Mesh Structure Research Articles

3D porous NiCo2(CO3)3/reduced graphene oxide aerogel with heterogeneous interfaces for high-efficiency microwave absorption

Advanced electromagnetic absorbing materials (EAMs) with strong absorption and a wide effective absorption bandwidth (EAB), using innovative microstructural design and suitable multicomponents remain a persistent challenge. Here, we report the production of a material by the hydrothermal reduction of a mixture of graphene oxide (GO), Ni(NO3)2·6H2O, and Co(NO3)2·6H2O, resulting in reduced GO (RGO) with a self-assembled 3D mesh structure filled with NiCo2(CO3)3. The unique microstructure of this assembly not only solves the problem of NiCo2(CO3)3 particles agglomerating but also changes the electromagnetic parameters, thereby improving the impedance matching and attenuation ability. High electromagnetic wave absorption (EMA) was achieved by combining the 3D interconnected mesh structure and the various interfaces between NiCo2(CO3)3 and RGO. The minimal reflection loss (RLmin) was −58.5 dB at 2.3 mm, and the EAB was 6.5 GHz. The excellent EMA performance of the aerogel can be attributed to the multiple reflection, scattering, and relaxation process of the porous 3D structure as well as the strong polarization of the interfacial matrix.

Generative deep learning model‐based manifold construction for ATN biomarkers

AbstractBackgroundGenerative models have the potential to be beneficial in neuroimaging, as they can produce new data samples that closely resemble the ones they were trained on. This can assist in addressing the scarcity of brain scan data, which is a significant issue in neuroscience.MethodIn this study, we developed a one‐ring convolution‐based generative adversarial network (GAN) to generate ATN biomarkers, which operate on an icosahedron cortical mesh structure on a cortical surface. Data from the Alzheimer’s Disease Neuroimaging Initiative cohort was used in the study, including AV45, AV1451, and T1 magnetic resonance imaging (MRI) scans from 2283 subjects. The cortical thickness of all T1 MRI images was calculated using a Freesurfer pipeline and resampled into icosahedron subdivision level 3. AV45 and AV1451 scans were then converted into standard uptake value ratio (SUVR) and mapped to the cortical surface before being resampled into icosahedron subdivision level 3. For each modality, the proposed GAN was trained and evaluated using density and coverage metrics, which measure the fidelity and diversity of generated samples. Also, we validated the efficacy of the constructed ATN manifolds using the data augmentation scenario. Specifically, we evaluated the performance of tau stage classifiers using cortical thickness data with and without augmenting tau PET data from our manifolds.ResultOur proposed GAN achieved high performance in density and coverage. Specifically, the density and coverage for the amyloid SUVR were 1.0 and 0.97, for the tau SUVR were 1.34 and 1.0, and for the cortical thickness were 1.66 and 0.98, respectively, showing high performance in generating realistic and diverse samples for all modalities. The effectiveness of our proposed GAN was further demonstrated to show improved performance in the Tau Braak stage classification experiment when using the generated data. When augmented with generated data, the classification accuracy was improved from 0.77 to 0.84 in terms of AUC scores.ConclusionOur proposed GAN achieved high performance in density and coverage, generating realistic and diverse samples for each ATN biomarker, and demonstrated improved performance in a Tau Braak stage classification experiment when using the generated data.

STAR-TM: STructure Aware Reconstruction of Textured Mesh From Single Image.

We present a novel method for single-view 3D reconstruction of textured meshes, with a focus to address the primary challenge surrounding texture inference and transfer. Our key observation is that learning textured reconstruction in a structure-aware and globally consistent manner is effective in handling the severe ill-posedness of the texturing problem and significant variations in object pose and texture details. Specifically, we perform structured mesh reconstruction, via a retrieval-and-assembly approach, to produce a set of genus-zero parts parameterized by deformable boxes and endowed with semantic information. For texturing, we first transfer visible colors from the input image onto the unified UV texture space of the deformable boxes. Then we combine a learned transformer model for per-part texture completion with a global consistency loss to optimize inter-part texture consistency. Our texture completion model operates in a VQ-VAE embedding space and is trained end-to-end, with the transformer training enhanced with retrieved texture instances to improve texture completion performance amid significant occlusion. Extensive experiments demonstrate higher-quality textured mesh reconstruction obtained by our method over state-of-the-art alternatives, both quantitatively and qualitatively, as reflected by a better recovery of texture coherence and details.

