Spatial Distribution Of Fibers Research Articles

Micromechanical analyses of unidirectional (UD) discontinuous flax fiber reinforced composites

Prediction of the strength and damage behavior of natural fiber composites is a challenging task due to computational difficulties resulting from large fiber aspect ratio, non-uniform fiber length distribution, and interface modelling. A computational micromechanical model, which solves the above-mentioned challenges by incorporating a novel representative volume element (RVE) generation algorithm inspired by Lennard-Jones potential, is developed to predict the tensile behavior of unidirectional (UD) flax/epoxy composites. Effects of fiber aspect ratio, fiber spatial distribution, interfacial and matrix properties on longitudinal modulus, strength, and the damage behavior of the flax/epoxy composites are extensively studied using the numerical model. The modulus and strength predicted by numerical model were compared with both analytical models and experimental results, which not only validated the numerical model but identified the limitations of analytical model. The longitudinal strength of flax/epoxy composites initially increased with fiber aspect ratio. Upon reaching certain fiber aspect ratio (25 in this study), strength predicted by both RVE (RVEs with many fibers) and unit cell (unit cells with two fibers) are close to experimental value (240 MPa). This finding provides confidence and guidance on how to use simplified unit cells to predict the strength of natural fiber composites with acceptable accuracy and much lower computational cost.

A method for quantitative spatial analysis of immunolabeled fibers at regenerative electrode interfaces

BackgroundRegenerative electrodes are being explored as robust peripheral nerve interfaces for neuro-prosthetic control and sensory feedback. Current designs differ in electrode number, spatial arrangement, and porosity which impacts the regeneration, activation, and spatial distribution of fibers at the device interface. Knowledge of sensory and motor fiber distributions are important in optimizing selective fiber activation and recording. New MethodWe use confocal microscopy and immunofluorescence methods to conduct spatial analysis of immunolabeled fibers across whole nerve cross sections. ResultsThis protocol was implemented to characterize motor fiber distribution within 3 macro-sieve electrode regenerated (MSE), 3 silicone-conduit regenerated, and 3 unmanipulated control rodent sciatic nerves. Total motor fiber counts were 1485 [SD: +/- 50.11], 1899 [SD: +/- 359], and 5732 [SD: +/- 1410] for control, MSE, and conduit nerves respectively. MSE motor fiber distributions exhibited evidence of deviation from complete spatial randomness and evidence of dispersion and clustering tendencies at varying scales. Notably, MSE motor fibers exhibited clustering within the central portion of the cross section, whereas conduit regenerated motor fibers exhibited clustering along the periphery. Comparison with Existing MethodsPrior exploration of fiber distributions at regenerative interfaces was limited to either quadrant-based density analysis of randomly sampled subregions or qualitative description. This method extends existing sample preparation and microscopy techniques to quantitatively assess immunolabeled fiber distributions within whole nerve cross-sections. ConclusionsThis approach is an effective way to examine the spatial organization of fiber subsets at regenerative electrode interfaces, enabling robust assessment of fiber distributions relative to electrode arrangement.

Image driven deep learning method with FFT solver for predicting the microscale full field stress of stochastic boundary composites

AbstractA novel image‐driven deep learning approach embedded with model‐data‐knowledge information can directly predict the full field stress distribution and mechanical properties of composites solely based on initial scanning geometry images. The fiber spatial distribution and morphological features are identified by Scanning Electron Microscopy (SEM) initially. An effective random fiber generation algorithm is further utilized to generate images database comprising equivalent geometric models of various microstructures. Subsequently, the geometry model images database is analyzed by fast Fourier transform (FFT) to produce the database including the elastic modulus parameters and full field stress distributions of composites with distinct microstructures. Finally, an innovative image‐learning comprehensive paradigm uniting convolutional neural networks and convolutional autoencoders is elaborated systematically to learn the inherent laws of geometry images and field images. The results show that the proposed method have capacity to effectively and accurately predict the elastic modulus and full field stress distributions combined with error analysis even for stochastic boundary composites. The proposed methodology is a symbiosis of cutting‐edge image learning and non‐destructive testing techniques for composites, which can directly use the local non‐destructive or scanning images of composite structural components to quickly obtain field information and further predict damage evolution online.Highlights Proposing a novel deep learning approach combined with FFT directly to predict stress distribution of stochastic boundary composites solely based on micro‐images. Adopting equivalent geometric models dispersed by higher pixels mesh density considering extreme thin interface. Incorporate adaptive algorithms for streamlined training optimization. An innovative integration of deep learning and non‐destructive testing technology for the stress distribution and properties prediction online.

