Multi-channel graphene-based perfect absorbers utilizing Tamm plasmon and Fabry-Perot resonances.

In this paper, a multi-channel narrowband absorption structure utilizing the Tamm plasmon and Fabry-Perot resonances in the 1-2 THz range is presented. The structure consists of a graphene sheet, followed by a spacer layer and a dielectric-metal photonic crystal. The transfer matrix method (TMM) is employed to evaluate the effect of different parameters such as the constituent materials and thicknesses of the layers as well as the graphene chemical potential on the spectral response of the structure. Simulation results show that the number of channels, resonance frequencies, and absorption peaks can be easily adjusted by controlling the thicknesses and materials of the layers. The absorption value can reach as high as 99.23% for normal incidents. Additionally, perfect absorption of 100% is achievable by changing the angle of the incident light. Owing to high absorption and straightforward fabrication process, the proposed structure can find various applications such as filtering, sensing, optical switches, and thermal emissions.

Impact Dynamics of Non-Newtonian Droplets on Superhydrophobic Surfaces.

Droplet impact behavior on a solid surface is critical for many industrial applications such as spray coating, food production, printing, and agriculture. For all of these applications, a common challenge is to modify and control the impact regime and contact time of the droplets. This challenge becomes more critical for non-Newtonian liquids with complex rheology. In this research, we explored the impact dynamics of non-Newtonian liquids (by adding different concentrations of Xanthan into water) on superhydrophobic surfaces. Our experimental results show that by increasing the Xanthan concentration in water, the shapes of the bouncing droplet are dramatically altered, e.g., its shape at the separation moment is changed from a conventional vertical jetting into a "mushroom"-like one. As a result, the contact time of the non-Newtonian droplet could be reduced by up to ∼50%. We compare the impact scenarios of Xanthan liquids with those of glycerol solutions having a similar apparent viscosity, and results show that the differences in the elongation viscosity induce different impact dynamics of the droplets. Finally, we show that by increasing the Weber number for all of the liquids, the contact time is reduced, and the maximum spreading radius is increased.

Mechanical properties of honeycomb nanoporous silicon: a high strength and ductile structure.

There are remarkable studies geared towards developing mechanical analysis of nanoporous structures, while the size effect has been a major concern so far to improve strength or deformability. In this study, molecular dynamics simulations are utilized to study the pore shape effect on the mechanical behaviour of nanoporous silicon with circular, elliptical, square and hexagonal pore shapes. The influence of pore configuration on load transfer capabilities is studied for nanoporous silicon. A distinguished set of mechanical properties is observed on silicon with a hexagonal pore shape-resembling a honeycomb structure-with a high tensile strength and toughness. The study exhibits an improvement in the ductility through unique stress transformation in the hexagonal pore shape. In addition to the relative density, the potential to control the mechanical properties is demonstrated through the hexagon angle. Finally, a scaling law is developed for the mechanical behaviour of honeycomb nanoporous silicon. In addition to their outstanding mechanical properties, the work provides further insight into the capability of nanoporous structures in sensing applications due to their high surface-to-volume ratios.

A review of 20 years publication in the field of radiation protection in diagnostic radiology in Iran.

In recent years, knowing the risks of stochastic effects of radiation, patient dose in diagnostic radiology is taken in to consideration extensively. Many countries and international organization, including International Commission on Radiological Protection, use quantities such as dose area product, entrance surface dose, etc. in radiological investigations, which serve as a guide for patient dose reduction. The concept of diagnostic reference level (DRL) is used to optimizing the dose of patients undergoing diagnostic radiology examinations. The present study aimed to review the studies on the DRLs in Iran. This study was conducted to review the status of patient dose in diagnostic radiology in Iran. A comprehensive literature search was performed without time constraints in the popular databases such as Google Scholar, Medline, Embase, PubMed, Irandoc, Iran Medex, Magiran and Scientific Information Database under the key words 'Entrance Surface Dose', 'dose area product, 'diagnostic reference level' and 'DRL' in Iran. Data on the radiation dose to patients has demonstrated further reductions in patient doses in the Iran. Considering advanced techniques and dose reduction methodologies complicates comparability between studies. Joint efforts of experts in the field of radiological protection and medical imaging and training program are necessary for achieving an acceptable condition.

