3D-Printed Polycaprolactone-Based Containing Calcium Zirconium Silicate: Bioactive Scaffold for Accelerating Bone Regeneration.

There is an essential clinical need to develop rapid process scaffolds to repair bone defects. The current research presented the development of calcium zirconium silicate/polycaprolactone for bone tissue engineering utilising melt extrusion-based 3D printing. Calcium zirconium silicate (CZS) nanoparticles were added to polycaprolactone (PCL) porous scaffolds to enhance their biological and mechanical properties, while the resulting properties were studied extensively. No significant difference was found in the melting point of the samples, while the crystallisation temperature points of the samples containing bioceramic increased from 36.1 to 40.2 °C. Thermal degradation commenced around 350 °C for all materials. According to our results, increasing the CZS content from 0 to 40 wt.% (PC40) in porous scaffolds (porosity about 55-62%) improved the compressive strength from 2.8 to 10.9 MPa. Furthermore, apatite formation ability in SBF solution increased significantly by enhancing the CZS percentage. According to MTT test results, the viability of MG63 cells improved remarkably (~29%) in PC40 compared to pure PCL. These findings suggest that a 3D-printed PCL/CZS composite scaffold can be fabricated successfully and shows great potential as an implantable material for bone tissue engineering applications.

3D and 4D Printing of PETG-ABS-Fe3O4 Nanocomposites with Supreme Remotely Driven Magneto-Thermal Shape-Memory Performance.

This study introduces novel PETG-ABS-Fe3O4 nanocomposites that offer impressive 3D- and 4D-printing capabilities. These nanocomposites can be remotely stimulated through the application of a temperature-induced magnetic field. A direct granule-based FDM printer equipped with a pneumatic system to control the output melt flow is utilized to print the composites. This addresses challenges associated with using a high weight percentage of nanoparticles and the lack of control over geometry when producing precise and continuous filaments. SEM results showed that the interface of the matrix was smooth and uniform, and the increase in nanoparticles weakened the interface of the printed layers. The ultimate tensile strength (UTS) increased from 25.98 MPa for the pure PETG-ABS sample to 26.3 MPa and 27.05 MPa for the 10% and 15% Fe3O4 nanocomposites, respectively. This increase in tensile strength was accompanied by a decrease in elongation from 15.15% to 13.94% and 12.78%. The results of the shape-memory performance reveal that adding iron oxide not only enables indirect and remote recovery but also improves the shape-memory effect. Improving heat transfer and strengthening the elastic component can increase the rate and amount of shape recovery. Nanocomposites containing 20% iron oxide demonstrate superior shape-memory performance when subjected to direct heat stimulation and a magnetic field, despite exhibiting low print quality and poor tensile strength. Smart nanocomposites with magnetic remote-control capabilities provide opportunities for 4D printing in diverse industries, particularly in medicine, where rapid speed and remote control are essential for minimally invasive procedures.

Multiscale modeling of unfolding and bond dissociation of rubredoxin metalloprotein.

Mechanical properties of proteins that have a crucial effect on their operation. This study used a molecular dynamics simulation package to investigate rubredoxin unfolding on the atomic scale. Different simulation techniques were applied, and due to the dissociation of covalent/hydrogen bonds, this protein demonstrates several intermediate states in force-extension behavior. A conceptual model based on the cohesive finite element method was developed to consider the intermediate damages that occur during unfolding. This model is based on force-displacement curves derived from molecular dynamics results. The proposed conceptual model is designed to accurately identify bond rupture points and determine the associated forces. This is achieved by conducting a thorough comparison between molecular dynamics and cohesive finite element results. The utilization of a viscoelastic cohesive zone model allows for the consideration of loading rate effects. This rate-dependent model can be further developed and integrated into the multiscale modeling of large assemblies of metalloproteins, providing a comprehensive understanding of mechanical behavior while maintaining a reduced computational cost.

Enhancing the Thermal Performance of Shape Memory Polymers: Designing a Minichannel Structure.