On the importance of Crystal Plasticity Finite Element discretisation for the identification of crack initiation in RCF using energy-based criteria

Material microstructure plays a key role in crack initiation under rolling contact fatigue. When studying microstructure with crystal plasticity finite element method (CPFE), mesh sensitivity study is of great importance, as the surface-near region is under high uniaxial stresses. In this paper, a new structured mesh strategy is purposed and compared with the classical unstructured mesh strategy. Modelling tests on a bi-grain and a polycrystal model show the calculation of geometrically necessary dislocation (GND) density, recently proposed as a suitable fatigue damage indicator, is highly dependent on mesh morphology, when GND hotspots tend to appear near distorted elements even in homogeneous materials. With uniform mesh size and shape, structured mesh elements can provide physically more acceptable GND calculations, which is particularly important in loading scenarios with complex stresses, such as rolling contact fatigue. Computational efficiency is also improved compared to unstructured models because a smaller number of elements are required in a structured mesh model and pre-processing of the mesh is not required.

Evaluation of Gelatin/Hyaluronic Acid-Generated Bridging in a 3D-Printed Titanium Cage for Bone Regeneration.

3D-printed titanium (Ti) cages present an attractive alternative for addressing issues related to osteoporosis-induced fractures, accidental fractures, and spinal fusion surgery due to disc herniation. These Ti-based bone implants possess superior strength compared to other metals, allowing for versatile applications in orthopedic scenarios. However, when used as standalone solutions, certain considerations may arise, such as interaction with soft tissues. Therefore, to overcome these issues, the combination with hydrogel has been considered. In this study, to impart Ti with regenerative abilities a 3D-printed Ti cage was loaded with gelatin and hyaluronic acid (G-H) to improve the cell attachment ability of the Ti-based bone implants. The void spaces within the mesh structure of the 3D Ti cage were filled with G-H, creating a network of micro-sized pores. The filled G-H acted as the bridge for the cells to migrate toward the large inner pores of the 3D Ti cage. Due to the microporous surface and slow release of gelatin and hyaluronic acid, the biocompatibility of the coated Ti cage was increased with an elevation in osteoconduction as depicted by the up-regulation of bone-related gene expressions. The in vivo implantation in the rabbit femur model showed enhanced bone regeneration due to the coated G-H on the Ti cage compared to the pristine hollow Ti cage. The G-H filled the large holes of the 3D Ti cage that acted as a bridge for the cells to travel inside the implant and aided in the fast regeneration of bone.

Design and analysis of wireless data center network topology HCDCN based on VLC

With the rapid development of the Internet of Things, big data, cloud computing and other industries, the data center network, as the core infrastructure of the computing industry, is becoming increasingly important. Wireless communication technology is developing rapidly. This paper uses visible light wireless communication technology to build a wireless data center network, solving the problems of complex wiring and expansion difficulties in the existing data center network.Aiming at the problems of insufficient spectrum, low bandwidth utilization and large energy consumption in the existing wireless data center, this paper adopts the visible light wireless communication technology based on LED, and proposes a new wireless data center network structure HCDCN. Firstly, work have been done to analyze the feasibility of introducing data centers in this technology, and transform server racks in combination with the characteristics of the technology. Secondly, the construction process of the structure is introduced and a new coding rule is proposed. In order to increase the routing efficiency and fault tolerance, the rack top link routing path is added, and an efficient routing algorithm is designed. Finally, the aggregate throughput of HCDCN under different scales is obtained through experiments, and the network performance of HCDCN structure is compared with that of OWCells and Mesh structure. Experiments have proved that HCDCN's network performance is better than the other two structures under the same conditions. The new wireless data center network proposed in this paper has largely alleviated the bandwidth shortage of hotspot servers.

