Porosity Factor Research Articles

Effects of electroosmosis and entropy generation on a particulate-fluid suspension through peristaltic motion of Prandtl fluid: a numerical investigation

In thermodynamics, engineering, and environmental science, entropy generation are useful for comprehending complicated phenomena like heat transfer and fluid dynamics, analysing irreversibility, and optimising energy systems. Particulate-fluid flow with chemical reactions are employed in pharmaceutical manufacturing, catalytic converters, and combustion processes. This study provides insights into mass and thermal transmission analysis and influences of electroosmosis on flow of the Prandtl fluid in peristaltic micro-channel. The flow is also investigated considering the multiphase phenomenon influenced by chemical reaction. The flow problem modelling utilises lubrication theory with lengthy wavelengths and creeping flow assumptions. Furthermore, using Debye linearisation, the Poisson equation is simplified. The consequent structures for coupled ordinary differential equations have been handled by numerical solutions computed by using a highly efficient shooting technique with the help of NDSolve tool in Mathematica. It is concluded graphically that the profile of velocity declines with the increasing variation of porosity factor while enhances with the rise in Helmholtz-Smoluchowski velocity parameter. Thermal distribution increases with higher values of solid particle concentration and porous surface. It is evident that the concentration profile diminishes with the porosity coefficient but rises with the Helmholtz-Smoluchowski velocity factor.

Dynamic analysis and structural optimization study of porous FG-MEE microplate with impact and hygrothermal loads

Functionally graded magnetic-electro-elastic (FG-MEE) materials with their remarkable multifunctional energy conversion capabilities are widely used in several components such as biosensors, micro imaging devices, micro robotics and micro actuators. This paper presents vibration analysis and optimization studies of FG-MEE microplate resting on nonlinear visco-elastic foundation under hygrothermal loads. The equations of motion for a two-dimensional (2D) functionally graded MEE porous microplate are first obtained using four variable higher order shear deformation plate theory in conjunction with the modified couple stress model. By considering the material grading and porosity distribution functions, the dynamic formulation of the microplate is derived using variational approach. The general analytical procedure is employed to obtain the stiffness and mass matrices for different boundary conditions. Initially, the model is validated using free vibration analysis results. Further, dynamic analysis of FG-MEE microplate subjected to different impulse excitations is conducted to obtain the response amplitudes. Parametric studies are performed to investigate the effects of grading indices, porosity factor, length-scale parameter, foundation parameters, multi-physical loads on the fundamental frequency and dynamic response. Using design of experiments, it is noticed that five factors only have vital influence on the dynamic response of the microplate. Accordingly, a multilayer perceptron neural network model is trained with parametric data acting as a surrogate model. Finally, the optimal parameters are predicted to improve the stability by solving a constrained optimization problem using firefly optimization scheme with the trained neural network model. From optimization results it is noticed that the fundamental frequency has improved by 63.66% with enormous saving of computational time using the neural network surrogate model.

Gyrotactic micro-organism dusty fluid flow over an extended surface

The conventional magnetohydrodynamics (MHD) equations are modeled for the dusty fluid flow past a stretching sheet embedded in a porous medium. These equations are coupled with motile microorganisms, temperature and concentration equations for both fluid and dust particles dynamical equations. The two-phase dusty fluid flow is subjected to magnetic effect, porosity factor, 2nd order chemical reaction, Brownian diffusion, thermophoretic effect and Arrhenius activation energy. An appropriate transformation is used to reset the system of partial differential equations (PDEs) into non-linear system of ODEs (ordinary differential equations). The flow equations are numerically solved by using the parametric continuation method (PCM). For validity of the applied methodology, the results are compared using numerical and graphical results of Matlab built-in package “BVP4c”. Both results show accuracy up to four decimal places. Furthermore, from graphical results, it can be noticed that the fluid particles interaction parameter causes an attractive force among fluid particles which prevents the dust particles phase flow. This technique can be used for biological separations, filters, fluid dust separators and crude oil purification.

