Material Parameters Research Articles

Unraveling hierarchically ordered melt electro-written tissue engineering scaffolds: Morphological and mechanical insights

Addressing critical tissue defects treatment remains a pressing challenge in medicine and bioengineering. Tissue engineering (TE) scaffolds, characterized by porous architectures suitable to cell growth, is a pivotal solution. Recent advances in additive techniques have revolutionized scaffold fabrication, enabling precise control over complex porous structures. This study conducts a comprehensive analysis of hierarchically ordered melt electrospun written (MEW) TE scaffolds, elucidating the relationships between fabrication parameters and their morphological and mechanical properties. Leveraging the phenomenon of melt jet deposit buckling, characteristic hierarchically ordered porous architectures were attained. The study explores the fabrication potential of hierarchically ordered porous MEW architectures across varied voltages, feed rates, and needle sizes. Morphometric parameters, including percent porosity, density of fiber intersections, and fiber diameter, were identified. It was revealed that for feed rates exceeding 20 mm/s, resultant fiber diameters were unaffected by voltage. However, increasing voltage leads to noticeable reduction of mesh stiffness due to the coiled fibers presence. Exceptions occur at the feed rate of 20 mm/s and for needle G24, where stiffness surpasses those of regular primary pattern, which could be attributed to increased number of fiber interconnections.

Conceptual and Applied Aspects of Water Retention Tests on Tailings Using Columns

The water retention capacity of porous materials is crucial in various geotechnical and environmental engineering applications such as slope stability analysis, landfill management, and mining operations. Filtered tailings stacks are considered an alternative to traditional tailings dams. Nevertheless, the mechanical behaviour and stability of the material under different water content conditions are of concern because these stacks can reach considerable heights. The water behaviour in these structures is poorly understood, particularly the effects of the water content on the stability and potential for liquefaction of the stacks. This study aims to investigate the water retention and flow characteristics of compacted iron ore tailings in high columns to better understand their hydromechanical behaviour. The research used 5 m high columns filled with iron ore tailings from the Quadrilátero Ferrífero region in Minas Gerais, Brazil. The columns were prepared in layers, compacted, and instrumented with moisture content sensors and suction sensors to monitor the water movement during various stages of saturation, drainage, infiltration, and evaporation. The sensors provided consistent data and revealed that the tailings exhibited high drainage capacity. The moisture content and suction profiles were effectively established over time and revealed the dynamic water retention behaviour. The comparison of the data with the theoretical soil water retention curve (SWRC) demonstrated a good correlation which indicates that there was no hysteresis in the material response. The study concludes that the column setup effectively captures the water retention and flow characteristics of compacted tailings and provides valuable insights for the hydromechanical analysis of filtered tailings stacks. These findings can significantly help improve numerical models, calibrate material parameters, and contribute to the safer and more efficient management of tailings storage facilities.

Damage Prediction in the Wire Drawing Process

In this study, the prediction of damage in the wire drawing process of 2011 aluminum alloy was investigated through both experimental and numerical methods. A comprehensive experimental setup was designed involving 20 cases of wire drawing with varying die angles (10°, 15°, 21°, 27°, and 34°) and reductions (21%, 29%, 31%, and 38%). Each case was tested three times, and the drawing forces, as well as occurrences of wire breakage, were recorded. The mechanical behavior of the material was firstly characterized using uniaxial tensile tests, whose results were used to determine the material parameters of both the hardening Voce law and those of uncoupled and coupled damage models. Then, the numerical simulations of the wire drawing process were performed using a finite element model, accounting for axisymmetric conditions and mesh convergence analysis to ensure accuracy. The previously characterized damage models were applied to evaluate their fracture prediction capabilities. A novel presentation method using three-dimensional graphs was employed to indicate the level of damage for each angle and reduction, providing greater sensitivity and insight into the damage values. Good agreement between the experimental and numerical data was demonstrated for the bilinear coupled damage model, validating its effectiveness. This study contributes to a better understanding of damage prediction in the wire drawing process, with implications for improving industrial practices and material performance evaluations.