Effect of 3D titanium substrates with TiO2 nanotube arrays on photoelectrocatalysis degradation of phenol

TiO2 nanotube array electrodes are coated on 3D titanium foam and mesh by the anodic oxidation procedure to investigate the effect of electrode substrates. Distribution homogeneity, crystal shape, structure, and light absorption of TiO2 nanotube are the same, orientations are different due to the planar structure of the titanium foam and the non-planar structure of the titanium mesh. However, the performance of the photoelectrocatalysis, phenol degradation, and charge transfer significantly influences by anode substrates. Although the photocurrent density of titanium mesh is 3.29 mA·cm−2, which is 1.5% lower than that of titanium foam, 96.1% ± 2% of the phenol can be degraded by the titanium mesh anode in 120 min with a degradation efficiency of 86.5% ± 3% after 4 cycles, demonstrated titanium mesh substrate is better than titanium foam. The mechanism results show that·OH and photoelectric synergy play a primary role in phenol degradation, and electrocatalysis (29.8%) increased by anode substrates, is better than photocatalysis (17.9%). The electric field enhances by electrocatalysis, resulting in accelerating electron transfer and decreasing in the recombination of electron and hole. Due to its porous structure and large specific surface area, the 3D substrate improves mass transport, providing more photocatalysts, reactants, and reaction sites for photoelectrochemical reactions, further enhancing photoelectric synergy, and ultimately promoting phenol degradation. This study confirms the role of 3D anode titanium substrate and encourages electric field enhancement to solve the recombination of electrons and holes.

Singularity structure simplification for hex mesh via integer linear program

Topology optimization of hexahedral (hex) meshes has been a widely studied topic, with the primary goal of optimizing the singularity structure. Previous works have focused on simplifying complex singularity structures by collapsing sheets/chords. However, these works require a large number of checks during the process to prevent illegal operations. Moreover, the employed simplification strategies are not based on the topological characteristics of the structure, but rather on the rank of the components that can be simplified. To overcome these problems, we analyze how topology operations affect the degree of edges in hex meshes, and introduce a fast and automatic algorithm to simplify the singularity structure of hex meshes. The algorithm relies on sheet operations, using mesh volume as a metric to assess the degree of simplification. Moreover, it designs constraints to prevent illegal operations and employs integer linear program to plan the overall optimization strategy for a mesh. After that, we relax the singularity constraints to further simplify the structure, and handle unreasonable singularities via sheet inflation operation. Our algorithm can also improve singularity structure without merging singularities by adjusting the singularity constraint conditions. Numerous experiments demonstrate the effectiveness and efficiency of our algorithm.

Agglomeration of polygonal grids using graph neural networks with applications to multigrid solvers

Agglomeration-based strategies are important both within adaptive refinement algorithms and to construct scalable multilevel algebraic solvers. In order to automatically perform agglomeration of polygonal grids, we propose the use of Machine Learning (ML) strategies, that can naturally exploit geometrical information about the mesh in order to preserve the grid quality, enhancing performance of numerical methods and reducing the overall computational cost. In particular, we employ the k-means clustering algorithm and Graph Neural Networks (GNNs) to partition the connectivity graph of a computational mesh. Moreover, GNNs have high online inference speed and the advantage to process naturally and simultaneously both the graph structure of mesh and the geometrical information, such as the areas of the elements or their barycentric coordinates. These techniques are compared with METIS, a standard algorithm for graph partitioning, which is meant to process only the graph information of the mesh. We demonstrate that performance in terms of quality metrics is enhanced for ML strategies. Such models also show a good degree of generalization when applied to more complex geometries, such as brain MRI scans, and the capability of preserving the quality of the grid. The effectiveness of these strategies is demonstrated also when applied to MultiGrid (MG) solvers in a Polygonal Discontinuous Galerkin (PolyDG) framework. In the considered experiments, GNNs show overall the best performance in terms of inference speed, accuracy and flexibility of the approach.