Experimental Extraction of Carbon-Binder-Domain Characteristic for Simulation Purposes

In the last years, there has been a substantial push in the lithium ion battery field to develop accurate electrochemical models that are capable of reproducing the response of experimental electrodes. There are myriad applications and uses of these models that range from material design to cell integration, providing means for a rational design of electrodes and cells, as well as diagnosis and prognosis tools during its life.1 Among all the models, electrode scale 3D mesoscopic electrochemical modeling approaches have gained substantial relevance due to the possibility of linking the electrochemical model with manufacturing models, creating a unique simulation pipeline that gathers all information from slurry to cell. 2 These 3D mesoscopic models count with an additional advantage compared to simpler models such as the p2D approaches, and it is the fact they account better for the microstructure, which is known to be very determinant in the electrode performance.3 These models usually consider three main domains inside the electrode, the active material, the carbon-binder domain and the pores. While the active material and its pore network have been better studied and can be more easily obtained by tomography, the carbon-binder domain and its distribution in the microstructure is harder to study 4 but extremely determinant in the properties of the electrodes.5 The carbon binder domain properties, such as its conductivity, porosity and tortuosity factor will determine the heterogeneity of the reactions happening in the electrodes. 5 However, until now, there is a lack of experimental approaches able to characterize the porosity and tortuosity factor of the carbon binder domain, and this is precisely the topic of this work. We propose a methodology to extract the characteristics of the carbon binder domain, namely its porosity and tortuosity factor, by combining electrode manufacturing, characterization and computational simulations.Our methodology is based on the study of electrochemical impedance data over a wide matrix of cases in a systematic way using, as a proof of concept, well-characterized NMC electrodes and relating it with electrode-level simulation results.The proposed methodology is completely general and independent of the cell chemistry, and not only provides a tool to better understand and simulate the behaviour of the carbon binder domain, but it is also demonstrated how it can be used to obtain experimental data that was not easily available before. A. A. Franco et al., Chem. Rev., 119, 4569–4627 (2019). C. Liu, T. Lombardo, J. Xu, A. C. Ngandjong, and A. A. Franco, Energy Storage Materials, 54, 156–163 (2023).T.-T. Nguyen et al., npj Comput Mater, 6, 123 (2020). J. Landesfeind, M. Ebner, A. Eldiven, V. Wood, and H. A. Gasteiger, J. Electrochem. Soc., 165, A469–A476 (2018). M. Chouchane and A. A. Franco, Energy Storage Materials, 47, 649–655 (2022).

Time-Dependent Deep Learning Manufacturing Process Model for Battery Electrode Microstructure Prediction

Manufacturing process of Lithium-ion battery electrode has a direct impact on the resulting practical properties of the cell such as durability, safety and overall performance. In this scenario, together with experimental efforts to understand the correlation between manufacturing process parameters and final overall electrode performance, computational tools have shown the potential to produce insights on the manufacturing properties interdependencies. The ARTISTIC initiative [1] pioneered the development of a series of computational 3D-resolved physics-based models describing each step of the manufacturing process following a sequential pattern, going from the slurry phase, through the drying phase, to produce a calendered electrode. Such physics-based models have the capability to predict how manufacturing parameters impact the electrode microstructure. At the end of each phase the output microstructure is carefully validated with experimental data, assuring that the model is in good agreement with real properties. For the calendering phase, the Discrete Element Method (DEM) is used for the model simulation.One of the main limitations of the DEM simulations is their high computational cost, preventing their direct application in electrode optimization loops. In this matter, and given the great amount of already validated data that was produced in our previous works by using the ARTISTIC model, we take one step forward in the development of a new generation model employing machine learning (ML) techniques to accelerate the physics-based simulations but keeping their accuracy. In this presentation we report a novel time-dependent deep learning (DL) model of the battery electrodes manufacturing process. The DL model is demonstrated for calendering of nickel manganese cobalt (NMC111) electrodes, and trained with time-series data arising from physics-based DEM simulations coming from the ARTISTIC physics-based modeling pipeline [2]. The innovative DL model presented in this work can predict an electrode microstructure evolution over time at a given compression degree during calendering and provide the associated final relaxed electrode microstructure. Here we used the formulation of 96% AM and 4% CBD as a proof of concept.The DL model predictions are validated by comparing data evaluation metrics (e.g., mean square error (MSE) and R2 score) and electrode functional metrics (contact surface area, porosity, diffusivity, and tortuosity factor), showing very good accuracy with respect to the DEM simulations (all the properties are above 90% accurate when compared to target data). Regarding transport properties and given the high accuracy of the DL model to predict tortuosity factor and porosity values we can conclude that the model correctly predicts the effect of the calendering on the transport properties since tortuosity factor and porosity has a high impact on the effective transport properties of Li-ions in the electrolyte. The DL model can remarkably capture the elastic recovery of the electrode upon compression (spring-back phenomenon) and the main 3D electrode microstructure features without using the functional descriptors during its training. In terms of computational cost, the DL model performs a timestep in 15 seconds (wall time) while the DEM model performs an equivalent time step in ∼47 minutes (wall time). Given the DL model significant lower computational cost than the DEM simulations, it paves the way toward quasi-real-time optimization loops of the 3D electrode architecture predicting the calendering conditions to adopt in order to obtain the desired electrode performance. Moreover, we consider that our model has tremendous potential to grow in several aspects such as accuracy and predictability. Therefore, our incoming efforts will be focused on continuing the development of the model and testing it for other formulations and in other manufacturing stages.[1] ARTISTIC Project website. https://www.erc-artistic.eu.[2] D. E. Galvez-Aranda, T. L. Dinh, U. Vijay, F. M. Zanotto, A. A. Franco, Time-Dependent Deep Learning Manufacturing Process Model for Battery Electrode Microstructure Prediction. Adv. Energy Mater. 2024, 2400376. https://doi.org/10.1002/aenm.202400376 Figure 1

