focusing on dense image recognition techniques for detailed damage analysis. The study involved a series of fully convolutional networks (FCNs), evaluating semantic segmentation with U-Net and instance segmentation with state-of-the-art frameworks such as YOLOv8 and Mask R–CNN. A comparative analysis revealed that deep learning models outperform traditional methods, providing efficient and accurate damage classification and detection. The U-Net model demonstrated the ability to recognize cubes and bars but was limited in detecting minor damage regardless of size. YOLO-V8, which specializes in case segmentation, achieved remarkable performance in detecting significant damage but struggled to accurately identify minor damage. By leveraging deep learning techniques, this study enables an efficient and accurate damage assessment, which is crucial for ensuring the reliability and safety of composite structures in various industries.

The healing of infected bone fractures represents a significant clinical challenge in orthopedic surgery. Calcium phosphate cements (CPCs) are attractive materials, highly applicable in bone healing due to their satisfactory biological properties and chemical similarity to bone minerals. However, manual mixing is often required before localized application of conventional CPCs, increasing the risk of infection. Moreover, their antibacterial properties are often insufficient for treating infected fractures. In this study, antimicrobial two-paste premixed CPCs loaded with carvacrol were developed. The influence of the carvacrol on the setting properties and mechanical strength of the cement was studied. In vitro studies demonstrated the biocompatibility, osteogenic potential, and broad-spectrum antimicrobial efficacy of the premixed bone cements, including effectiveness against gram-positive, gram-negative, and drug-resistant bacteria. Notably, in vivo studies revealed exceptional antimicrobial and osteogenic properties of the carvacrol-loaded cements, confirming the promising potential of the premixed cements for the healing of infected bone defects.

This study presents the development of a groundbreaking electrochemical sensor for detecting fenitrothion (FNT) using a multi-walled carbon nanotubes (MWCNTs) and a newly synthesized zinc (II) tetra trifluoromethyl carboxamide phthalocyanine (ZnTFMPCAPc). The ZnTFMPCAPc was synthesized concluding a two-step mechanical and magnetic stirring method, and the ensuing ZnTFMPCAPc@MWCNTs underwent comprehensive characterization employing X-ray diffraction (XRD), Ultraviolet visible spectroscopy (UV–Vis), mass spectrum, Fourier transform infrared spectroscopy (FT-IR), Thermo-gravimetric analysis (TGA), X-ray photoelectron spectroscopy (XPS), Mass, Raman spectra, Transmission Electron Microscopy (TEM) and scanning electron microscope (SEM). Cyclic voltammetry (CV) analysis demonstrated a remarkable seven-fold improvement in electrochemical signals with ZnTFMPCAPc@MWCNTs on modified glassy carbon electrode (GCE) compared to a bare and modified GCE. The correlation between peak current and FNT concentration (in the range of 10–310 μmol) was established. The estimated limits of detection (LOD) and quantification (LOQ) were determined to be 1.358 nmol and 4.075 nmol respectively. The ZnTFMPCAPc@MWCNTs/GCE sensor was successfully evaluated by quantifying FNT in tomatoes, grapes, paddy grains, and potato extracts, resulting in satisfactory results. Detecting fenitrothion is crucial due to its widespread use as a pesticide, which can result in environmental contamination and pose health risks. Regular monitoring is essential for protecting food and water supplies, preserving ecosystems, and ensuring compliance with regulations to prevent long-term environmental damage.

Advancements in additive manufacturing coincide with the influx of assiduous research in realizing complex structures using printable composite materials with tunable properties. In this research study, photocurable resins with a broad range of mechanical properties were hybridized with ceramic particles to engineer the overall mechanical response. The newly formulated printable resins comprised up to 20 wt.% glass microballoons, balancing the tunability of the composite properties and manufacturability by overcoming light-reinforcement challenges. The compressive and tensile bulk properties were first assessed using additively manufactured samples tested under quasi-static loading. Complementary digital image correlation (DIC) was used to resolve the strain fields, revealing insights about the mechanical behavior and failure modes as a function of reinforcement weight ratio. Despite the expected hyperelastic constitutive behavior and shared macromolecular composition, the neat and hybridized photocurable resins exhibited distinctive mechanical behavior, leading to the characterization of the dynamic properties as a function of temperature to ascertain the underpinnings of respective responses. Triply periodic minimal surface (TPMS) structures were also manufactured using the vat photopolymerization approach to demonstrate the utility of newly formulated printable composite resins. The printed structures were tested under compression at a quasi-static loading rate. The DIC-resolved strains revealed the underlying structural mechanics as a function of the material properties. This case study correlates the mechanics governing particulate-reinforced elastomers with the observed variations in strain development, stiffness, load-bearing capacity, and specific energy absorption for TPMS structures. Finite element analysis (FEA) based on hyperelastic potential and using the properties of the bulk resin closely matched the deformation patterns from the experimental DIC results. The outcomes of this research reveal the potential for tunable, 3D printed sports gear for impact mitigation in various biomechanical loading conditions.