Experimental Study on the Environmental Effects and Mechanical Characteristics of Solidified Cr(VI)-Contaminated Soil Based on Slag and Desulfurization Gypsum

This study proposed a new curing agent consisting of slag and desulfurization gypsum industrial waste to solidify Cr(VI)-contaminated soils and prevent its migration and bioaccumulation in the ecosphere. The curing agent dosage of 10–30% resulted in a Cr(VI) toxic leaching concentration, compressive strength, and hydraulic conductivity range of 0.118–5.824 mg/L, 2.70–10.22 MPa, and 1.70 × 10−9–1.37 × 10−6 cm/s, respectively. Following four dry and wet cycles, the dosage of the curing agent was found to be 20–30% to achieve minimum environmental safety requirements. Cr(VI) in the cured specimens mainly existed as CrO42−, or acid salt, in which a portion was changed to Cr(III) during precipitation or directly was encased in the silica-alumina mesh structure. The adsorption capacity of hexavalent chrome on the outer of the hydration product groups was insignificant owing to the electronegativity. Hence, the Cr(VI) was solidified by hydrides such as C-S(A)-H and calcium alumina inclusions. Calcite, quartz, and several zeolite-like substances were also found to be colloidal in the pores to block Cr(VI).

A novel and facile preparation of superhydrophilic/superoleophobic Mg(OH)2/ZnO coated mesh for fast and efficient oil/water separation

This study presents a new method for the rapid preparation of a superhydrophilic/underwater superoleophobic Mg(OH)2/ZnO coated mesh. Through one-step electrodeposition, a high underwater oil contact angle of 168.54° was achieved on the coated mesh. The electrodeposition process involved the deposition of ZnO nanoparticles on the cathode due to the presence of Mg2+ in the electroplating solution, while Mg(OH)2 was simultaneously formed on the surface of the cathode copper mesh. The cathode surface, where hydrogen is generated, leads to the formation of micropores and nanosheets on the Mg(OH)2/ZnO coated mesh structure. This structural feature traps more water molecules, further enhancing the superhydrophilicity of the coated mesh. The oil/water separation efficiencies for p-xylene, kerosene, paraffin liquid, and mineral oil were 99.8 %, 99.3 %, 99.5 %, and 99.4 %, respectively. The separation efficiency remained higher than 99.2 % even after conducting 350 p-xylene/water separation experiments. Additionally, the coated mesh displayed a strong oil/water separation ability after being immersed in alkali and salt solutions (pH = 7, 9) for 10 h. The coated mesh exhibited an enhanced water flux of (2.99 ± 0.20) × 105 L·m−2·h−1, along with a heightened invasion pressure (>2.8 kPa) and excellent oil-resistance performance. Overall, the Mg(OH)2/ZnO coated mesh developed in this study shows promising potential as a membrane for fast and efficient oil/water separation, with outstanding durability.

Evaluation of the Fixation Method and the Intraperitoneal Mesh Location Effect on Adhesion Formation, Deformation, and Shrinkage of the Mesh in Large Animals