On the dispersion of bulk wave in hygrothermally affected poroelastic gymnastics beams based on refined higher-order shear deformation theory during athlete training

Poroelastic materials have gained prominence due to their beneficial characteristics, prompting the authors to investigate their behavior in wave propagation. Additionally, the balance beam used in gymnastics is a classic example of a beam structure. It is designed to support the weight of the gymnast while providing stability and strength. This paper focuses on analyzing wave dispersion behavior in a hygrothermally excited poroelastic gymnastics beam. It is considered that the beam is made of a composition of Alumina and Aluminum as ceramic and metallic phases, respectively. Initially, the basic characteristics are determined using an improved power-law homogenization scheme. Subsequently, a poroelastic beam is modeled based on a refined higher-order shear deformation theory, and based on it and Hamilton’s principle, the kinetic relations are obtained. The obtained governing equations are then solved through analytical schemes using harmonic functions, and the outcomes are presented. These results are afterward verified through comprehensive comparisons with existing literature. Furthermore, this paper findings shows that the phase velocity and wave frequency of the beam are influenced by gradient parameters, porosity, and environmental factors like temperature and humidity to gain further insights.

A Hybrid Approach of Buongiorno's Law and Darcy–Forchheimer Theory Using Artificial Neural Networks: Modeling Convective Transport in Al2O3‐EO Mono‐Nanofluid Around a Riga Wedge in Porous Medium

ABSTRACTThe inspiration for this study originates from a recognized research gap within the broader collection of studies on nanofluids, with a specific focus on their interactions with different surfaces and boundary conditions (BCs). The primary purpose of this research is to use an artificial neural network to examine the combination of Alumina‐Engine oil‐based nanofluid flow subject to electro‐magnetohydrodynamic effects, within a porous medium, and over a stretching surface with an impermeable structure under convective BCs. The flow model incorporates Thermophoresis and Brownian motion directly from Buongiorno's model. Accounting for the porous medium's effect, the model integrates the Forchheimer number (depicting local inertia) and the porosity factor developed in response to the presence of the porous medium. The conversion of governing equations into non‐linear ordinary differential systems is achieved by implementing transformations. A highly non‐linear ordinary differential system's final system is solved using a numerical scheme (Runge–Kutta fourth‐order). Findings indicate that the porosity factor positively impacts the skin friction and the momentum boundary layer. The influence suggests an increment in the frictional force and a decline in the velocity profile. The volume fraction, Prandtl number, and magnetic number significantly impact the flow profiles. The skin friction data is tabulated with some physical justifications.

A Higher-Order Theory for Nonlinear Dynamic of an FG Porous Piezoelectric Microtube Exposed to a Periodic Load

This paper investigates the nonlinear dynamic deflection, natural frequency, and wave propagation in functionally graded (FG) porous piezoelectric microscale tubes under periodic load, hygrothermal conditions, and an external electric field. The piezoelectric material used to make the smart microtubes has pores that may be smoothly changed or uniformly distributed over the tube wall. Here, three types of porosity distribution are taken into consideration. The nonlinear motion equations are constructed using a novel shear deformation beam theory and the modified couple stress theory (MCST). The nonlinear motion equations are solved using the fourth-order Runge–Kutta technique and the Galerkin approach. The effects of various geometric parameters, porosity distribution type, porosity factor, periodic load amplitude and frequency, material length scale parameter, moisture, and temperature on the nonlinear dynamic deflection, natural frequency, and wave frequency of FG porous piezoelectric microtubes are explored through a number of parametric investigations.

