Search Results (36)

Thin spray on-liner (TSL) is a new type of rock support technology, but ordinary cement-based TSL has low tensile strength and poor toughness, which makes it difficult to meet the challenges of large deformation of coal mine roadway perimeter rock surface maintenance. A high-performance composite cement-based TSL was obtained by adding acrylic emulsion, basalt fiber and rubber powder to modify ordinary Portland cement. The orthogonal test and range analysis method were used to systematically study the change law of the physical and mechanical properties of the composite cement-based TSL, determine its reasonable ratio, and further microscopic analysis to find out the modification mechanism. The results show that the reasonable ratio of composite cement-based TSL is as follows: polymer–cement ratio is 1.75, basalt fiber content is 1%, and rubber powder content is 3%; that is, the viscosity is 20,000 mps, and the elongation, tensile strength and adhesive strength in 28 d are 121%, 2.28 Mpa, and 1.66 Mpa, respectively. When the acrylic emulsion-basalt fiber-rubber powder is compositely modified, the acrylic emulsion cures and the cement hydration product to form a three-dimensional space network structure, which increases the compactness, the basalt fiber reduces the porosity of the matrix, inhibits the development of matrix cracks, and the rubber powder improves the elongation of the matrix and jointly improves the mechanical properties of TSL. This study provides a theoretical basis for the preparation of composite cement-based TSL. Full article

Silk fibroin (SF)-based materials attract significant interest because of their biocompability and great diversity of possible morphologies. One of the approaches to obtain SF materials is the use of an air–water or oil–water interface as a template for protein self-assembly. Surfactants can change the surface properties of adsorbed SF layers by promoting or preventing the formation of SF fiber networks. This study focuses on the influence of two typical ionic surfactants, cationic cetyltrimethylammonium bromide (CTAB) and anionic sodium dodecyl sulfate (SDS), on the dynamic properties of SF layers adsorbed at the air–water interface. The dynamic surface elasticity, surface tension, ellipsometric angle Δ, and the film thickness were measured as a function of the surface age and surfactant concentration. The morphology of the layers was evaluated by atomic force microscopy (AFM). For the adsorption layers of globular proteins, the main effect of the surfactants consists in the protein unfolding at high concentrations and in a decrease in the electrostatic adsorption barrier. In the case of SF layers, CTAB and SDS strongly influence the protein aggregation at the air–water interface. Regardless of the sign of the surfactant charge, its addition to SF solutions results in a decrease in the surface elasticity and the destruction of the ordered structure of protein fibers at concentrations higher than 1 × 10⁻⁴ M. With the further increase in the surfactant concentration, the thread-like aggregates disappear, the packing of thin fibers becomes less tight, a uniform layer disintegrates into separate islands, and finally, the protein is displaced from the interface. Full article

The main objective of this work is to investigate the influence of shear on the rheological properties and physical stability of aqueous dispersions of flaxseed fiber. The variable to consider will be the homogenization rate in two different rotor-stator homogenizers, Ultraturrax T50 or T25. In order to achieve the proposed objective, small amplitude oscillatory tests, flow curves, and multiple light scattering measurements were carried out. All samples exhibited a shear thinning behavior that was not influenced by the shear imposed, and a weak gel-like behavior. The latter, unlike the flow behavior, was sensitive to the homogenization rate. Thus, an increase in this variable caused a decrease in the viscoelastic moduli values. This result pointed out a weakening of the network formed by the flaxseed fiber in an aqueous medium. On the contrary, the physical stability improved. Nevertheless, all samples were highly stable. The homogenizer used was a significant variable. The shear negatively influenced the microstructure of the aqueous flaxseed fiber dispersions, although the obtained gels were highly stable. The gel-like behavior, the high viscosity at low shear rates, and the high physical stability of the samples studied make them interesting food stabilizers and thickeners. Full article