This research proposes a numerical approach to improve the thermal performance of shape memory polymers (SMPs) while their mechanical properties remain intact. Sixteen different 3D minichannel structures were numerically designed to investigate the impact of embedded water flow in microchannel networks on the thermal response and shape recovery of SMPs. This work employs two approaches, each with different physics: approach A focuses on solid mechanics analysis and, accordingly, thermal analysis in solids without considering the fluid. approach B tackles solid and fluid mechanics analysis and thermal analysis in both solid and fluid subdomains, which inherently calls for fluid-structure coupling in a uniform procedure. Finally, the results of these two approaches are compared to predict the SMP's thermal and mechanical behavior. The structural designs are then analyzed in terms of their shape recovery speed, recovery ratio, and recovery parameters. The results indicate that isotropic structures thermally outperform their anisotropic counterparts, exhibiting improved thermal characteristics and faster shape recovery. Additionally, it was observed that polymeric structures with a low volume fraction of embedded branches thermally perform efficiently. The findings of this study predict that the geometrical angle between the main branch and sub-branches of SMP favorably impacts the enhancement of thermal characteristics of the structure, accelerating its shape recovery. Approach B accelerates the shape recovery rate in SMPs due to fluid flow and uniform heat transfer within the structures.

In silico optimization of aligned fiber electrodes for dielectric elastomer actuators.

Dielectric elastomer actuators (DEAs) exhibit fast actuation and high efficiencies, enabling applications in optics, wearable haptics, and insect-scale robotics. However, the non-uniformity and high sheet resistance of traditional soft electrodes based on nanomaterials limit the performance and operating frequency of the devices. In this work, we computationally investigate electrodes composed of arrays of stiff fiber electrodes. Aligning the fibers along one direction creates an electrode layer that exhibits zero stiffness in one direction and is predicted to possess high and uniform sheet resistance. A comprehensive parameter study of the fiber density and dielectric thickness reveals that the fiber density primary determines the electric field localization while the dielectric thickness primarily determines the unit cell stiffness. These trends identify an optimal condition for the actuation performance of the aligned electrode DEAs. This work demonstrates that deterministically designed electrodes composed of stiff materials could provide a new paradigm with the potential to surpass the performance of traditional soft planar electrodes.

Application of the Quality by Design Concept (QbD) in the Development of Hydrogel-Based Drug Delivery Systems.

Hydrogel-based drug delivery systems are of interest to researchers for many reasons, such as biocompatibility, high diversity, and the possibility of administration from different routes. Despite these advantages, there are challenges, such as controlling the drug release rate and their mechanical properties during the manufacturing of these systems. For this reason, there is a need for the production and development of such drug delivery systems with a scientific strategy. For this reason, the quality by design (QbD) approach is used for the development of drug delivery systems. This approach, by identifying the most effective factors in the manufacturing of pharmaceutical products and controlling them, results in a product with the desired quality with the least number of errors. In this review article, an attempt is made to discuss the application and method of applying this approach in the development of hydrogel-based drug delivery systems. So that for the development and production of these systems, according to the type of drug delivery system, what target characteristics should be considered (QTPP) and what factors, such as material properties (CMA) or process parameters (CPP), should be taken into account to reach the critical quality attributes of the product (CQA).

Tablet Geometry Effect on the Drug Release Profile from a Hydrogel-Based Drug Delivery System.

In order to achieve the optimal level of effectiveness and safety of drugs, it is necessary to control the drug release rate. Therefore, it is important to discover the factors affecting release profile from a drug delivery system. Geometry is one of these effective factors for a tablet-shaped drug delivery system. In this study, an attempt has been made to answer a general question of how the geometry of a tablet can affect the drug release profile. For this purpose, the drug release process of theophylline from two hundred HPMC-based tablets, which are categorized into eight groups of common geometries in the production of oral tablets, was simulated using finite element analysis. The analysis of the results of these simulations was carried out using statistical methods including partial least squares regression and ANOVA tests. The results showed that it is possible to predict the drug release profile by knowing the geometry type and dimensions of a tablet without performing numerous dissolution tests. Another result was that, although in many previous studies the difference in the drug release profile from several tablets with different geometries was interpreted only by variables related to the surface, the results showed that regardless of the type of geometry and its dimensions, it is not possible to have an accurate prediction of the drug release profile. Also, the results showed that without any change in the dose of the drug and the ingredients of the tablet and only because of the difference in geometry type, the tablets significantly differ in release profile. This occurred in such a way that, for example, the release time of the entire drug mass from two tablets with the same mass and materials but different geometries can be different by about seven times.

Unraveling the effect of hydrogenation on the mechanical properties of coiled carbon nanotubes: a molecular dynamics study.