A Novel Normal Contact Stiffness Model of Bi-Fractal Surface Joints

The contact stiffness of the mechanical joint usually becomes the weakest part of the stiffness for the whole machinery equipment, which is one of the important parameters affecting the dynamic characteristics of the engineering machinery. Based on the three-dimensional Weierstrass–Mandelbrot (WM) function, the novel normal contact stiffness model of the joint with the bi-fractal surface is proposed, which comprehensively considers the effects of elastoplastic deformation of asperity and friction factor. The effect of various parameters (fractal dimension, scaling parameter, material parameter, friction factor) on the normal contact stiffness of the joint is analyzed by numerical simulation. The normal contact stiffness of the joint increases with an increase in the fractal dimension, normal load, and material properties and decreases with an increase in the scaling parameter. Meanwhile, the fractal parameters of the equivalent rough surface of the joint are calculated by the structural function method. The experimental results show that when the load is between 14 and 38 N∙m, the error of the model is within 20%. The normal contact stiffness model of the bi-fractal surface joint can provide a theoretical basis for the analysis of the dynamic characteristics of the whole machine at the design stage.

Enhancement of second-harmonic generation in a lithium niobate metasurface by exploring the bound states in the continuum

The nonlinear lithium niobate (LiNbO3), characterized by transparency across a broad spectral range from ultraviolet to mid-infrared, stands out as an ideal material for second-harmonic generation (SHG). The concept of bound states in the continuum (BIC) represents a non-radiative mode embedded within the radiation continuum, offering the capability to confine the electromagnetic field within the nanostructure. Here, we propose the design of a LiNbO3 metasurface utilizing the BIC mechanism to enhance SHG at fundamental wavelengths above 2 µm, which injects new thoughts into the field of integrated optics and on-chip photonics. Notably, we rigorously accounted for the influence of the side-wall angle θ and the corner rounding radius R of the LiNbO3 metasurface, which arises from fabrication tolerances. Considering those influences in the simulation, we achieved a quasi-BIC (q-BIC) with a quality factor (Q-factor) up to 1.44 × 105. Moreover, by considering the depleted pump model, the absolute efficiency of SHG reached 4.09% with a corresponding normalized efficiency of 8.19 × 10−10m2/W under a radiation intensity of 5 kW/cm2. Our research aims to establish a predictive framework through numerical simulations, specifically addressing realistic fabrication problems. This approach is intended to optimize the parameters for LiNbO3 metasurface fabrication, ensuring that the subsequent experiment efforts are efficient. Our findings provide an approach to predicting the optical response in actual structures and inspire new applications in photonics.

Modeling CSF circulation and the glymphatic system during infusion using subject specific intracranial pressures and brain geometries

BackgroundInfusion testing is an established method for assessing CSF resistance in patients with idiopathic normal pressure hydrocephalus (iNPH). To what extent the increased resistance is related to the glymphatic system is an open question. Here we introduce a computational model that includes the glymphatic system and enables us to determine the importance of (1) brain geometry, (2) intracranial pressure, and (3) physiological parameters on the outcome of and response to an infusion test.MethodsWe implemented a seven-compartment multiple network porous medium model with subject specific geometries from MR images using the finite element library FEniCS. The model consists of the arterial, capillary and venous blood vessels, their corresponding perivascular spaces, and the extracellular space (ECS). Both subject specific brain geometries and subject specific infusion tests were used in the modeling of both healthy adults and iNPH patients. Furthermore, we performed a systematic study of the effect of variations in model parameters.ResultsBoth the iNPH group and the control group reached a similar steady state solution when subject specific geometries under identical boundary conditions was used in simulation. The difference in terms of average fluid pressure and velocity between the iNPH and control groups, was found to be less than 6% during all stages of infusion in all compartments. With subject specific boundary conditions, the largest computed difference was a 75% greater fluid speed in the arterial perivascular space (PVS) in the iNPH group compared to the control group. Changes to material parameters changed fluid speeds by several orders of magnitude in some scenarios. A considerable amount of the CSF pass through the glymphatic pathway in our models during infusion, i.e., 28% and 38% in the healthy and iNPH patients, respectively.ConclusionsUsing computational models, we have found the relative importance of subject specific geometries to be less important than individual differences in resistance as measured with infusion tests and model parameters such as permeability, in determining the computed pressure and flow during infusion. Model parameters are uncertain, but certain variations have large impact on the simulation results. The computations resulted in a considerable amount of the infused volume passing through the brain either through the perivascular spaces or the extracellular space.