This study investigates the rheological behavior of cellulose microfiber suspensions derived from kahili ginger stems (Hedychium gardnerianum), an invasive species, in two adhesive matrices: a commercial water-based adhesive (Coplaseal^®) and a casein-based adhesive made from non-food-grade milk, referred to as K and S samples, respectively. Rheological analyses were performed using oscillatory and rotational shear tests conducted at 25 °C, 50 °C, and 75 °C to assess the materials’ viscoelastic properties more comprehensively. Oscillatory tests across a frequency range of 1–100 rad/s assessed the storage modulus (G′) and loss modulus (G″), while rotational shear tests evaluated apparent viscosity and shear stress across shear rates from 0.1 to 1000 s⁻¹. Fiber-free samples consistently showed lower moduli than fiber-containing samples at all frequencies. The incorporation of fibers increased the dynamic moduli in both K and S samples, with a quasi-plateau observed at lower frequencies, suggesting solid-like behavior. This trend was consistent in all tested temperatures. As frequencies increased, the fiber network was disrupted, transitioning the samples to fluid-like behavior, with a marked increase in G′ and G″. This transition was more pronounced in K samples, especially above 10 rad/s at 25 °C and 50 °C, but less evident at 75 °C. This shift from solid-like to fluid-like behavior reflects the transition from percolation effects at low frequencies to matrix-dominated responses at high frequencies. In contrast, S samples displayed a wider frequency range for the quasi-plateau, with less pronounced moduli changes at higher frequencies. At 75 °C, the moduli of fiber-containing and fiber-free S samples nearly converged at higher frequencies, indicating similar effects of the fiber and matrix components. Both fiber-reinforced and non-reinforced suspensions exhibited pseudoplastic (shear-thinning) behavior. Fiber-containing samples exhibited higher initial viscosity, with K samples displaying greater differences between fiber-reinforced and non-reinforced systems compared to S samples, where the gap was narrower. Interestingly, S samples exhibited overall higher viscosity than K samples, implying a reduced influence of fibers on the viscosity in the S matrix. This preliminary study highlights the complex interactions between cellulosic fiber networks, adhesive matrices, and rheological conditions. The findings provide a foundation for optimizing the development of sustainable biocomposites, particularly in applications requiring precise tuning of rheological properties. Full article

In this paper, we propose a novel approach to coordinate-based acoustic emission (AE) source localization to address the challenges of limited and imbalanced datasets from fiber-optic AE sensors used for structural health monitoring (SHM). We have developed a hybrid deep learning model combining four generative adversarial network (GAN) variants for data augmentation with an adapted inception neural network for regression-based prediction. The experimental setup features a single fiber-optic AE sensor based on a tightly coiled fiber-optic Fabry-Perot interferometer formed by two identical fiber Bragg gratings. AE signals were generated using the Hsu-Nielsen pencil lead break test on a grid-marked thin aluminum plate with 35 distinct locations, simulating real-world structural monitoring conditions in bounded isotropic plate-like structures. It is demonstrated that the single-sensor configuration can achieve precise localization, avoiding the need for a multiple sensor array. The GAN-based signal augmentation expanded the dataset from 900 to 4500 samples, with the Wasserstein distance between the original and synthetic datasets decreasing by 83% after 2000 training epochs, demonstrating the high fidelity of the synthetic data. Among the GAN variants, the standard GAN architecture proved the most effective, outperforming other variants in this specific application. The hybrid model exhibits superior performance compared to non-augmented deep learning approaches, with the median error distribution comparisons revealing a significant 50% reduction in prediction errors, accompanied by substantially improved consistency across various AE source locations. Overall, this developed hybrid approach offers a promising solution for enhancing AE-based SHM in complex infrastructures, improving damage detection accuracy and reliability for more efficient predictive maintenance strategies. Full article