With the increase in the utilization of nanomaterials in daily life, carbon nanostructures have received the attention of many researchers due to their special physical, chemical, and electrical properties. Chemical functionalization is one of the common methods to improve the thermomechanical properties of carbon nanomaterials used for specific applications. In this research, the effect of functionalization with hydrogen atoms on the mechanical properties of coiled carbon nanotubes with different geometrical dimensions has been examined. In addition, the mechanical properties of CCNTs with random and patterned distributions of hydrogen atoms have been investigated. The random distribution of hydrogen atoms up to 10% causes a sharp decrease in the mechanical properties of CCNTs such as the Young's modulus and spring constant, and increasing the percentage of H-coverage by more than 10% does not cause a significant effect on the mentioned properties. Also, unlike other carbon nanostructures, the stretchability of most CCNTs increases by increasing the percentage of hydrogenation beyond 30 percent. On investigating the effect of temperature on the properties of hydrogenated CCNTs, the temperature increase does not affect the Young's modulus and spring constant, and also there is no explicit relationship between their stretchability and temperature. Exploring the mechanical behavior of hydrogen-functionalized CCNTs via the tensile test and also how their mechanical properties change compared to those of pure CCNTs can help researchers in many applications.

Design of Refractory Alloys for Desired Thermal Conductivity via AI-Assisted In-Silico Microstructure Realization.

A computational methodology based on supervised machine learning (ML) is described for characterizing and designing anisotropic refractory composite alloys with desired thermal conductivities (TCs). The structural design variables are parameters of our fast computational microstructure generator, which were linked to the physical properties. Based on the Sobol sequence, a sufficiently large dataset of artificial microstructures with a fixed volume fraction (VF) was created. The TCs were calculated using our previously developed fast Fourier transform (FFT) homogenization approach. The resulting dataset was used to train our optimal autoencoder, establishing the intricate links between the material's structure and properties. Specifically, the trained ML model's inverse design of tungsten-30% (VF) copper with desired TCs was investigated. According to our case studies, our computational model accurately predicts TCs based on two perpendicular cut-section images of the experimental microstructures. The approach can be expanded to the robust inverse design of other material systems based on the target TCs.

A New Strategy for Achieving Shape Memory Effects in 4D Printed Two-Layer Composite Structures.

In this study, a new strategy and design for achieving a shape memory effect (SME) and 4D printed two-layer composite structures is unveiled, thanks to fused deposition modeling (FDM) biomaterial printing of commercial filaments, which do not have an SME. We used ABS and PCL as two well-known thermoplastics, and TPU as elastomer filaments that were printed in a two-layer structure. The thermoplastic layer plays the role of constraint for the elastomeric layer. A rubber-to-glass transition of the thermoplastic layer acts as a switching phenomenon that provides the capability of stabilizing the temporary shape, as well as storing the deformation stress for the subsequent recovery of the permanent shape by phase changing the thermoplastic layer in the opposite direction. The results show that ABS-TPU had fixity and recovery ratios above 90%. The PCL-TPU composite structure also demonstrated complete recovery, but its fixity was 77.42%. The difference in the SME of the two composite structures is related to the transition for each thermoplastic and programming temperature. Additionally, in the early cycles, the shape-memory performance decreased, and in the fourth and fifth cycles, it almost stabilized. The scanning electron microscopy (SEM) photographs illustrated superior interfacial bonding and part integrity in the case of multi-material 3D printing.

Silk Fibroin Hydrogel Reinforced With Magnetic Nanoparticles as an Intelligent Drug Delivery System for Sustained Drug Release.

