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Carbon nitride/bismuth sulfide (C3N4/Bi2S3) nanocomposites were prepared by a facile one‐step in situ calcination method using ammonium thiocyanate and bismuth chloride as reagents. The crystal structure, composition, and morphology of the nanocomposites were characterized by X‐ray diffraction (XRD), Fourier transform infrared (FT‐IR) spectroscopy, scanning electron microscopy (SEM), and transmission electron microscopy (TEM). The photocatalytic degradation of rhodamine B (RhB) was carried out to evaluate the photocatalytic activity of the nanocomposites under visible light irradiation. The as‐prepared C3N4/Bi2S3 nanocomposites displayed enhanced visible light absorption. These photocatalysts exhibited superior photodegradation performance under visible light irradiation compared to pure C3N4. Meanwhile, the C3N4/Bi2S3 nanocomposites exhibited remarkably stable photocatalytic efficiency after five cycles of photodegradation of RhB. The C3N4/Bi2S3 heterostructure improved the separation and transfer efficiency of the photogenerated electron–hole pairs. It played an important role for the enhanced photocatalytic performance of the nanocomposite photocatalysts. [ABSTRACT FROM AUTHOR]

The deformation behavior of single-layer braided fabric and its influence on the fracture modes of composite tubes under quasi-static axial compression were studied. By changing the parameters of the manufacturing process, braided fabrics with varying structures were produced and were further cured into braided composite tubes. Quasi-static compression experiments were conducted on the tubes, and the crush process was recorded on camera to analyze the deformation of the braided fabric. By employing the finite element method, a numerical model of a composite tube at the yarn scale was established to further verify the role of braided fabric in the process. Two fracture modes that depended on the fabric structure were also observed. The tension in the braiding yarns in the bucking section tends to cause them to separate. Braiding structures with larger braiding angles and more interlacing points between yarns provided more resistance to prevent yarns separating from the braided structure and led to a folding fracture mode. In contrast, a splaying fracture mode occurred in braided fabrics with smaller angles. [ABSTRACT FROM AUTHOR]

I-section carbon fiber reinforced polymer (I-CFRP) laminates are widely used as hatch and cargo doors of wide-body airliners. However, estimating the loading capacity of I-CFRP laminates has always been empirically dependent since there is no uniform failure criterion for composite materials. This study attempts to reveal the failure characteristic points of I-CFRP laminates from a thermodynamic perspective. First, the test strain data can be modeled as state variables, thus equating the loaded engineering structure to a thermodynamic system. Based on the relation of state variables, matrices (Modes) and Hamiltonians (Characteristic parameters) that characterize the overall stressing state evolution of the specimen can be established. Integrating the Hamiltonians of each part into the whole is similar to the group representation and renormalization of Wilson's phase transition theory. Applying the clustering analysis (CA) criterion in combination with the bifurcation and transition of the mode and characteristic parameter curves reveals the phase transformation loads of the specimen. The accuracy and stability can be verified for the phase transition loads before and after the renormalization transformation. In conclusion, this study reveals the deformation-failure law of I-CFRP laminates from the thermodynamic perspective, which provides a new reference and method for the design of composite laminates. [Display omitted] • Eleven I-CFRP laminates axial compression tests were conducted. • Modeling the behavior of I-CFRP laminates from a thermodynamic perspective. • Propose a matrix representation (Mode) and Hamiltonian (Characteristic parameter) of the I-CFRP laminates' stressing state. • Reveal the phase transition point of I-CFRP laminates: EPB and FS points. [ABSTRACT FROM AUTHOR]

In this contribution, we introduced a simple, efficient, and green method of preparing a mesoporous Fe 3 O 4 /hydroxyapatite (HA) composite. The as-prepared material had a large surface area, high pore volume, and good magnetic separability, which made it suitable for targeted drug delivery systems. The chemotherapeutic agent doxorubicin (DOX) was used to investigate the drug release behavior of Fe 3 O 4 /HA composite. The drug release profiles displayed a little burst effect and pH-dependent behavior. The release rate of DOX at pH 5.8 was larger than that at pH 7.4, which could be attributed to DOX protonation in acid medium. In addition, the released DOX concentrations remained at 0.83 and 1.39 μg/ml at pH 7.4 and 5.8, respectively, which indicated slow, steady, and safe release rates. Therefore, the as-prepared Fe 3 O 4 /hydroxyapatite composite could be an efficient platform for targeted anticancer drug delivery. [ABSTRACT FROM AUTHOR]

Accurately identifying various meso-morphological features and sub-phases within the damaged braided composite fabric is crucial for assessing the mechanical properties of composites under complex loading conditions and different processing parameters. However, obtaining 3D reconstructions that can be quantitatively analyzed is still a challenge, primarily due to the low contrast of the meso-morphological features (yarn tow and matrix crack networks). In this work, a new data enhancement algorithm is proposed to generate a realistic-looking artificial training dataset for enriching the information of the meso-morphological features. Then, the effect of various training networks and training parameters (the size of real and hybrid training dataset, number of epochs) on segmentation performance were examined. Finally, the meso-morphological features were statistically analyzed to accurately evaluate the mechanical properties of the composite material. The work presented here provides an effective tool that enables the quantitative analysis of the dynamic crack evolution process under the axial load of the composite materials. [Display omitted] [ABSTRACT FROM AUTHOR]

In order to reduce the potential toxicity to the environment after treatment of radioactive waste water, an eco-friendly graphene oxide nanoribbons/chitosan (GONRs-CTS) composite was prepared and utilized for adsorption of Th(IV) from aqueous solution. The GONRs-CTS adsorbent was characterized by XRD, FTIR and SEM, indicating that GONRs-CTS contains a large amount of oxygen-containing groups and chitosan was successfully attached to the GONRs surface. The Th(IV) adsorption on GONRs-CTS was endothermic and spontaneous, with the maximum single layer adsorption capacity was 140.6 mg g−1 at pH = 3.0, in the contact time of 12 h. The adsorption process was consistent with pseudo-second-order kinetics and the Langmuir isotherm model. [ABSTRACT FROM AUTHOR]

Accurate 3D representations of multiphase layered composite fabric, including distinguish and label the pores, matrices, warp and weft yarns, can assist in understanding and ultimately improving composite material design performance. In this work, a new data enhancement algorithm is proposed to generate realistic-looking artificial learning datasets for expanding the datasets. Then, we demonstrate how supervised, the Swin Transformer approach can help to realize accurate segmentation of low-resolution and poor-contrast fabric datasets by populating databases with real and artificial learning datasets. Most of the pixels were correctly identified and the boundaries of the yarn could be identified clearly, except for some spots and a few merged boundaries. The work presented here provides an effective tool that can be widely used in semantic segmentation for various composite materials (woven composites, fiber-reinforced composites, short-fiber materials, etc.), which enables higher-precision reconstruction of fiber-composites and significant reduction in dataset processing time, and provides a route to generating artificial learning dataset. [ABSTRACT FROM AUTHOR]