Nowadays, a variety of underwater activities, such as underwater surveillance, marine monitoring, etc., are becoming crucial worldwide. Underwater sensors and autonomous underwater vehicles (AUVs) are widely adopted for underwater exploration. Underwater communication via radio frequency (RF) or acoustic wave suffers high transmission loss and limited bandwidth. In this work, we present and demonstrate a rolling shutter (RS)-based underwater optical camera communication (UWOCC) system utilizing a long short-term memory neural network (LSTM-NN) with side glow optical fiber (SGOF). SGOF is made of poly-methyl methacrylate (PMMA) SGOF. It is lightweight and flexibly bendable. Most importantly, SGOF is water resistant; hence, it can be installed in an underwater environment to provide 360° “omni-directional” uniform radial light emission around its circumference. This large FOV can fascinate the optical detection in underwater turbulent environments. The proposed LSTM-NN has the time-memorizing characteristics to enhance UWOCC signal decoding. The proposed LSTM-NN is also compared with other decoding methods in the literature, such as the PPB-NN. The experimental results demonstrated that the proposed LSTM-NN outperforms the PPB-NN in the UWOCC system. A data rate of 2.7 kbit/s can be achieved in UWOCC, satisfying the pre-forward error correction (FEC) condition (i.e., bit error rate, BER ≤ 3.8 × 10⁻³). We also found that thin fiber also allows performing spatial multiplexing to enhance transmission capacity. Full article

Fiber lasers, the latest laser-cutting technology, are notable for their high process efficiency, cutting precision, and high cutting quality for thin materials. However, the quality of the cut significantly decreases when machining thicker materials. For now, this is a challenge for the metalworking industry. This study investigated the effects of laser power, cutting speed, and auxiliary gas pressure on the fiber-laser cutting quality of 4 and 6 mm thick S355JR steel plates. To evaluate the influence of cutting parameters on cutting quality, surface roughness, dimensional accuracy and cut taper were measured. A microscopic analysis of the laser cuts was performed, revealing the heat-affected zone, transition zone and unaffected base-material zone. Research results show that laser cutting is a complex process, and the correct choice of cutting parameters greatly influences the cutting performance and final quality. An artificial neural network was created and trained using the results from measuring the quality characteristics to achieve optimum cutting quality. The accuracy of the optimization model was assessed by control samples, which were cut using calculated optimum parameters. The actual values of the quality characteristics only slightly differ from the predicted values, showing that the optimization model is suitable for selecting cutting parameters. Full article

This study presents fibers based on methacrylic acid–methyl methacrylate (Eudragit L100) as Cu(II) adsorbents, resulting in antimicrobial complexes. Eudragit L100, an anionic copolymer synthesized by radical polymerization, was electrospun in dimethylformamide (DMF) and ethanol (EtOH). The electrospinning process was optimized through a 2²-factorial design, with independent variables (copolymer concentration and EtOH/DMF volume ratio) and three repetitions at the central point. The smallest average fiber diameter (259 ± 53 nm) was obtained at 14% w/v Eudragit L100 and 80/20 EtOH/DMF volume ratio. The fibers were characterized using scanning electron microscopy (SEM), infrared spectroscopy in attenuated total reflectance mode (FTIR-ATR), and differential scanning calorimetry (DSC). The pseudo-second-order mechanism explained the kinetic adsorption toward Cu(II). The fibers exhibited a maximum adsorption capacity (q_e) of 43.70 mg/g. The DSC analysis confirmed the Cu(II) absorption, indicating complexation between metallic ions and copolymer networks. The complexed fibers showed a lower degree of swelling than the non-complexed fibers. The complexed fibers exhibited bacteriostatic activity against Gram-negative (Pseudomonas aeruginosa) and Gram-positive (Staphylococcus aureus) bacteria. This study successfully optimized the electrospinning process to produce thin fibers based on Eudragit L100 for potential applications as adsorbents for Cu(II) ions in aqueous media and for controlling bacterial growth. Full article