Micromechanical modeling for longitudinal tensile property of unidirectional CFRP considering dispersion of fiber properties

Longitudinal tensile failure prediction has always been the focus of research on unidirectional (UD) Carbon Fiber Reinforced Polymer (CFRP). In this paper, the longitudinal tensile tests of unidirectional T800 grade carbon fiber reinforced epoxy resin composite were carried out first. The experimental stress–strain curves show that there are obvious progressive damages before the final failures of the specimens. However, this phenomenon is usually neglected in most of the existing investigations and tests to only obtain elastic modulus and ultimate strength. Thus, a Representative Volume Element (RVE) model based on micromechanics was established using finite element method, considering the randomness of fiber spatial distribution and fiber strength. The tensile strength of fiber monofilaments was assumed to follow a statistical two-parameter Weibull distribution. The failure process of UD composite under longitudinal tensile load was simulated by explicit finite element method. The stress–strain curves obtained by simulation are in good agreement with the experimental results. Finally, the influence of different boundary conditions on the calculation of the RVE was studied. It is found that the final failure strengths of the RVE with uniform displacement boundary conditions are similar to the case of periodic boundary conditions, while the former has higher computational efficiency.

Synthesising realistic 2D microstructures of unidirectional fibre-reinforced composites with a generative adversarial network

The microstructure governs the behaviour of unidirectional fibre-reinforced composites. In this study, we developed a Deep Convolutional Generative Adversarial Network (DCGAN) to generate realistic 2D transverse microstructures of such composites. We evaluated the DCGAN-generated microstructures using three different methods: Fréchet inception distance, walking through the latent space, and feature matching. The results from these evaluations confirmed that the generated microstructures are distinct and not simply a replication of the training data. The generated microstructures were then compared to real microstructures, confirming that they match qualitatively and quantitatively with respect to detailed statistical characteristics, including fibre diameters, fibre volume fraction, fibre spatial distribution, and resin-rich pockets. We also found that microstructures created by traditional generators could not capture the real resin-rich pockets. This illustrates the capability and value of DCGAN to generate realistic transverse microstructures and provides insights for modelling methods based on real images.

Mechanical Properties and Ion Release from Fibre-Reinforced Glass Ionomer Cement.

The aim of this study was to compare the mechanical properties and ion release from a commercially available resin-modified glass ionomer cement to a formulation reinforced by the addition of short glass fibres at various percentages. Methods: Three experimental groups were prepared by adding a mass ratio of 10%, 15% and 20% of short glass fibres to the powder portion of the cement from a capsule (GC Fuji II LC), while the control group contained no fibres. Microhardness (n = 12), fracture toughness, and flexural, compressive and diametral tensile strength (n = 8) were evaluated. To study ion release, readings were obtained utilising fluoro-selective and calcium-selective electrodes after 24 h, 7 days and 30 days (n = 12). The spatial distribution of fibres within the material was evaluated through scanning electron microscopy. The data were analysed using one-way ANOVA with a Bonferroni adjustment. Results: The findings suggest that elevating fibre weight ratios to 20 wt% results in improved mechanical properties (p < 0.05) in microhardness, flexural strength, diametral tensile strength and fracture toughness. In terms of ion release, a statistically significant difference (p < 0.001) was observed between the groups at the conclusion of 24 h and 7 days, when the fluoride release was much higher in the control group. However, after 30 days, no significant distinction among the groups was identified (p > 0.05). Regarding calcium release, no statistically significant differences were observed among the groups at any of the evaluated time points (p > 0.05). SEM showed the fibres were homogeneously incorporated into the cement in all experimental groups. Conclusions: Resin-modified glass ionomer enhanced with short glass fibres at a weight loading of 20% showcased the most favourable mechanical properties while concurrently maintaining the ability to release fluoride and calcium after a 30-day period.