Steady and periodical heat-mass transfer behavior of mixed convection nanofluid with reduced gravity, radiation and activation energy effects

Significance of this study is to explore Arrhenius activation energy, microgravity, chemical reaction and porous medium effects on Darcy nanofluid over a stretching sheet with nonlinear thermal radiations. Purpose of this study is to enhance the lubrication efficiency, excessive heat reduction, surface roughness and tool wear in drilling, milling, grinding and cutting tools of machining procedures. Use of Brownian and thermophoretic nanoparticles in base liquid is very effective to reduce friction in tool-chip and tool-work interfaces, tool life ability and cooling ability through nanofluid minimum quantity lubrication in machining operations. A refined mathematical model is solved using dimensionless variables; oscillatory stokes conditions and primitive variables. The implicit form of finite difference method is applied for numerical results using Gaussian elimination approach. The numerical and physical outputs are secured using Gaussian elimination methodology in FORTRAN tool and TecPlot-360. Graphs are displayed by using numerical data in Tecplot-360 program. Impact of activation energy (AE), solar radiation (Rd), microgravity (RG), porosity (Ω), thermophoresis (NT), chemical reaction (Kr), Prandtl (Pr) and Schmidt factor (Sc) on oscillating frequency of heat and mass transport is depicted. Amplitude in fluid velocity, surface temperature and volume of concentration is enhanced as radiation, microgravity and activation energy increases. It is noted that the increasing amplitude in fluid velocity, temperature and fluid concentration is found with activation energy, radiation and buoyancy force effects. Steady behavior of skin friction and mass transport decreases as Darcy porous medium and reaction rate decreases. Frequency of oscillating skin friction and oscillating heat transfer increases as Schmidt and Prandtl coefficient increases. Fluctuations and amplitude in the frequency of heat and mass transport enhances as thermophoretic nanoparticle enhances.

Critical lateral‐torsional buckling loads of porous orthotropic rectangular beams containing warping effects

AbstractDue to their elemental porosity, porous materials display individual mechanical properties, rendering them essential components in various engineering applications. So, this study investigates the lateral‐torsional buckling (LTB) sensitivity of porous orthotropic rectangular beams subjected to a concentrated load, including the warping effects. Porosity‐dependent material properties vary along the beam's height via four different porosity distribution patterns. The constitutive equations are obtained using the principle of virtual work based on the classical beam theory containing the warping effect, and the governing equations are solved by performing the Ritz method to obtain the critical LTB load. The porous orthotropic beams and the warping effects add complexity to the analysis. So, the influence of some parameters, such as porosity coefficients, porosity distribution patterns, orthotropy, height‐to‐width ratio, slenderness ratio, and warping coefficients, on the LTB characteristics of porous orthotropic rectangular beams are investigated in detail. The novelty of this study lies in its comprehensive LTB analysis of orthotropic rectangular beams under specific effects, such as porosity and warping factors. The findings offer valuable insights into the LTB characteristics of orthotropic porous beams, contributing to an improved comprehension of their structural behavior and facilitating the design of engineering structures. This study contributes to the composite materials and structural stability analysis, offering potential applications in many engineering domains.

Investigations on the leak resistance performance and the difference mechanism of composite materials under several typical curing processes

This study aims to investigate the leak resistance of carbon fiber composite products formed by various typical curing processes. Firstly, the leak rates of specimens produced through different curing methods were measured, and the defect were statistically analyzed. After that, the simulation approach was applied to numerically study the impact of these defect characteristics on leak rates was examined from the three factors of porosity, void distribution and void morphology, specimen with prefabricated defects was prepared, and its leak performance were tested to validate the simulation results. Finally, the differences in leak resistance among specimens under different curing processes were analyzed from the perspective of curing defects, and specific defect characteristics contributing to enhanced leak resistance were identified.