This study focused on the efficacy of employing a pulsed fiber laser in the curved cutting of thin, non-oriented electrical steel sheets. Experiments were conducted in paraffinic oil by adjusting the input process parameters, including laser power, pulse frequency, cutting speed, and curvature radius. The multiple output quality metrics included kerf width, inner and outer heat-affected zones, and re-welded portions. Analyses of the Random Forest Method and Response Surface Method indicated that laser pulse frequency was the most important variable affecting the cut quality, followed by laser power, curvature radius, and cutting speed. To improve cut quality, an innovative artificial intelligence (AI) approach incorporating a deep neural network (DNN) model and a modified equilibrium optimizer (M-EO) was proposed. Initially, the DNN model established correlations between input parameters and cut quality aspects, followed by M-EO pinpointing optimal cut qualities. Such an approach successfully identified an optimal set of laser process parameters, even beyond the specified process window from the initial experiments on curved cuts, resulting in significant enhancements confirmed by validation experiments. A comparative analysis showcased the developed models’ superior performance over prior studies. Notably, while the models were initially developed based on the results from curved cuts, they proved adaptable and capable of yielding comparable outcomes for straight cuts as well. Full article

The recent advancements in communication technology have facilitated the widespread deployment of electronic communication equipment globally, resulting in the pervasive presence of electromagnetic pollution. Consequently, there is an urgent necessity to develop a thin, lightweight, efficient, and durable electromagnetic interference (EMI) shielding material capable of withstanding severe environmental conditions. In this paper, we propose an innovative and scalable method for preparing EMI shielding films with a tunable sandwich structure. The film possesses a nylon mesh (NM) backbone, with AgNWs serving as the shielding coating and aramid nanofibers (ANFs) acting as the cladding layer. The prepared film was thin and flexible, with a thickness of only 0.13 mm. AgNWs can easily form a conductive network structure, and when the minimum addition amount was 0.2 mg/cm², the EMI SE value reached 28.7 dB, effectively shielding 99.884% of electromagnetic waves and thereby meeting the commercial shielding requirement of 20 dB. With an increase in dosage up to 1.0 mg/cm², the EMI SE value further improved to reach 50.6 dB. The NAAANF film demonstrated remarkable robustness in the face of complex usage environments as a result of the outstanding thermal, acid, and alkali resistance properties of aramid fibers. Such a thin, efficient, and environmentally resistant EMI shielding film provided new ideas for the broad EMI shielding market. Full article

Previous studies demonstrated coupling of acoustic guided waves from one optical fiber to another through a simple adhesive bond coupler. This paper experimentally utilizes such an adhesive bond coupler to easily extend an already existing sensor network. We experimentally demonstrate this concept for detecting simulated cracks growing from circular holes in a thin aluminum plate. A single, remotely bonded FBG sensor is used to detect the original crack growth, followed by the addition of other optical fiber segments using adhesive couplers to detect new crack growth locations on the plate. A laser Doppler vibrometer is also used to measure the guided wave propagation through the plate to verify that the changes in the FBG sensor measurements are due to the growth of the cracks. Full article

Mooring systems are incorporated from mooring (thin) lines that are constituted of fiber ropes, steel wires, and chains. Mooring systems are used for the stationary keeping of floating units during the drilling process of oil and gas from offshore deep water, and the unloading of productions to the shuttle storage tanker. However, it is crucial to monitor mooring systems for early-stage failure detection in mooring lines during offshore mooring operations to avoid any unexpected losses, including human injuries, and catastrophic failure. This paper addresses the challenges of mooring line detection, and proposes a deep learning-based approach for the detection of mooring lines from marine images using the bounding box. A convolutional neural network, Inception v3, is used for the detection and classification of thin-line objects from marine images, and it is a pre-trained model with 1000 classes. Furthermore, various testing samples have been evaluated for assessing the performance of the pre-trained proposed model. According to the results, it has been observed that the proposed model obtained the highest accuracy (87.33%) in classifying the mooring line objects from images, but failed to accurately detect mooring lines. Furthermore, in a few highlighted cases, the performance of the model decreased in terms of accuracy due to the misclassification and wrong detection of mooring line objects. Despite this, the proposed study furnishes a potential solution for the detection of failure in mooring lines from marine images. Full article