Due to the well-known biocompatibility, tunable biodegradability, and mechanical properties, silk fibroin hydrogel is an exciting material for localized drug delivery systems to decrease the therapy cost, decrease the negative side effects, and increase the efficiency of chemotherapy. However, the lack of remote stimuli response and active drug release behavior has yet to be analyzed comparatively. In this study, we developed magnetic silk fibroin (SF) hydrogel samples through the facile blending method, loaded with doxorubicin hydrochloride (DOX) and incorporated with different concentrations of iron oxide nanoparticles (IONPs), to investigate the presumable ability of controlled and sustained drug release under the various external magnetic field (EMF). The morphology and rheological properties of SF hydrogel and magnetic SF hydrogel were compared through FESEM images and rheometer analysis. Here, we demonstrated that adding magnetic nanoparticles (MNPs) into SFH decreased the complex viscosity and provided a denser porosity with a bigger pore size matrix structure, which allowed the drug to be released faster in the absence of an EMF. Release kinetic studies show that magnetic SF hydrogel could achieve controlled release of DOX in the presence of an EMF. Furthermore, the drug release from magnetic SF hydrogel decreased in the presence of a static magnetic field (SMF) and an alternating magnetic field (AMF), and the release rate decreased even more with the higher MNPs concentration and magnetic field strength. Subsequently, Wilms' tumor and human fibroblast cells were cultured with almost the same concentration of DOX released in different periods, and cell viability was investigated using MTT assay. MTT results indicated that the Wilms' tumor cells were more resistant to DOX than the human fibroblasts, and the IC50 values were calculated at 1.82 ± 0.001 and 2.73 ± 0.004 (µg/ml) for human fibroblasts and Wilms' tumor cells, respectively. Wilms' tumor cells showed drug resistance in a higher DOX concentration, indicating the importance of controlled drug delivery. These findings suggest that the developed magnetic SFH loaded with DOX holds excellent potential for intelligent drug delivery systems with noninvasive injection and remotely controlled abilities.

Decisive structural elements in water and ion permeation through mechanosensitive channels of large conductance: insights from molecular dynamics simulation.

In this paper, a series of equilibrium molecular dynamics simulations (EMD), steered molecular dynamics (SMD), and computational electrophysiology methods are carried out to explore water and ion permeation through mechanosensitive channels of large conductance (MscL). This research aims to identify the pore-lining side chains of the channel in different conformations of MscL homologs by analyzing the pore size. The distribution of permeating water dipole angles through the pore domains enclosed by VAL21 and GLU104 demonstrated that water molecules are oriented toward the charged oxygen headgroups of GLU104 from their hydrogen atoms to retain this interaction in a stabilized fashion. Although, this behavior was not perceived for VAL21. Numerical assessments of the secondary structure clarified that, during the ion permeation, in addition to the secondary structure alterations, the structure of Tb-MscL would also undergo significant conformational changes. It was elucidated that VAL21, GLU104, and water molecules accomplish a fundamental task in ion permeation. The mentioned residues hinder ion permeation so that the pulling SMD force is increased remarkably when the ions permeate through the domains enclosed by VAL21 and GLU102. The hydration level and potassium diffusivity in the hydrophobic gate of the transmembrane domain were promoted by applying the external electric field. Furthermore, the implementation of an external electric field altered the distribution pattern for potassium ions in the system while intensifying the accumulation of Cl- in the vicinity of ARG11 and ARG98.

Multiphysics modeling and experiments on ultrasound-triggered drug delivery from silk fibroin hydrogel for Wilms tumor.

In this study, silk fibroin hydrogel is employed as a carrier for vincristine and ultrasound as a method to accelerate the drug release. The Acoustic, deformation, swelling, and diffusion fields are coupled in a multi-physics model to optimize the drug delivery. A transient acoustic structure model and a chemically controlled mechanism are implemented, while a coupled model of deformation and diffusion takes the impact of mechanical forces into account. An evaluation of the model is made through experiments. To monitor the drug release rate over 40 days following injection of silk hydrogel syringes containing vincristine, they were triggered by ultrasound in some selected time intervals. Drug release rates were determined using different power intensities and induction times. Computed simulation results and laboratory experiments revealed that ultrasound could cause a significant improvement in drug release rate, with an increase of up to 10 times over a release without ultrasound stimulation. By increasing the ultrasound power and induction time up to their peak value, the drug release rate rises and drops then. Predictions of the drug release rate by the model were in good agreement with those observed in experiments. This makes the model a valuable tool for potential predictions. Results showed that the ultrasound triggers the increased cell death rates, but the Wilms tumor cells were resistant to higher concentrations of released drugs.

Computational analysis of vincristine loaded silk fibroin hydrogel for sustained drug delivery applications: Multiphysics modeling and experiments.