A new scattering-filling process for regulating coarse aggregate and fiber spatial distribution in ultra-high performance concrete

In this study, a novel scattering-filling coarse aggregate process (SFCAP) was employed to prepare sustainable coarse aggregate ultra-high performance concrete (CA-UHPC) without compromising performance. The spatial distribution of CAs and steel fibers in the CA-UHPC were identified by artificial intelligence recognition technology to reveal the mechanism of SFCAP method on improving performance with high CAs contents compared to ordinary preparation process method. The experimental results showed that the SFCAP method could enhance the mechanical properties of CA-UHPC by 10% even at higher CAs content of 40%. Moreover, the SFCAP method improved the CAs homogeneity by 90% and optimized their orientation. The distance among CAs was shortened by SFCAP method, which was conducive to format a tight interlocking skeleton structure inside CA-UHPC. Additionally, the SFCAP method optimized the coordination between steel fibers and CAs and increased cracking paths, resulting in enhanced mechanical properties. Microstructure analysis indicated that the SFCAP method could improve the interface transition zone between CAs and paste and reduced the porosity of CA-UHPC. regression analysis was conducted to connect the spatial parameters of CAs and steel fibers with the mechanical properties of CA-UHPC, further emphasizing the feasibility of the SFCAP method in the production of CA-UHPC. Finally, the sustainability of CA-UHPC prepared by SFCAP method was demonstrated by energy consumption intensity, CO2 emission intensity and cost intensity analyses.

Fibre orientation in SFRC slabs and consequences for punching shear and flexural resistance

The use of steel fibre reinforced concrete (SFRC) is a well-known method for enhancing the punching and flexural resistances of flat slabs. The structural performance of SFRC elements depends significantly on the fibre distribution and orientation, which are typically unknown. One of the largest uncertainties regarding the performance and reliability of SFRC concerns the determination of its post-cracking mechanical properties in the real structural element, normally performed via standardised tests. However, due to differences in element sizes and casting procedures, and to the presence of rebars, the fibre spatial distribution and orientation in the standard specimens differ in general from those of the structural member. Several researchers have studied the correlation between the orientation of fibres and the mechanical performance of SFRC, yet tests were rarely carried out under representative conditions of the actual behaviour of the target structural member. The present paper analyses the spatial distribution and orientation of steel fibres in six SFRC flat slabs that were previously tested in concentric punching at the University of Brescia. The aim of this work is to analyse the fibre orientation and spatial distribution in structural elements. Cores were extracted from the specimens after the punching tests and scanned using micro-computed tomography (μ-CT) to reconstruct the fibre skeleton. The fibre arrangement was analysed focusing on the variation of the fibre spatial distribution and orientation through the slab thickness. A formulation to express the actual fibre dispersion in concrete is proposed, and effectiveness factors are defined to reflect the efficacy of fibres in punching shear and flexure, based on their location in the slab thickness. Pseudo-horizontal fibre orientations are found to be governing, with closer distributions to a 2D scenario for thicker slabs and higher fibre contents. Furthermore, the influence of flexural reinforcement on the fibre orientation has been observed to be significant. The observed fibre orientations are detrimental for the direct transfer of shear forces across cracks, but are favourable for enhancing the flexural capacity of the slab.

Impact of morphological features and chemical composition of tendon biomimetic scaffolds on immune recognition via Toll-like receptors.