Modeling of Catalyst Degradation in Polymer Electrolyte Membrane Fuel Cells Applied to Three‐Dimensional Computational Fluid Dynamics Simulation

ABSTRACTIn a polymer electrolyte membrane (PEM) fuel cell, the following degradation mechanisms are associated with the catalyst particles and their support: carbon support corrosion triggered by carbon and platinum oxidation, platinum dissolution with redeposition, and particle detachment with agglomeration. In this work, an electrochemical model for those degradation effects is presented as well as its coupling with a three‐dimensional computational fluid dynamics PEM fuel cell performance model. The overall model is used to calculate polarization curves and current density distributions of a PEM fuel cell in a fresh and aged state as well as the degradation process during an accelerated stress test with 30 000 voltage cycles. The obtained simulation results are compared to measurements on a three‐serpentine channel PEM fuel cell with an active area of 25 cm2 under various temperatures and humidities. The experimental data are obtained with a segmented test cell using respective degradation protocols and test conditions proposed by the United States Department of Energy. In addition to the temperature and humidity changes, the influence of geometry and material parameters on the degree of degradation and the resulting fuel cell performance is explored in detail.

Exploring the CO2 conversion activated by the dielectric barrier discharge plasma assisted with photocatalyst via machine learning

DBD plasma assisted with halide perovskite photocatalysts is very attractive for the conversion of CO2 into high value-added products under mild conditions. However, it is still challenging to further effectively improve the corresponding CO2 conversion efficiency due to the consumption of time, materials, equipments via traditional experimental approach. In this paper, we simulate the CO2 conversion process under the DBD plasma assisted with different photocatalysts by machine learning. The process conditions and material parameters were selected as the feature variables of the machine learning model, and all the features were screened by combining Pearson's correlation coefficient and MIR ranking. The regression algorithms were evaluated using K-fold cross-validation combined with the coefficient of determination (R2), while SVR and BPANN with the best prediction performance for CO2 conversion ratio and energy efficiency were selected to establish the prediction models. In order to increase the accuracy of predictions, the hyperparameters of these two base models were improved using genetic algorithm, particle swarm optimization and Bayesian optimization. The R2 of the GA-SVR-CO2 and Bayesian-BPANN-EE reached 0.8960 and 0.9679 for the testing sets, respectively, and their practical applications were successfully verified. In addition, the effects of feature parameters on the conversion efficiency were revealed by SHAP. SEI scatter plots and Spearman correlation coefficient have been used for correlation analysis to better explore the correlation between machine learning models and experiments. This work brings new insights into the contributions of multiple parameters in the plasma system assisted with photocatalyst for efficient conversion of CO2.

A 90SrHfO3-based betavoltaic/beta-photovoltaic dual-effect integrated nuclear battery