In this paper, silk fibroin hydrogel is used as a drug carrier for vincristine. To optimize drug delivery, a multi-physics model is proposed that couples the deformation and diffusion fields. We applied inverse analysis and general continuum mechanics to define material parameters and mechanical properties. To examine the mass transport and chemical behavior, an affinity-based diffusion and degradation of a drug-loaded polymer matrix is employed. Some experiments are carried out to examine the capability of the presented model. After preparing the vincristine loaded silk hydrogel syringes, they were injected into PBS and enzyme solutions to monitor the drug release rate for 40 days. Obtained results from the computational simulation and laboratory tests showed that the silk fibroin hydrogel was deswelled after about 40 days in enzyme solution. Degradation led to faster and higher doses of vincristine drug release in comparison to the case of PBS solution. Results revealed that more than 80% of the drug was released in the first 5 days in the enzyme solution, but in PBS solution only 10% of the drug was released during 40 days. The model predictions of deswelling behavior and drug release rate were in good agreement with those of experimental results. Therefore, it can be employed as a reliable tool for further predictions.

A Microfabrication Method of PCL Scaffolds for Tissue Engineering by Simultaneous Two PDMS Molds Replication.

Not very far away, "tissue engineering" will become one of the most important branches of medical science for curing many types of diseases. This branch needs the cooperation of a wide range of sciences like medicine, chemistry, cellular biology, and genetic and mechanical engineering. Different parameters affect the final produced tissue, but the most important one is the quality and biocompatibility of the scaffold with the desired tissue which can provide the functionality of "native ECM" as well. The quality of the scaffold is directly dependent on its materials, design, and method of fabrication. As to the design and fabrication, there are two main categories: (a) random microporosity such as phase separation, electrospinning, and fused deposition modeling (3D printing) and (b) designed microporosity mostly achievable by stereo lithography and soft lithography. The method of fabrication implemented in this research is a novel method in soft lithography employing a type of "replica molding" with one pair of polydimethylsiloxane (PDMS) molds in contrast to traditional replica molding with just one single mold. In this operation, the solution of polycaprolactone in chloroform is initially prepared, and one droplet of the solution is placed between the molds while a preset pressure is applied to maintain the molds tightly together during the solidification of the polymer layer and vaporization of the solvent. Thus, a perfect warp and woof pattern is created. In this research, it has been approved that this is a feasible method for creating complex patterns and simple straight fiber patterns with different spacings and pore sizes. Cell attachment and migration was studied to find the optimum pore size. It was shown that the small pore size improves the cells' adhesion while reducing cell migration capability within the scaffold.

A novel machine learning based computational framework for homogenization of heterogeneous soft materials: application to liver tissue.

Real-time simulation of organs increases comfort and safety for patients during the surgery. Proper generalized decomposition (PGD) is an efficient numerical method with coordinate errors below 1 mm and response time below 0.1 s that can be used for simulated surgery. For input of this approach, nonlinear mechanical properties of each segment of the liver need to be calculated based on the geometries of the patient's liver extracted using medical imaging techniques. In this research work, a map of the mechanical properties of the liver tissue has been estimated with a novel combined method of the finite element (FE) optimization. Due to the existence of major-size vessels in the liver that makes the surrounding tissue anisotropic, the simulation of hyperelastic material with two different sections is computationally expensive. Thus, a homogenized, anisotropic, and hyperelastic model with the nearest response to the real heterogeneous model was developed and presented. Because of various possibilities of the vessel orientation, position, and size, homogenization has been carried out for adequate samples of heterogeneous models to train artificial neural networks (ANNs) as machine learning tools. Then, an unknown sample of heterogeneous material was categorized and mapped to its homogenized material parameters with the trained networks for the fast and low-cost generalization of our combined FE optimization method. The results showed the efficiency of the proposed novel machine learning based technique for the prediction of effective material properties of unknown heterogeneous tissues.

How to characterize interfacial load transfer in spiral carbon-based nanostructure-reinforced nanocomposites: is this a geometry-dependent process?

There is a great deal of attention given to spiral carbon-based nanostructures (SCBNs) because of their unique mechanical, thermal and electrical properties along with fascinating morphology. Dispersing SCBNs inside a polymer matrix leads to extraordinary properties of nanocomposites in diverse fields. However, the role of the interfacial mechanical properties of these nanocomposites remains unknown. Here, using molecular dynamics simulations, the characteristics of interfacial load transfer of SCBN-polyethylene nanocomposites are explored. Considering the geometric characteristics of SCBNs, new insight into the separation behavior of nanoparticles in normal and sliding modes is addressed. Interestingly, the results show that the maximum force and the separation energy of the SCBNs are much larger than those of graphene because of interlocking of the coils and polymer. The heavy influence of changes in the geometric characteristics of SCBNs on the separation behavior is observed. Pullout tests reveal that the influence of parameters such as the length and number of polyethylene chains, temperature, and functionalization of the SCBNs on the interfacial mechanical properties is also significant. This study sheds new light in understanding the crucial effect of the interaction of SCBNs with polymer chains on the interfacial mechanical properties, which can lead to better performance of nanocomposites.