Background: The quality of mesh integration and adhesion formation in the intraperitoneal onlay mesh hernia repair (IPOM) is considered to depend not only on the mesh structure but also on a complex of other factors. This study aims to evaluate the correlation between the adhesion formation, mesh deformation, and shrinkage on the fixation method and the mesh location in the abdominal cavity.Materials and methods: During laparoscopy (IPOM), 6 different meshes (2 each in the epi-, meso-, and hypogastric regions) were installed in the intraperitoneal position in each of 3 pigs. For mesh fixation, absorbable poly-D, L-lactide or non-absorbable stainless steel and polyetheretherketone (PEEK) tacks were randomly selected. Relaparoscopy was performed after 45 days, autopsy after 90 days. The results were calculated for meshes with absorbable and non-absorbable tacks and for meshes placed in the epi-, meso-, and hypogastrium.Results: The fixation methods did not significantly affect the deformation and shrinkage of the mesh. Absorbable tacks were associated with higher adhesion formation for most indicators at all stages of the study. The difference between absorbable and non-absorbable fixation reached a maximum by day 90, when the number of mesh pieces with adhesions was 66.7% vs 11.1% (P = .05), and the adhesion scores were 5 (0-6) vs 0 (0-0) (P = .01). The mesh location on the abdominal wall did not significantly affect its deformation and shrinkage. At the same time, most of the adhesion indicators depended on the mesh location. Starting from hypogastrium, the extent and the type of adhesions increased and were maximal in epigastrium. The greatest difference between the epi-, meso-, and hypogastrium was noted by day 45, when the number of mesh pieces with adhesions was 100%, 50%, and 16.7% (P = .014), and the adhesion scores were 6 (5-7), 2 (0-4), and 0 (0-0) (P = .007).Conclusions: The fixation method and the intraperitoneal mesh location do not affect its deformation and shrinkage but significantly affect the adhesion formation.

MeshNet-SP: A Semantic Urban 3D Mesh Segmentation Network with Sparse Prior

A textured urban 3D mesh is an important part of 3D real scene technology. Semantically segmenting an urban 3D mesh is a key task in the photogrammetry and remote sensing field. However, due to the irregular structure of a 3D mesh and redundant texture information, it is a challenging issue to obtain high and robust semantic segmentation results for an urban 3D mesh. To address this issue, we propose a semantic urban 3D mesh segmentation network (MeshNet) with sparse prior (SP), named MeshNet-SP. MeshNet-SP consists of a differentiable sparse coding (DSC) subnetwork and a semantic feature extraction (SFE) subnetwork. The DSC subnetwork learns low-intrinsic-dimensional features from raw texture information, which increases the effectiveness and robustness of semantic urban 3D mesh segmentation. The SFE subnetwork produces high-level semantic features from the combination of features containing the geometric features of a mesh and the low-intrinsic-dimensional features of texture information. The proposed method is evaluated on the SUM dataset. The results of ablation experiments demonstrate that the low-intrinsic-dimensional feature is the key to achieving high and robust semantic segmentation results. The comparison results show that the proposed method can achieve competitive accuracies, and the maximum increase can reach 34.5%, 35.4%, and 31.8% in mR, mF1, and mIoU, respectively.

MDSSN: An end-to-end deep network on triangle mesh parameterization

Mesh data plays a crucial role in a wide range of applications worldwide within the field of 3D computer vision. In contrast to traditional Euclidean space arrangements, meshes encompass spatial information, including edges, faces, angles, and graph structures that embed face information within the mesh data. Nevertheless, conventional deep learning frameworks such as convolutional neural networks (CNNs) have struggled to achieve significant advancements in handling meshes. This paper proposes a simple mesh computation framework called Mesh Decomposition Second-order Sobolev Network (MDSSN) to model triangle meshes and represent their shape. The construction of face-based Riemannian and edge-based Riemannian graphs is motivated by the remarkable representation capabilities of edges and faces in mesh data, achieved through a graph composition mechanism. Furthermore, we design end-to-end operators, including the Sobolev-filter block, pooling, and unpooling blocks, which draw inspiration from traditional deep learning frameworks for modeling the mesh structure. Importantly, the design of the mesh-filter block incorporates the second-order Sobolev metric. To address common challenges in mesh classification and segmentation tasks, we have designed dedicated classification and segmentation modules. We utilize a joint loss function that integrates losses from adjacent faces and categories. We evaluate the performance of the MDSSN in mesh classification and segmentation tasks, focusing on its ability to represent 3D shapes. Experimental results demonstrate that MDSSN achieves superior performance compared to other state-of-the-art approaches.