Hydrothermal dynamics of darcy Forchheimer ternary hybrid nanofluid flow past bended surface with active—passive controls

This dynamic study investigates hydrothermal and rheological properties of two-dimensional ternary nanofluid flow by using active passive control mechanisms on nanoparticles navigating a porous curved surface. The working fluid contains nanometer sized spherical shaped particles of three different metals [Formula: see text], which are homogenously and stably dispersed in liquid dihydrogen monoxide as host fluid. A strong magnetic force of strength [Formula: see text] acts along radial direction of curved surface. Thermal radiation term in heat equation suggests a strong heat source in the vicinity. This unique amalgamation, which has not been addressed so far by any researcher incorporates Brownian motion and thermophoresis to construct the hydrothermal framework. The passive control of nanoparticles on surface influenced by Brownian movement and thermophoresis, which is considered more realistic in comparison to constant concentration hypothesis (active control), have been taken into account. The nanoliquid flow is governed by system of Partial Differential Equations (PDEs) that cater thermophysical properties of nanoparticles. After simplifying the system to Ordinary Differential Equations (ODEs) using suitable transform, it is solved numerically using bvp4c in MATLAB 2023a. The obtained graphical and numerical results are discussed in detail to explain physics of observed thermo-rheological behaviour and mass diffusivity. The study reveals that curvature and porosity factors have opposite effects on velocity profile. All involved parameters augment temperature profile for both active and passive controls except curvature factors. In all the cases, active control assures higher temperature over passive control. Dual consequences have been noted for active and passive controls for concentration panels in most cases. Nusselt number is found increasing for increasing nanoparticles concentration with ternary nanofluid having maximum heat transfer rate. This study significantly contributes to the knowledge of ternary nanofluidic thermo-rheological analysis on curved stretching surface having numerous applications in mechanical, thermal and industrial domains. It also offers insights for developing advanced coatings and materials in industrial and engineering context.

3D microstructure reconstruction and characterization of porous materials using a cross-sectional SEM image and deep learning

Accurate assessment of the three-dimensional (3D) pore characteristics within porous materials and devices holds significant importance. Compared to high-cost experimental approaches, this study introduces an alternative method: utilizing a generative adversarial network (GAN) to reconstruct a 3D pore microstructure. Unlike some existing GAN models that require 3D images as training data, the proposed model only requires a single cross-sectional image for 3D reconstruction. Using porous ceramic electrode materials as a case study, a comparison between the GAN-generated microstructures and those reconstructed through focused ion beam-scanning electron microscopy (FIB-SEM) reveals promising consistency. The GAN-based reconstruction technique demonstrates its effectiveness by successfully characterizing pore attributes in porous ceramics, with measurements of porosity, pore size, and tortuosity factor exhibiting notable agreement with the results obtained from mercury intrusion porosimetry.

Unleashing innovation: 3D-printed biomaterials in bone tissue engineering for repairing femur and tibial defects in animal models - a systematic review and meta-analysis.

3D-printed scaffolds have emerged as an alternative for addressing the current limitations encountered in bone reconstruction. This study aimed to systematically review the feasibility of using 3D bio-printed scaffolds as a material for bone grafting in animal models, focusing on femoral and tibial defects. The primary objective of this study was to evaluate the efficacy, safety, and overall impact of these scaffolds on bone regeneration. Electronic databases were searched using specific search terms from January 2013 to October 2023, and 37 relevant studies were finally included and reviewed. We documented the type of scaffold generated using the 3D printed techniques, detailing its characterization and rheological properties including porosity, compressive strength, shrinkage, elastic modulus, and other relevant factors. Before incorporating them into the meta-analysis, an additional inclusion criterion was applied where the regenerated bone area (BA), bone volume (BV), bone volume per total volume (BV/TV), trabecular thickness (Tb. Th.), trabecular number (Tb. N.), and trabecular separation (Tb. S.) were collected and analyzed statistically. 3D bio-printed ceramic-based composite scaffolds exhibited the highest capacity for bone tissue regeneration (BTR) regarding BV/TV of femoral and tibial defects of animal models. The ideal structure of the printed scaffolds displayed optimal results with a total porosity >50% with a pore size ranging between 300- and 400µM. Moreover, integrating additional features and engineered macro-channels within these scaffolds notably enhanced BTR capacity, especially observed at extended time points. In conclusion, 3D-printed composite scaffolds have shown promise as an alternative for addressing bone defects.