In this paper, the secondary conversion idea is used to reduce the self-absorption effect of the radioactive source by combining the radioactive source with the scintillation material, so as to enhance the energy conversion efficiency of the battery. A theoretical model of a dual-effect integrated nuclear battery based on 90SrHfO3 doped with Ce is proposed. The emission photon and electron spectra of the β-luminescent integrated radioactive source 90SrHfO3 have been calculated by GEANT4. The average outgoing electron energy of SrHfO3 was calculated, and the thickness of the energy reducing material was determined. The effect of structural parameters of GaAs materials on the dual-effect integrated nuclear battery was analyzed to obtain the optimal output performance according to theoretical calculation. From the perspective of conversion efficiency, the activity density and thickness of 90SrHfO3 are determined to be 1.6 Ci/cm2 and 53.6 μm. At this time, the thickness of SrHfO3 is 1.28 mm. The total maximum output power density of the optimized dual-effect integrated nuclear battery is 9.31 μ W/cm2, and the energy conversion efficiency is 0.18 %. At this point, the doping concentrations of GaAs are Na = 1.26 × 1017 cm−3 and Nd = 6.31 × 1018 cm−3, and xj is 0.05 μm. Compared with nonintegrated batteries, the output performance is significantly improved.

Prediction of rotational bending fatigue life of bearing steel based on damage mechanics

Based on the theory of continuous damage mechanics, this paper proposes a fatigue damage evolution model and numerical calculation method considering the influence of vacuum chemical heat treatment process, and studies the rotational bending fatigue damage analysis and life prediction method of M50NiL bearing steel. Firstly, the constitutive model, fatigue damage evolution equation and material parameter calibration method in the theoretical model are given. Secondly, based on the ABAQUS platform, the damage mechanics-finite element numerical calculation method of fatigue damage analysis considering the influence of hardened layer is realized by writing the UMAT subroutine; then, the rotational bending fatigue test of M50NiL bearing steel treated by vacuum chemical heat treatment of quenching and tempering, carburizing and carbonitriding is carried out, and the influence of heat treatment process on fatigue performance is analyzed; finally, based on the proposed fatigue damage model and numerical calculation method, the rotational bending fatigue life of M50NiL bearing steel is predicted, and the results are compared with the experimental results to verify the correctness of the proposed method.

Design of piezoelectric quasi— zero—stiffness metastructures for improved low—frequency vibration isolation

Abstract In this paper, a novel type of piezoelectric quasi–zero stiffness (QZS) metastructure is proposed and the improved vibration isolation performance is investigated. A piezoelectric QZS metastructure is composed of curved beams covered with piezoelectric macro–fiber composite patches, to which digital circuits are connected. Based on the principle of minimum potential energy and the mode superposition method, the constrain on the geometry and material parameters of the piezo–curved beams to achieve QZS is given. According to this design criterion, 1D, 2D and 3D piezoelectric QZS metastructures are designed, and their improved low–frequency vibration isolation characteristics are comprehensively analyzed through theoretical, numerical and experimental studies. It is demonstrated that by optimizing the parameters of the resonant transfer functions implemented in the digital circuits, high–level damping localized near the first resonant peak can be introduced into the curved beams. As a result, the resonant peaks of the metastructures can be reduced without compromising the isolation performance beyond those peaks.

Prediction of electron-solid interaction parameters using machine learning.

Electron backscattering coefficient and electron-stopping power are essential concepts in many disciplines, from radiation to materials science, semiconductor manufacturing, and space exploration. They enable precise calculations, measurements, and simulations of electron interactions with matter, which contribute to advancing science, technology, and safety in a variety of applications. The availability of these data is fundamental to scientific research to validate hypotheses, conduct experiments, and explore new theories. A relatively novel machine learning approach has demonstrated notable success in enhancing data quality and completeness, significantly contributing to the facilitation of data discovery. Using fundamental material property data, the stacking ensemble machine learning (EML) technique was established in this study to generate electron-solid interaction parameters for any target material over a wide range of energies. The final stacking EML was built using the base and meta learners bagging regressor (BR), K-nearest neighbors (k-NN), random forest (RF), support vector regression (SVR), and eXtreme Gradient Boosting (XGB). In this study, two publicly available databases with a total of 4030 data points were used. Training datasets have 785 and 525 data points for electron backscattering coefficient and stopping power, respectively, whereas testing datasets contain 262 and 175 data points. Fivefeatures were used as input variables to train different individual algorithms and their combinations. On both the training and test datasets, the model was evaluated using different error metrics, including R-squared (R2), mean-absolute-error (MAE), root-mean-squared-error (RMSE), and mean-absolute-percentage-error (MAPE). Our model evaluation tests revealed that combining RF and XGB with a k-NN meta-learner outperformed other algorithms. The analysis of error metrics demonstrated a very close fit to all samples in each training dataset. Furthermore, predictions made by the model on unseen test data indicated accurate estimations of new backscattering and stopping power data. The developed model achieved high prediction accuracy for various target materials across the broad electron energy spectrum. The outcomes demonstrate the effectiveness of machine learning methodology and the chosen models' suitability for addressing substantial physics challenges.