Insights into interphase thickness characterization for graphene/epoxy nanocomposites: a molecular dynamics simulation.

This work presents a molecular dynamics simulation study on the interfacial characterization of graphene/epoxy nanocomposites. In polymeric nanocomposites, the thermo-mechanical properties of a system strongly depend on the characteristics of the interphase region between the matrix and the inclusions. The first step in the characterization of this interphase is to distinguish its border limit (i.e., the interphase thickness). Here, we present a methodology to systematically quantify the interphase thickness based on analyzing the variation of the local mass density profile. To this end, three functions (average accumulated mass density, accumulated standard deviation (ASD) and its first derivative) are successively applied on the local mass density profile. Using this procedure, the interphase limit can be easily detected regardless of the oscillatory nature of the local mass density. The effect of the epoxy crosslinking density and number of graphene layers on the interphase thickness is then investigated, and the results are analyzed by studying the interaction energies, polymer dynamics and distribution quality of reacted and unreacted components, as well as conformational changes of the polymer chains in the interphase region. The results reveal that the crosslinking density is the most influential parameter on the interphase thickness: the higher the crosslinking degree, the thicker the interphase region. To a lower extent, the interaction energy has also an effect on the interphase thickness since there is an inverse relationship between the interaction energy and the crosslinking density in our case study. Overall, the reported findings highlight useful insights into the detection and properties of the interphase region in thermoset composites.

Hydrogenation-controlled mechanical properties in graphene helicoids: exceptional distribution-dependent behavior.

The ever-increasing development of nanotechnology has led to the creation of nanomaterials with spiral geometry such as graphene helicoids (GHs) that are mainly used for mechanical, chemical, and electrical applications. Controlling the properties of these nanomaterials with geometric changes and functionalizations is the most common and accessible task. However, functionalization leads to specific applications. In the present research, using molecular dynamics simulation, mechanical properties of pristine and functionalized GHs have been investigated for various geometries and H-coverages. Also, hydrogenation has been performed for patterned and random distributions. The random H-coverage up to 10 percent results in a decrease in the Young's modulus. Also, by increasing the percentage of H-coverage beyond 10 percent, no conspicuous alteration is observed in the Young's modulus, while the ultimate strain is reduced. By examining the effect of temperature rise on the properties of pristine and functionalized GHs, a sharp decrease in the strain range is observed for both. In addition, it has been shown that the toughness is severely reduced by decreasing the external and internal radii of pristine and functionalized GHs. Investigating the mechanical properties of pristine and hydrogenated GHs leads to better control of the mechanical properties of these nanoparticles and optimal efficiency in nano-scale devices.

Homogenization of heterogeneous brain tissue under quasi-static loading: a visco-hyperelastic model of a 3D RVE.

Researches, in the recent years, reveal the utmost importance of brain tissue assessment regarding its mechanical properties, especially for automatic robotic tools, surgical robots and helmet producing. For this reason, experimental and computational investigation of the brain behavior under different conditions seems crucial. However, experiments do not normally show the distribution of stress and injury in microscopic scale, and due to various factors are costly. Development of micromechanical methods, which could predict the brain behavior more appropriately, could highly be helpful in reducing these costs. This study presents computational analysis of heterogeneous part of the brain tissue under quasi-static loading. Heterogeneity is created by irregular distribution of neurons in a representative volume element (RVE). Considering time-dependent behavior of the tissue, a visco-hyperelastic constitutive model is developed to predict the RVE behavior more realistically. The RVE is studied in different loads and load rates; 1, 2, 3, 10 and 15% strain load are applied at 0.03 and 0.2 s on the RVE as tensile and shear loads. Due to complexity in geometry, self-consistent approximation method is employed to increase the volume fraction of neurons and analyze RVE behavior in various NVFs. The results show increasing the load rate leads to a raise in the maximum stress that indicates the tissue is more vulnerable at higher rates. Moreover, stiffness of the tissue is enhanced in higher NVFs. Additionally, it is found that axons undergo higher stresses; hence, they are more sensitive in accidents which lead to axonal death and would cause TBI and DAI.