Infusion Simulation of Graphene-Enhanced Resin in LCM for Thermal and Chemo-Rheological Analysis.

The present numerical study proposes a framework to determine the heat flow parameters-specific heat and thermal conductivity-of resin-graphene nanoplatelets (GNPs) (modified) as well as non-modified resin (with no GNPs). This is performed by evaluating the exothermic reaction which occurs during both the filling and post-filling stages of Liquid Composite Moulding (LCM). The proposed model uses ANSYS Fluent to solve the Stokes-Brinkman (momentum and mass), energy, and chemical species conservation equations to a describe nano-filled resin infusion, chemo-rheological changes, and heat release/transfer simultaneously on a Representative Volume Element (RVE). The transient Volume-of-Fluid (VOF) method is employed to track free-surface propagation (resin-air interface) throughout the computational domain. A User-Defined Function (UDF) is developed together with a User-Defined Scaler (UDS) to incorporate the heat generation (polymerisation), which is added as an extra source term into the energy equation. A separate UDF is used to capture intra-tow (microscopic) flow by adding a source term into the momentum equation. The numerical findings indicate that the incorporation of GNPs can accelerate the curing of the resin system due to the high thermal conductivity of the nanofiller. Furthermore, the model proves its capability in predicting the specific heat and thermal conductivity of the modified and non-modified resin systems utilising the computed heat of reaction data. The analysis shows an increase of ∼15% in the specific heat and thermal conductivity due to different mould temperatures applied (110-170 °C). This, furthermore, stresses the fact that the addition of GNPs (0.2 wt.%) improves the resin-specific heat by 3.68% and thermal conductivity by 58% in comparison to the non-modified thermoset resin. The numerical findings show a satisfactory agreement with and in the range of experimental data available in the literature.

Assessment of machine learning models trained by molecular dynamics simulations results for inferring ethanol adsorption on an aluminium surface.

Molecular dynamics (MD) simulations can reduce our need for experimental tests and provide detailed insight into the chemical reactions and binding kinetics. There are two challenges while dealing with MD simulations: one is the time and length scale limitations, and the latter is efficiently processing the massive amount of data resulting from the MD simulations and generating the proper reaction rates. In this work, we evaluated the use of regression machine learning (ML) methods to solve these two challenges by developing a framework for ethanol adsorption on an Aluminium (Al) slab. This framework comprises three main stages: first, an all-atom molecular dynamics model; second, ML regression models; and third, validation and testing. In stage one, the adsorption of ethanol molecules on the Al surface for various temperatures, velocities and concentrations is simulated using the large-scale atomic/molecular massively parallel simulator (LAMMPS) and ReaxFF. The outcome of stage one is utilised for training, testing, and validating the predictive models in stages two and three. We developed and evaluated 28 different ML models for predicting the number of adsorbed molecules over time, including linear regression, support vector machine (SVM), decision trees, ensemble, Gaussian process regression (GPR), neural network (NN) and Bayesian hyper-parameter optimisation models. Based on the results, the Bayesian-based GPR showed the highest accuracy and the lowest training time. The developed model can predict the number of adsorbed molecules for new cases within seconds, while MD simulations take a few weeks. This adsorption rate can then be used in macroscale simulations to tackle the time and length scale limitations. The proposed numerical framework has the potential to be generalised and, therefore, contribute to future low-cost binding reaction estimations, providing a valuable tool for industry and experimentalists.

A Numerical Thermo-Chemo-Flow Analysis of Thermoset Resin Impregnation in LCM Processes.

This paper presents a numerical framework for modelling and simulating convection-diffusion-reaction flows in liquid composite moulding (LCM). The model is developed in ANSYS Fluent with customised user-defined-functions (UDFs), user-defined-scalar (UDS), and user-defined memory (UDM) codes to incorporate the cure kinetics and rheological characteristics of thermoset resin impregnation. The simulations were performed adopting volume-of-fluid (VOF)-a multiphase flow solution-based on finite volume method (FVM). The developed numerical approach solves Darcy's law, heat transfer, and chemical reactions in LCM process simultaneously. Thereby, the solution scheme shows its ability to provide information on flow-front, viscosity development, degree of cure, and rate of reaction at once unlike existing literature that commonly focuses on impregnation stage and cure stage in isolation. Furthermore, it allows online monitoring, controlled boundary conditions, and injection techniques (for design of manufacturing) during the mould filling and curing stages. To examine the validity of the model, a comparative analysis was carried out for a simple geometry, in that the numerical results indicate good agreement-3.4% difference in the degree of cure compared with previous research findings.