Design and Testing of a Pleurotus pulmonarius Stick Cutting Machine

Based on the cultivation environment and material parameters of Pleurotus pulmonarius sticks, a P. pulmonarius stick cutting machine is designed to solve the problem of directly realizing the cutting of P. pulmonarius sticks for a net-shaped mushroom stick cultivation layer frame. The machine consists of three parts: a traveling trolley, an XYZ three-axis moving mechanism, and a clamping and cutting mechanism. Based on the force analysis of the clamping and cutting mechanism and the P. pulmonarius sticks, the key components were designed and selected. A prototype was constructed, and several cutting tests were conducted, with the cutting speed, number of cutting circles, and depth of the cut as the main factors. The success rate of cutting, the roundness of the cut, and the degree of loss in terms of the cutout substrate, were used as the performance indicators. An optimized design, based on orthogonal testing, was employed to determine the optimal parameter combinations for the cutting process. The optimal parameters were found to be a cutting speed of 376.3 r/min, 5.4 cutting circles, and a cutting depth of 4 mm. Finally, a validation test was carried out on the machine, and the results show that under the same parameter conditions, the success rate of cutting was 86.6%, the roundness of the cut was 0.235, and the degree of loss in terms of the cutout substrate was 0.851%, which is consistent with the optimization results. This study provides a reference, including a practical reference, for the design and development of a machine to realize the cutting of P. pulmonarius sticks for a cultivation layer frame.

Stochastic modelling of uncertainty in Chilean radiata pine wood beams subject to lateral torsional buckling

ABSTRACT This paper aims to investigate the effect of the variability in the mechanical and cross-section properties of Chilean radiata pine on the lateral buckling strength of slender wooden beams. The study is conducted numerically through finite element modeling and using linear eigenvalue analysis. The numerical model is validated using experimental results available in the literature. Two approaches are employed in the analysis, firstly a deterministic approach using design of experiments (DOE) techniques to establish the relevance of the parameters, and secondly a stochastic one, through Monte-Carlo simulation to investigate the impact of each parameter on the lateral buckling resistance. Various distribution functions are considered to investigate the effect of uncertainties in the material parameters and their impact on the critical buckling moments. Moreover, Sobol indices indicated that from the total variability of the distribution of the critical moment, an average of 50% of the variability is attributed to the shear modulus G LR , 37% to the longitudinal elastic modulus E L , and a negligible percentage of the grain angle F .