Multicriteria evaluations for sand production potentials: a case study from a producing oil field in the Niger Delta Basin (Nigeria)

Geomechanical evaluation of three wells in an onshore Niger Delta Oil Field in which some reservoirs have histories of free water production was carried out to predict their potential for sand production. The mechanical characteristics of the reservoirs, such as the Poisson ratio (v), shear modulus (G), elastic modulus (E), bulk modulus (Kb), and bulk compressibility, were calculated using petrophysical parameters such as s-velocity (Vs), p-velocity (Vp), gamma ray index (IGR), the volume of shale (Vsh), porosity (), and pseudo factor (q) derived from wireline logs (Cb). Five geomechanical techniques—porosity, acoustic wave travel time, sand production index, Schlumberger sand production index, and shear modulus to bulk compressibility ratio (G/Cb) were used to forecast the potential for sand production in the reservoirs. The porosity values varied from 14 to 27%, which is below the threshold value of 30% for sand production. The acoustic wave travel time ranged from 81.59 to 89.91s/ft, which is below the threshold value of 104s/ft for sand production. The threshold value for the sand production index was < 2.9 ×106psi. The values for the sand production index fell between 11x 106psi and 12.4 x 106psi, which is below the limits for the onset of sanding. The G/Cb values in the reservoirs across the three wells fell between 1.15 x 1012psi2 and 1.43 x 1012psi2 while the threshold value for the Schlumberger sand production index fell between 1.15 x 1012psi2 and 1.43 x 1012psi2. Only the Schlumberger criterion predicted the potential for likely sand production.

Experimental and theoretical studies on 3D printed short and continuous carbon fiber hybrid reinforced composites

3D-printed carbon fiber composites hold significant potential in aerospace applications because of their lightweight, high strength and complex structure fabrication capabilities. However, additive manufacturing characteristics such as high porosity, anisotropy and continuous fiber content significantly affect the mechanical properties of printed parts. The aim of this study is to investigate the influence of hybrid printing of short and continuous carbon fibers on the mechanical properties and failure mechanisms of composites and to develop a model to predict elastic properties considering porosity. First, mechanical testing and characterization of short and continuous fiber reinforced polyamide (nylon) print filaments are performed. The results indicate that the elastic modulus and ultimate strength of continuous carbon fiber filaments reach 60 GPa and 1500 MPa, respectively, while the strength and elastic modulus of short carbon fiber filaments reach 60 MPa and 1 GPa, respectively. Then tensile specimens with different continuous fiber orientations and different continuous fiber placement sequences are printed and tested using a multi-material 3D printing technique. The specimens are characterized before and after testing using scanning electron microscopy and microscopy to assess the porosity and failure mechanisms of specimens with different configurations. The results show that the mechanical properties of the printed parts are much lower than those of the print filaments, which proves the serious negative impact of the material extrusion process on the mechanical properties of the printed structural parts. Finally, an analytical method for predicting the elastic behavior of printed composites is developed by introducing the porosity factor in the volume-averaged stiffness model. For continuous and short fiber filled composites with different fiber contents and printing orientations, the predicted results are in agreement with the experiments, and the prediction error is greatly reduced from 30 % to <5 %.

Evaluation of Pedotransfer Functions to Estimate Soil Water Retention Curve: A Conceptual Review

The soil water retention curve (SWRC) is a vital soil property used to evaluate the soil’s water holding capacity, a critical factor in various applications such as determining soil water availability for plants, soil conservation and management, climate change adaptation, and mitigation of flood risks. Estimating SWRC directly in the field and laboratory is a time-consuming and laborious process and requires numerous instruments and measurements at a specific location. In this context, various estimation approaches have been developed, including pedotransfer functions (PTFs), over the past three decades to estimate soil water retention and its associated properties. Despite the efficiencies, PTFs and semi-physical approach-based models often have several limitations, particularly in the dry range of the SWRC. PTFs-based modeling has become a key research topic due to readily available soil data and cost-effective methods for deriving essential soil parameters, which enable more efficient decision-making in sustainable land-use management. Therefore, advancement and adjustment are necessary for reliable estimations of the SWRC from readily available data. This article reviews the evaluation of the current and past PTFs for estimating the SWRC. This study aims to evaluate PTF techniques and semi-physical approaches based on soil texture, bulk density, porosity, and other related factors. Additionally, it also assesses the performance and limitations of various common semi-physical models proposed and developed by Arya and Paris, Haverkamp and Parlange, the Modified Kovács model by Aubertin et al., Chang and Cheng, Meskini-Vishkaee et al., Vidler et al., and Zhai et al. This assessment will be effective for researchers in this field and provide valuable insight into the importance of new PTFs for modeling SWRC.