Engineering of 0D/1D architectures in 3D networks over CDs/PPy-CPP biomass foam with high efficiency on seawater evaporation

Converting waste biomass into solar evaporator foam has been recognized as an effective strategy for waste recycling and solving the problem of water shortage, but problems of salt deposition and mechanical stability seriously hinder their practical application. Herein, this work proposes a sustainable and high mechanical strength biomass foam (CDs/PPy-CPP) evaporator with zero-dimensional/one-dimensional (0D/1D) hierarchical structure by hydrophilic carbon dots (CDs) and tubular polypyrrole (PPy) are seamlessly integrated into the outer surface of the three-dimensional (3D) carbonized pomelo peel (CPP) biomass foam. The evaporator achieved a high-efficient evaporation rate of up to 2.46 kg m−2 h−1 in 3.5 wt% NaCl solution at 1 sun irradiation, and similarly exhibited outstanding salient evaporation efficiency and resistance to salt deposition in 25 wt% NaCl solution, as well as prominent swage purification ability. Excellent photothermal conversion efficiency is demonstrated attributed to the 0D/1D structure in 3D networks of the CDs/PPy-CPP evaporator, as well as superior mechanical and water transport properties based on the multilayer mesh structure and microscopic water transport pores. The research intends to offer a low-cost, energy-efficient, and environmentally friendly approach to obtain multifunctional photothermal biomass foams and demonstrate the potential applications in seawater desalinization and swage purification.

Investigation on microstructure characteristics of crumb rubber compound modified asphalt at preparation process

This study presented the investigation on the microstructure characteristics and the interaction among asphalt, crumb rubber (CR) and modifiers (softener, activator, and cross-linking agent) of crumb rubber compound modified asphalt (CRCMA) at process phases by microscopic testing techniques. The results showed that CRCMA had excellent rheological properties at high or low-temperature. The softener was only physically mixed with asphalt and did not change the microscopic morphology of the asphalt. The activator can break some of the chemical bonds of CR and promote the desulphurization degradation reaction which makes the asphalt show a large number of small-body type mesh structures and a small amount of chains in microscopic morphology. The crosslinking agent provided a source of sulphur to induce the crosslinking reaction which resulted in an increase in the reticulation of CRCMA. Finally, a modification mechanism model and the evolution mechanisms based on the microstructure characteristics at preparation phases of CRCMA were proposed.

Calibration of the Mechanical Boundary Conditions for a Patient-Specific Thoracic Aorta Model Including the Heart Motion Effect.

We propose a procedure for calibrating 4 parameters governing the mechanical boundary conditions (BCs) of a thoracic aorta (TA) model derived from one patient with ascending aortic aneurysm. The BCs reproduce the visco-elastic structural support provided by the soft tissue and the spine and allow for the inclusion of the heart motion effect. We first segment the TA from magnetic resonance imaging (MRI) angiography and derive the heart motion by tracking the aortic annulus from cine-MRI. A rigid-wall fluid-dynamic simulation is performed to derive the time-varying wall pressure field. We build the finite element model considering patient-specific material properties and imposing the derived pressure field and the motion at the annulus boundary. The calibration, which involves the zero-pressure state computation, is based on purely structural simulations. After obtaining the vessel boundaries from the cine-MRI sequences, an iterative procedure is performed to minimize the distance between them and the corresponding boundaries derived from the deformed structural model. A strongly-coupled fluid-structure interaction (FSI) analysis is finally performed with the tuned parameters and compared to the purely structural simulation. The calibration with structural simulations allows to reduce maximum and mean distances between image-derived and simulation-derived boundaries from 8.64 mm to 6.37 mm and from 2.24 mm to 1.83 mm, respectively. The maximum root mean square error between the deformed structural and FSI surface meshes is 0.19 mm. This procedure may prove crucial for increasing the model fidelity in replicating the real aortic root kinematics.