Tendinopathies are a major worldwide clinical problem. The development of tendon biomimetic scaffolds is considered a promising, therapeutic approach. However, to be clinically effective, scaffolds should avoid immunological recognition. It has been well described that scaffolds composed of aligned fibers lead to a better tenocyte differentiation, vitality, proliferation and motility. However, little has been studied regarding the impact of fiber spatial distribution on the recognition by immune cells. Additionally, it has been suggested that higher hydrophilicity would reduce their immune recognition. Herein, polycaprolactone (PCL)-hyaluronic acid (HA)-based electrospun scaffolds were generated with different fiber diameters (in the nano- and micro-scales) and orientations as well as different grades of wettability and the impact of these properties on immunological recognition has been assessed, by means of Toll-like receptor (TLR) reporter cells. Our results showed that TLR 2/1 and TLR 2/6 were not triggered by the scaffolds. In addition, the TLR 4 signalling pathway seems to be triggered to a greater extent by higher PCL and HA concentrations, but the alignment of the fibers prevents the triggering of this receptor. Taken together, TLR reporter cells were shown to be a useful and effective tool to study the potential of scaffolds to induce immune responses and the results obtained can be used to inform the design of fibrous scaffolds for tendon repair.

A new quantitative method to evaluate the spatial distribution of fibres in composites: the degree of randomness

A new quantitative method, the D-index, is proposed to evaluate the departure of a given fibre arrangement from the completely spatial randomness (CSR) pattern. An explicit model which can effectively control the degree of randomness (DoR) is created to investigate the correlation between the D-index and DoR. The theory behind the correlation is analyzed followed by the derivation of first and higher order D-index with respect to the DoR under different fibre volume fractions. This may be the first time an accurate value of the departure from randomness is provided, which is considered important for accurate micro-mechanical analysis.

Bond performance of reinforcing bars in SFRC: Experiments and meso-mechanical model

The bond performance of reinforcing bars in steel fiber-reinforced concrete (SFRC) remains a topic of discussion in the community. The effect of deformed steel fiber types is not fully understood, and a rational mechanics-based model is required to consider this behavior. This study experimentally investigated the bond performance of steel rebars in SFRC containing milled-cut (M) fibers with proven high bridging capacity, namely MCSFRC. The results indicated that M fibers yielded a higher bond strength of rebars compared to hook-end (H), crimped (C), and straight (S) fibers from previous studies. This was attributed to the superior bond resistance of M fibers in the matrix, provided by the rough inner surface, stiff end hook, and longitudinal twist. A meso-mechanical model (rib and fiber scale) was then developed to effectively assess the bond strength of rebars in SFRC, accounting for rib-concrete interaction, as well as the fiber reinforcement differences including fiber–matrix interaction, fiber spatial distribution, content, and dimension. This model was applicable to rebars in SFRC with H, C, S, and M fibers, and the results showed good agreement with 301 tests in the established database. On that basis, a unified local bond-slip model for rebars in SFRC was proposed, which was able to suitably reproduce the entire bond-slip response under pull-out and splitting failures.

Numerical statistics-based research on the spatial distribution of cylindrical fiber in ideal simulated fiber reinforced concrete matrix

The macro properties of fiber reinforced concrete is significantly associated with the spatial distribution of fiber. In this paper, the statistics-based algorithms characterizing fiber spatial distribution including orientation probability, spacing distance and wall effect were proposed based on the established the random generation model of cylindrical fibers in an ideal three-dimensional fiber reinforced concrete matrix. The influence of fiber physical parameters on the overall fiber spatial distribution were statistically analyzed. The results show that the fiber orientation probability in ideal fiber reinforced concrete matrix is almost constant. The fiber geometric size plays an important role on the fiber spatial morphological characteristics, where the fiber spacing distance increases with the fiber length and diameter, and the wall effect is significantly affected by the fiber length. The heterogeneity of fiber distribution characterized by the higher fiber spacing distance in the edge region is associated with the fiber sparsity mainly influenced by the single fiber volume and total fiber fraction. With the constraints of wall effect and principal stress direction, the boundary region parallel to the principal stress has a higher fiber orientation coefficient, while the region perpendicular to principle stress possesses of the lowest coefficient.