The regeneration of articular cartilage remains a serious problem in various pathological conditions such as osteoarthritis, due to the tissue’s low self-healing capacity. The latest therapeutic approaches focus on the construction of biomaterials that induce cartilage repair. This research describes the design, synthesis, and investigation of a safe, “smart”, fibrous scaffold containing a genetically incorporated active peptide for chondrogenic induction. While possessing specific sequences and the respective mechanical properties from natural fibrous proteins, the fibers also incorporate a Transforming Growth Factor-β1 (TGF-β1)-derived peptide (YYVGRKPK) that can promote chondrogenesis. The scaffold formed stable porous networks with shear-thinning properties at 37 °C, as shown by SEM imaging and rheological characterization, and were proven to be non-toxic to human dental pulp stem cells (hDPSCs). Its chondrogenic capacity was evidenced by a strong increase in the expression of specific chondrogenesis gene markers SOX9, COL2, ACAN, TGFBR1A, and TGFBR2 in cells cultured on “scaffold-TGFβ1” for 21 days and by increased phosphorylation of intracellular signaling proteins Smad-2 and Erk-1/2. Additionally, intense staining of glycosaminoglycans was observed in these cells. According to our results, “scaffold-TGFβ1” is proposed for clinical studies as a safe, injectable treatment for cartilage degeneration. Full article

The various forms of cellulose-based materials possess high mechanical and thermal stabilities, as well as three-dimensional open network structures with high aspect ratios capable of incorporating other materials to produce composites for a wide range of applications. Being the most prevalent natural biopolymer on the Earth, cellulose has been used as a renewable replacement for many plastic and metal substrates, in order to diminish pollutant residues in the environment. As a result, the design and development of green technological applications of cellulose and its derivatives has become a key principle of ecological sustainability. Recently, cellulose-based mesoporous structures, flexible thin films, fibers, and three-dimensional networks have been developed for use as substrates in which conductive materials can be loaded for a wide range of energy conversion and energy conservation applications. The present article provides an overview of the recent advancements in the preparation of cellulose-based composites synthesized by combining metal/semiconductor nanoparticles, organic polymers, and metal-organic frameworks with cellulose. To begin, a brief review of cellulosic materials is given, with emphasis on their properties and processing methods. Further sections focus on the integration of cellulose-based flexible substrates or three-dimensional structures into energy conversion devices, such as photovoltaic solar cells, triboelectric generators, piezoelectric generators, thermoelectric generators, as well as sensors. The review also highlights the uses of cellulose-based composites in the separators, electrolytes, binders, and electrodes of energy conservation devices such as lithium-ion batteries. Moreover, the use of cellulose-based electrodes in water splitting for hydrogen generation is discussed. In the final section, we propose the underlying challenges and outlook for the field of cellulose-based composite materials. Full article

To improve the accuracy of deformation perception and shape reconstruction of flexible thin-walled structures, this paper proposes a method based on the combination of FOSS (fiber optic sensor system) and machine learning. In this method, the sample collection of strain measurement and deformation change at each measuring point of the flexible thin-walled structure was completed by ANSYS finite element analysis. The outliers were removed by the OCSVM (one-class support vector machine) model, and the unique mapping relationship between the strain value and the deformation variables (three directions of x-, y-, and z-axis) at each point was completed by a neural-network model. The test results show that the maximum error of the measuring point in the direction of the three coordinate axes: the x-axis is 2.01%, the y-axis is 29.49%, and the z-axis is 15.52%. The error of the coordinates in the y and z directions was large, and the deformation variables were small, the reconstructed shape had good consistency with the deformation state of the specimen under the existing test environment. This method provides a new idea with high accuracy for real-time monitoring and shape reconstruction of flexible thin-walled structures such as wings, helicopter blades, and solar panels. Full article