Cellular materials are known for their lightweight nature and remarkable energy absorption characteristics attributed to their cellular structure. This study focuses on the design aspect of cellular materials to achieve specific constitutive responses through density gradation. A three-parameter empirical constitutive model is employed to characterize the behavior of density-graded cellular materials, utilizing experimentally derived parameters for rigid polyurethane foam. The investigation reveals a highly nonlinear spatial variation of local strains that influence the mechanical behavior of density-graded materials. The study investigates the isolated effect of density gradients within these materials on their mechanical behavior and energy absorption. Comparative analyses demonstrate that density-graded materials outperform uniform-density counterparts, particularly at lower stress levels, with greater energy absorption enhancement observed in materials featuring steeper density gradients. Finally, the optimal variables controlling density variation are identified to achieve desired stress–strain responses. These findings contribute to the enhanced understanding and practical utilization of density-graded cellular materials in applications requiring tailored mechanical performance and energy absorption capabilities.

In order to prolong the service life of fiber-reinforced polymer composites, the implementation of self-healing ability with the micro-encapsulated healing agent has been extensively studied. However, such microcapsule-based self-healing composites typically suffer from degraded mechanical properties due to the liquid-phase inclusions, thereby limiting their proliferation. Here, a low-melting-point alloy is utilized as the particulate inclusions of carbon fiber/epoxy laminated composites. Field's Metal particles (melting point: 62 °C) are distributed between woven carbon fiber preforms followed by the resin impregnation to realize laminated composites with a Field's Metal-enhanced interlayer(s). The resulting laminated composites demonstrate the autonomic repair of interlaminar failure with a 40 % of healing efficiency. Most of all, the mechanical properties of these self-healing laminated composites are comparable to the conventional laminated composites attributed to the rigid inclusions that can be compressed to increase the fiber volume. Since the Field's Metal particle inclusions can bestow polymer composites with self-healing ability and the potential increase in mechanical properties, Field's Metal-enhanced fiber-reinforced polymer composites are expected to unlock the practical utility of self-healing composites.

This study explores the synthesis and characterization of polyamide/biochar composites via in situ polymerization of 12-aminolauric acid with varying biochar concentrations. The motivation behind this research is to enhance the properties of polyamide 12 (PA12) by integrating biochar, a sustainable material derived from biomass, to improve both performance and environmental impact. A detailed structure-property correlation analysis was conducted to assess the effects of biochar on PA12's morphology, mechanical behavior, crystallinity, thermal stability, viscoelastic performance, and environmental sustainability. Key findings include successful PA12 synthesis, confirmed by FTIR and ¹H NMR spectroscopy. Increased biochar content led to a decrease in molecular weight and an increase in crystallinity from 27 % to 38 %, suggesting enhanced nucleation effects. SEM analysis showed excellent dispersion and compatibility of biochar within the PA12 matrix, leading to significant improvements in tensile strength (from 38 ± 1 MPa to 54 ± 2 MPa) and modulus (from 745 ± 30 MPa to 2055 ± 65 MPa). Rheological tests demonstrated shear-thinning behavior, facilitating effective extrusion-based 3D printing of a complex object with 50 wt% biochar. A life cycle assessment revealed substantial environmental benefits, including a net reduction of 1.83 kg·CO₂ equiv.·kg⁻¹ due to the use of biochar derived from wood pyrolysis. These findings highlight the potential of PA12/biochar composites as environmentally sustainable structural materials, combining enhanced functional properties with significant ecological advantages.

Glass fiber reinforced polymer-based composites prepared by photocuring technology offer notable advantages. However, the traditional UV curing technology faces challenges in balancing curing efficiency, penetration depth, and mechanical properties when producing high-thickness fiber-reinforced composites. This study introduced a new method for crafting thick glass fiber reinforced composites via upconversion particle-assisted near-infrared photopolymerization (UCAP). Near-infrared (NIR) radiation had superior penetration in glass fiber composites system, effectively curing of specimens exceeding 20 mm in thickness. Through micro-CT and atomic force microscopy, it was verified that UCAP specimens had fewer interfacial defects and a wider interphase, making contribution to enhanced interlaminar and interfacial shear strength. Additionally, uniform curing effectively alleviated stress concentration under external forces, resulting in a 78.5 % increase in flexural strength and a 32.1 % increase in impact toughness for UCAP specimens compared to UV-cured ones. This approach facilitated rapid outdoor curing of large-sized glass fiber composites with sustained structural stability, showcasing the potential application of UCAP in high-performance glass fiber composites rapid prototyping.