A new approach to deposit homogeneous samples of asbestos fibres for toxicological tests in vitro.

In this paper we describe the results obtained with a novel method to prepare depositions of asbestos fibres for toxicological tests in vitro. The technique is based on a micro-dispenser, working as an inkjet printer, able to deposit micro-sized droplets from a suspension of fibres in a liquid medium; we used here a highly evaporating liquid (ethanol) to reduce the experimental time, however other solvents could be used. Both the amount and spatial distribution of fibres on the substrate can be controlled by adjusting the parameters of the micro-dispenser such as deposition area, deposition time, uniformity and volume of the deposited liquid. Statistical analysis of images obtained by optical and scanning electron microscopy shows that this technique produces an extremely homogeneous distribution of fibers. Specifically, the number of deposited single fibres is maximized (up to 20 times), a feature that is essential when performing viability tests where agglomerated or untangled fibrous particles need to be avoided.

Confocal reflectance microscopy for mapping collagen fiber organization in the vitreous gel of the eye.

Vitreous collagen structure plays an important role in ocular mechanics. However, capturing this structure with existing vitreous imaging methods is hindered by the loss of sample position and orientation, low resolution, or a small field of view. The objective of this study was to evaluate confocal reflectance microscopy as a solution to these limitations. Intrinsic reflectance avoids staining, and optical sectioning eliminates the requirement for thin sectioning, minimizing processing for optimal preservation of the natural structure. We developed a sample preparation and imaging strategy using ex vivo grossly sectioned porcine eyes. Imaging revealed a network of uniform diameter crossing fibers (1.1 ± 0.3 µm for a typical image) with generally poor alignment (alignment coefficient = 0.40 ± 0.21 for a typical image). To test the utility of our approach for detecting differences in fiber spatial distribution, we imaged eyes every 1 mm along an anterior-posterior axis originating at the limbus and quantified the number of fibers in each image. Fiber density was higher anteriorly near the vitreous base, regardless of the imaging plane. These data demonstrate that confocal reflectance microscopy addresses the previously unmet need for a robust, micron-scale technique to map features of collagen networks in situ across the vitreous.

A review on micromechanical modelling of progressive failure in unidirectional fibre-reinforced composites

The recent decades have seen various attempts at the numerical modelling of fibre-reinforced polymer (FRP) composites in the aerospace, auto and marine sectors due to their excellent mechanical properties. However, it is still challenging to accurately predict the failure of the composites because of their anisotropic and inhomogeneous characteristics, multiple failure modes and their interaction, especially under multiaxial loading conditions. Micromechanics-based numerical models, such as representative volume elements (RVEs), were developed to understand the progressive failure mechanisms of composites, and assessing existing failure criteria. To this aim, this review paper summarises the development of micromechanics-based RVE modelling of unidirectional (UD) FRP composites reported in the literature, with a focus on those models developed using finite element (FE) and discrete element (DE) methods. The generation of fibre spatial distribution, constitutive models of material constituents as well as periodic boundary conditions are briefly introduced. The progressive failure mechanisms of UD FRP composites simulated by RVEs under various loadings are discussed and the comparison of failure envelopes predicted by numerical results and classical failure criteria are reviewed.

Influence of fiber dispersion and distribution on flexural tensile properties of asphalt mixture Based on finite element simulation

• Basalt fibers can improve the low temperature crack resistance of asphalt mixture. • The Weibull distribution can approximately describe the fiber distribution in basalt fiber asphalt mortar. • Better reinforcement can be achieved by well dispersed basalt fibers. • The spatial distribution of fibers truly influence the flexural tensile properties of asphalt mixture. This study aims to reveal the relationship between the uneven distribution characteristics of basalt fibers and the flexural tensile performance of asphalt mixture. Nano Computed Tomography(nano-CT) was used to explore the realistic distribution features of basalt fibers in asphalt mortar. In addition, the distribution fitting and goodness of fit tests were carried out to find out the distribution form that can well describe the distribution of basalt fibers. The three-dimensional fiber models with different distribution forms were established in finite element software ABAQUS. Then the three-point bending test was simulated to analyze the influence of fiber dispersion and distribution on the flexural tensile properties of the asphalt mixture. Simulation results show that the asphalt mixture beam model shows better tensile resistance when the fibers are better dispersed. Moreover, when more basalt fibers are at the bottom of the middle span, it is more conducive to reducing the peak stress at the bottom of the beam and delaying the cracking of the asphalt mixture.