SPROUT: A Moving-mesh Hydro Code Using a Uniformly Expanding Cartesian Grid

We present the publicly available moving-mesh hydrodynamics code Sprout. Sprout solves the equations of ideal hydrodynamics on an expanding Cartesian mesh. The expanding mesh can follow fluid outflows for several orders of magnitude with very little numerical diffusion, thereby capturing shocks and fine structures accurately. Following the bulk flow accurately also allows for longer time steps in general. This makes Sprout particularly suitable for studying expanding outflows such as supernova remnants and active galactic nuclei. Relative to other moving-mesh codes, the simple mesh structure in Sprout is also convenient for implementing additional physics or algorithms. Many code tests are performed to test the accuracy and performance of the numerical scheme.

EXPERTISE OF PYRAMID TEA BAGS BY OPTICAL MICROSCOPY AND FTIR-SPECTROSCOPY METHODS. MICROPLASTICS FORMATION IN BREWED TEA

Eight brands of pyramid tea bags on the Ukrainian market were studied: Sun Gardens (1), Lovare (2), Curtis for Mc Donalds (3), Curtis (4), Lipton (5), Premiya (6), Sonnet (7) and Loyd (8). Using FTIR spectroscopy, it was found that the pyramids of the bags are made of thermoplastic polymers polyethylene terephthalate (samples 1-7) and polylactic acid (sample 8). The threads attached to the pyramids are made of thermoplastic polymers polypropylene (samples 1,2,4-7), polyethylene terephthalate (sample 4) and polylactic acid (sample 8). The specific optical rotation of polylactic acid [α]D25 is about -150° (c=1, CHCl3), which refers to poly(L-lactic acid). Using optical microscopy, it was established that the structure of the pyramids are divided into those made of plain woven fabrics (samples 1-3) and heat-bonded nonwoven fabrics (samples 4-8). The tea bags (samples 1-3) exhibit a well patterned net structure (mesh) with the pores perceived to be uniform and regular. The tea bags made of nonwoven materials have an irregular network consisting of compact and random arrangement of fibers. The fibers are pleated randomly, generating irregular pores. Nonwoven fabrics are made from fibers with a diameter of 12-18 µm, woven fabrics are made from fibers with a diameter of 48-54 µm. The area density of nonwoven fabrics is 18.5-20.3 g/m2, the area density of woven fabrics is 22.0-22.7 g/m2. The threads are made by twisting several single-twisted yarns. The structure of threads are a cabled yarn from three 2-ply yarns (samples 1,8), 4-ply yarn (samples 2-4,6,7) and 4-ply multyfilament yarn (samples 5). The final twist of the plied yarn/cord is S-twist. The primary structural element of threads in the case of samples 1-4, 6-8 are staple fibers, of sample 5 are multyfilament fibers. Each empty pyramids and thread was steeped at 95oC for 5 min in 10 mL of water. It has been established that the shape of microplastic particles formed in beverages during tea brewing is determined by the primary structure of tea bags. Both pyramids and threads release irregularly shaped microplastic particles.

Mechanical evaluation for the finite element analysis of intramedullary nailing and plate screw system used in humerus transverse fractures

AimThis study aims to examine the commonly used plate screw system and intramedullary nailing method in osteosynthesis in humeral shaft fractures in terms of stress shielding using finite element analysis. Material and methodsImages were obtained by computerized tomography (CT) to create a 3D model of the humerus bone. After the CT images were transferred to the ANSYS 2021 R2 program (ANSYS, Inc., Canons-burg, PA), a transverse fracture model was created from the shaft region of the humeral bone meshed to the humerus bone and modeled in the 3D environment. ResultsThe tetrahedron mesh structure was used for the finite element models in our study. The element size was chosen as 3.5 mm for the bone model and 2 mm for the plate and intramedullary nail models. The node numbers of bone, intramedullary nail, and plate were 91230, 462578, and 581352, respectively. The element numbers of bone, intramedullary nail, and plate were 61350, 311285, and 370350, respectively. Maximum stress values of 260 MPa on the nail and 280 MPa on the plate were detected in this study. ConclusionFewer stress values were obtained and stress concentrations were not formed on the implant in osteosynthesis performed by intramedullary nailing. It can be concluded that this study may guide further studies for those focusing on it and may contribute to the development of a more comprehensive understanding of the topic.