Plasmonic coupling effect of annealed gold nanoarrays

Periodic metal nanodisc arrays have the potential to exhibit regularly spaced large local field enhancements, especially when high-Q collective plasmonic grating resonances can be obtained. Here we demonstrate how Laser interference lithography (LIL) as a maskless and high throughput technique can be used to fabricate these on square centimeter areas. The drawback of LIL is the rather fixed ratio of the size of the individual nanostructure (d) to the period of the array (p) of about d/p ∼ 0.5 for the setup used in the current article, thereby, limiting its ability to create resonances with ultra-high quality factors (Q-factors). To improve the Q-factor of the resonances of the arrays, we study the effect of thermal annealing nanodisk arrays fabricated by LIL and a lift off process. The nanodisk arrays with periods of 400 nm and 500 nm exhibited a plasmonic resonance, which was caused by the interaction of the single disk resonance and a (1 0) grating resonance. Annealing for a short duration lowered the d/p ratio from 0.5 to 0.4, and led to smoothening of the disk surfaces and growth of gold grains, resulting in lower ohmic and radiative losses and doubling of the Q-factor of the resonances. Finite element method (FEM) simulations were used to monitor this improvement in material parameters. Annealing for a longer duration disintegrated the nanodisk into several smaller particles while maintaining the overall periodicity of the array. While the plasmonic resonances of the experimentally investigated fragmented disks were basically destroyed, simulation predict that for larger periods fragmented nanodisk arrays (keeping the d/p ∼ 0.4) can exhibit extremely strong and sharp resonances whose Q-factor increases more than 58.4 times compared to the unfragmented discs. In addition, simulations show a massive enhancement of the local electric field promising immense potential for surface enhanced Raman sensing.

Numerical simulation of the creep of a turbine blade made of a monocrystalline alloy

The article is devoted to the creep model of a monocrystalline alloy and the development of a methodology for identifying material parameters based on the results of physical experiments. A finite element analysis of the creep of a gas turbine engine blade was performed. Creep is one of the most dangerous types of deformation in the operating conditions of turbine blades. In the process of studying the problems of assessing the strength of turbine blades of aircraft engines and power plants, special attention should be paid to the study of stress redistribution during creep. The characteristics of the crystallographic structures of modern turbine blades have a very significant influence on the progress of the crack development process during engine operation. To date, turbine blades are manufactured by the method of single-crystal casting. This type of blade material structure is characterized by orthotropic mechanical properties. This study considers the steady-state creep model of an anisotropic heat-resistant single-crystal alloy with cubic symmetry. The authors carried out a numerical simulation of the material parameters using the well-known literary creep properties of single crystals. An algorithm is described that allows you to determine some creep characteristics of single crystals. The parameters of the given ratios can be obtained after conducting direct experiments, or based on micromechanical analysis, using the example of composite materials. The authors calculated the creep constants of a typical heat-resistant monocrystalline alloy as a result of the approximation of its creep curves, which were obtained experimentally. Based on the Norton-Bailey equation and using the Maple Release 2021.0 calculation complex, a graph of the dependence of the rate of creep deformation on the level of load applied to the material was plotted, and the minimum rate of deformation and creep constants were also determined. The results of the calculations were used for finite-element simulation of creep on the example of a solid-state model of a high-pressure turbine blade. Several series of calculations were carried out on the basis of the ANSYS Workbench complex, in particular, the calculation of the elastic problem when the blade is loaded by centrifugal forces, as well as the accumulation of creep deformations at different exposure times. Graphs of the change in equivalent stresses and creep deformations as a function of time are plotted.

Effectiveness of Laser-Assisted Teeth Bleaching: A Systematic Review

Objective: Esthetic dentistry is an important factor in increasing patients’ quality of life. This study aimed to investigate the impact of laser use on bleaching procedures for natural teeth and dental restorative materials. Methods: In January 2024, an electronic search was conducted using PubMed, Web of Science (WoS), and Scopus databases with the keywords (tooth) AND (laser) AND (bleaching), following PRISMA guidelines and the PICO framework. The initial search yielded 852 articles, of which 441 were screened. After applying inclusion criteria, 376 articles were excluded as they did not focus on the use of lasers in bleaching natural teeth and restorative materials. Consequently, 40 articles were included in the final review. Results: Of the 40 qualified publications, 29 utilized a diode laser, of which 10 authors concluded that it increases the whitening effect comparing classical methods. Three of included publications investigated the whitening of dental materials, while another three focused on endodontically treated teeth. Whitening procedures on ceramics effectively removed discoloration, but the resulting color did not significantly differ from the initial shade. Conversely, composite materials not only failed to bleach but also exhibited altered physical properties, thereby increasing their susceptibility to further discoloration. The KTP laser demonstrated promising outcomes on specific stains. The Er,Cr:YSGG and Er:YAG lasers also showed beneficial effects, although there were variations in their efficacy and required activation times. Conclusions: The findings partially indicate that laser-assisted bleaching improves the whitening of natural teeth. Further research on the effect of laser bleaching on the physical parameters of restorative materials is necessary.