Electrical cross-sectional imaging of human motor units in vivo

ObjectivesIn many neuromuscular diseases, weakness results from a disruption in muscle fibres' arrangement within a motor unit. Limitations in current techniques mean that the spatial distribution of fibres in human motor units remains unknown. MethodsA flexible multi-channel electrode was developed and bonded to a clinical electromyography (EMG) needle. Muscle fibre action potentials were localised using a novel deconvolution method. This was tested using simulated data, and in recordings collected from the tibialis anterior muscle of healthy subjects. ResultsSimulated data indicated good localisation reliability across all sections of the electrode except the end sections. A corrected fibre density was estimated up to 1.4 fibres/mm2. Across five recordings from three individuals, between 4 and 14 motor units were detected. Between 1 and 20 muscle fibres were localised per motor unit within the electrode detection area, with up to 220 muscle fibres localised per recording, with overlapping motor unit territories. ConclusionsWe provide the first direct evidence that human motor units spatially overlap, as well as data related to the spatial arrangement of muscle fibres within a motor unit. SignificanceAs well as providing insights into normal human motor physiology, this technology could lead to faster and more accurate diagnosis in patients with neuromuscular diseases.

Dynamics of directional soluble wicking

Abstract

Effective Complex Properties for Three-Phase Elastic Fiber-Reinforced Composites with Different Unit Cells

The development of micromechanical models to predict the effective properties of multiphase composites is important for the design and optimization of new materials, as well as to improve our understanding about the structure–properties relationship. In this work, the two-scale asymptotic homogenization method (AHM) is implemented to calculate the out-of-plane effective complex-value properties of periodic three-phase elastic fiber-reinforced composites (FRCs) with parallelogram unit cells. Matrix and inclusions materials have complex-valued properties. Closed analytical expressions for the local problems and the out-of-plane shear effective coefficients are given. The solution of the homogenized local problems is found using potential theory. Numerical results are reported and comparisons with data reported in the literature are shown. Good agreements are obtained. In addition, the effects of fiber volume fractions and spatial fiber distribution on the complex effective elastic properties are analyzed. An analysis of the shear effective properties enhancement is also studied for three-phase FRCs.

Bacteria Adhesion of Textiles Influenced by Wettability and Pore Characteristics of Fibrous Substrates.

Bacteria adhesion on the surface is an initial step to create biofouling, which may lead to a severe infection of living organisms and humans. This study is concerned with investigating the textile properties including wettability, porosity, total pore volume, and pore size in association with bacteria adhesion. As model bacteria, Gram-negative, rod-shaped Escherichia coli and the Gram-positive, spherical-shaped Staphylococcus aureus were used to analyze the adhesion tendency. Electrospun webs made from polystyrene and poly(lactic acid) were used as substrates, with modification of wettability by the plasma process using either O2 or C4F8 gas. The pore and morphological characteristics of fibrous webs were analyzed by the capillary flow porometer and scanning electron microscopy. The substrate’s wettability appeared to be the primary factor influencing the cell adhesion, where the hydrophilic surface resulted in considerably higher adhesion. The pore volume and the pore size, rather than the porosity itself, were other important factors affecting the bacteria adherence and retention. In addition, the compact spatial distribution of fibers limited the cell intrusion into the pores, reducing the total amount of adherence. Thus, superhydrophobic textiles with the reduced total pore volume and smaller pore size would circumvent the adhesion. The findings of this study provide informative discussion on the characteristics of fibrous webs affecting the bacteria adhesion, which can be used as a fundamental design guide of anti-biofouling textiles.