Investigating the Promising P28 Peptide-Loaded Chitosan/Ceramic Bone Scaffolds for Bone Regeneration.

Bone has the ability to heal itself; however, bone defects fail to heal once the damage exceeds a critical size. Bone regeneration remains a significant clinical challenge, with autograft considered the ideal bone graft material due to its sufficient porosity, osteogenic cells, and biological growth factors. However, limitations to bone grafting, such as limited bone stock and high resorption rates, have led to a great deal of research into developing bone graft substitutes. The P28 peptide is a small molecule bioactive biomimetic alternative to mimic the bone morphogenetic protein 2 (BMP-2). In this study, we investigated the potential of P28-loaded hybrid scaffolds to mimic the natural bone structure for enhancing the bone regeneration process. We hypothesized that the peptide-loaded scaffolds and nude scaffolds both have the potential to promote bone healing, and the bone healing process is accelerated by the release of the peptide. To verify our hypothesis, C2C12 cells were evaluated for the presence of calcium deposits by histological stain at 7 and 14 days in cultures with hybrid scaffolds. Total RNA was isolated from C2C12 cells cultured with hybrid scaffolds for 7 and 14 days to assess osteoblast differentiation. The project findings demonstrated that the hybrid scaffold could enhance osteoblast differentiation and significantly improve the therapeutic effects of the scaffold in bone regeneration.

Advances and challenges in the fractionation of edible oils and fats through supercritical fluid processing.

Petrochemical solvents are widely used for the extraction and fractionation of biomolecules from edible oils and fats at an industrial scale. However, owing to its safety concerns, toxicity, price fluctuations, and sustainability, alternative solvents and technologies have been actively explored in recent years. Technologies, such as ultrasound and microwave-assisted extraction, supercritical carbon dioxide extraction, supercritical fluid fractionation, and sub-critical water extraction, and solvents, like ionic liquids and deep eutectic solvents, are reported for extraction and fractionation of biomolecules. Among them, supercritical carbon dioxide extraction and fractionation are some of the most promising green technologies with the potential to replace petrochemical-based conventional techniques. The addition of cosolvents, such as water, ethanol, and acetone, improves the extraction of amphiphilic and polar compounds from edible oils and fats. Supercritical fluid processing has diverse applications, including concentration of solutes, selective separation of desired molecules, and separation of undesirable compounds from the feed material. Temperature, pressure, particle size, porosity, flow rate, solvent-to-feed ratio, density, viscosity, diffusivity, solubility, partition coefficient, and separation factor are the fundamental factors governing the extraction and fractionation of desired biomolecules from lipids. Supercritical fluids stand alone compared to conventional fluids, because of their tunable solvent properties. Overall, it is to be noted that supercritical fluid-based methods have lots of scope to replace conventional solvent-based methods and progress toward the creation of sustainable food-processing techniques. This review critically evaluates the parameters responsible for the extraction and fractionation of biomolecules from edible oils and fats under supercritical conditions.

Dynamics of axially moving spinning microbeams composed of tri-directional graded porous materials with axisymmetric cross-sections and piezoelectric layers in complex fields

The current article appraises the vibration and stability of tri-directional functionally graded porous microscale beams with rectangular cross-sections integrated with piezoelectric layers under spinning and axial movements in complex environments. The microbeam is surrounded by a three-parameter Winkler-Pasternak-Hetenyi medium, and its material characteristics are graded in thickness, width, and longitudinal spatial directions by considering non-uniform and uniform porosity models. Dynamic equations, vibration frequencies, and stability criteria of the system are determined with the aid of the Galerkin approach and Laplace transform. The Campbell diagram and stability maps are drawn. Frequency and stability analyses, as well as comparison and parametric analyses, are conducted. The impacts of piezoelectric voltage, magneto-hygro-thermal fields, axial and tangential distributed follower forces, substrate characteristics, scale parameter, aspect ratio, porosity factor, and material gradation on flutter and divergence instability boundaries are assessed in detail. It is deduced that instability regions are condensed, and the instability threshold is enhanced by fine-adjusting the porosity and material gradient. It is discovered that destructive environmental effects can be alleviated by regulating the piezoelectric voltage. In addition, compared with the case of a square cross-section, the divergence/flutter instability region of the microbeam with a rectangular cross-section is smaller/larger. The outcomes of the present research can be helpful in the design of next-generation bi-gyroscopic systems.