Influences of size effect and workpiece temperature during cryogenic micro milling of soft viscoelastic polymer: an experimental assessment

Application of tool-based micromachining in soft polymer has been limited due to high adhesion and low elastic modulus of this material at room temperature. This study aims to evaluate the machinability of a typical viscoelastic soft polymer and understand the effect of material and process parameters on machining performances. In this study, a micro-milling process using cryogenic-assisted cooling is proposed and the importance of temperature control toward glass transition zone was particularly addressed. To identify insight of machinability in micro domain, this article also determines minimum uncut chip thickness (MUCT) and size effects by considering the variations of cutting force and surface integrity with the ratio of h/re (uncut chip thickness (h) to cutting edge radius (re)). The experimental results reveal that consideration of size effect during micromilling of soft viscoelastic polymer helps in reduction of machined surface roughness (Sa) value. Based on the cutting force pattern, it is evaluated that higher machining stability can be achieved during cryogenic machining by reduction of specific cutting force value. Moreover, temperature range around glass transition zone yields more promising experimental outcomes, outperforming other cooling zones.

Label-free and sensitive detection of Citrus Bark Cracking Viroid in hop using Ti3C2Tx MXene-modified genosensor

Since the discovery of Citrus bark cracking viroid (CBCVd) in hops in 2007, affected hop-growing countries such as Slovenia and Germany have been actively pursuing efficient and easily accessible diagnostic tools that could contribute to the early detection of CBCVd in the field. In the early stages of CBCVd infection, typical symptoms or subtle signs of spread may not be evident. Detection becomes feasible only when the plant begins to display symptoms. Unfortunately, there is currently no treatment available, which requires the removal of infected plants as the only viable solution. However, this approach leads to significant economic losses on a large scale. This work demonstrates the development and study of a sensitive, selective, and label-free impedimetric genosensor for the detection of CBCVd in total RNA hop samples. The genosensor is based on a supporting glassy carbon electrode modified with streptavidin-agarose beads, which serve as an effective immobilization layer for a biotinylated single-stranded DNA capture probe. The integration of a 2D-layered Ti3C2Tx MXene into the sensing architecture resulted in a significantly improved electroanalytical performance of the genosensor. Several fabrication and operational parameters were optimized, such as the streptavidin-agarose beads deposition time, capture probe immobilization time and concentration, and sample incubation time. The optimized genosensor exhibited a limit of detection of only 0.5 fg μL⁻¹ (5.5 fmol L−1) in combination with a one-hour incubation with the denatured total RNA hop extract, thus eliminating the need for an additional and laborious amplification step.

Darcy-Brinkman Flow of a Micropolar Fluid through an Anisotropic Porous Channel

This study investigates the Darcy-Brinkman flow of micropolar fluid through an anisotropic porous channel, a configuration relevant to various industrial and natural processes involving complex fluid flow. The anisotropy of the porous medium is characterized by its permeability aligned along two principal axes, with one axis forming an angle with the horizontal direction. The model applies no-slip and no-spin boundary conditions at the channel’s impermeable walls. Using a combination of analytical techniques and graphical analysis, we explore how key parameters such as the Darcy number, anisotropy angle, permeability ratio, and micropolar material parameters affect the flow behavior. Results reveal that increasing the anisotropy angle and permeability ratio leads to a reduction in both fluid velocity and micro rotational velocity, while the Darcy number promotes an increase in flow velocity. Additionally, the study discusses the impact of these parameters on shear stress and couple stress at the channel walls. The findings contribute to a deeper understanding of micropolar fluid behavior in porous media, with potential applications in filtration, lubrication, and subsurface fluid dynamics.