A Molecular Dynamics Model for Biomedical Sensor Evaluation: Nanoscale Numerical Simulation of an Aluminum-Based Biosensor.

Metallic nanostructured-based biosensors provide label-free, multiplexed, and real-time detections of chemical and biological targets. Aluminum-based biosensors are favored in this category, due to their enhanced stability and profitability. Despite the recent advances in nanotechnology and the significant improvement in development of these biosensors, some deficiencies restrict their utilization. Hence a detailed insight into their behavior in different conditions would be crucial, which can be achieved with nanoscale numerical simulation. With this aim, an Aluminum-based biosensor is chosen to be analyzed with the help of all-atom molecular dynamics model (AA-MD), using large-scale atomic/molecular massively parallel simulator (LAMMPS). The surface properties and adsorption process through different flow conditions and various concentration of the target, are investigated in this study. In the future work, the results of this study will be used for developing a predictive model for surface properties of the biosensor. Clinical Relevance- The role of biosensors in clinical applications and early diagnosis is evident. This work provides a model for predicting the binding behavior of the target molecules on the biosensor surface in different conditions. Results demonstrate an increase in the adsorption of ethanol on the biosensor surface of 7% up to 80% with changing the velocity from 0.001 m/s to 1 m/s Although for cases with higher concentration this trend becomes complicated necessitating the implementation of machine learning models in the future works.

Non-Newtonian Droplet Generation in a Cross-Junction Microfluidic Channel.

A two-dimensional CFD model based on volume-of-fluid (VOF) is introduced to examine droplet generation in a cross-junction microfluidic using an open-source software, OpenFOAM together with an interFoam solver. Non-Newtonian power-law droplets in Newtonian liquid is numerically studied and its effect on droplet size and detachment time in three different regimes, i.e., squeezing, dripping and jetting, are investigated. To understand the droplet formation mechanism, the shear-thinning behaviour was enhanced by increasing the polymer concentrations in the dispersed phase. It is observed that by choosing a shear-dependent fluid, droplet size decreases compared to Newtonian fluids while detachment time increases due to higher apparent viscosity. Moreover, the rheological parameters-n and K in the power-law model-impose a considerable effect on the droplet size and detachment time, especially in the dripping and jetting regimes. Those parameters also have the potential to change the formation regime if the capillary number (Ca) is high enough. This work extends the understanding of non-Newtonian droplet formation in microfluidics to control the droplet characteristics in applications involving shear-thinning polymeric solutions.

A Numerical Analysis of Resin Flow in Woven Fabrics: Effect of Local Tow Curvature on Dual-Scale Permeability.

Permeability is a crucial flow parameter in liquid composite moulding (LCM), which is required to predict fibre impregnation, void formation and resin back flow. This work investigates the dual-scale (micro- and meso-) nature of permeability during resin infusion into woven fabric by incorporating the intra tow flow where the degree of local tow curvature (tow/yarn undulation) is taken into account. The mesoscopic permeability of a dual-scale porous media in a unit cell is estimated using Darcy's law, where the Gebart analytical model is applied for the intra tow flow in longitudinal and transverse directions with respect to distinct fibre packing arrangements. The results suggest that for a low fibre volume fraction (≤42%), the degree of local curvature at the mesoscale can be neglected. However, for a high fibre volume fraction (>42%) and a higher fibre bundle curvature, the proposed model should be adopted, since the resin flow is affected by a mesoscopic tow curvature that could result in around 14% error in predicting permeability. It is shown that the permeability results of the current study are in good agreement with and in the range of the retrieved available experimental data from the literature.

A computational simulation platform for designing real-time monitoring systems with application to COVID-19.

With the aim of contributing to the fight against the coronavirus disease 2019 (COVID-19), numerous strategies have been proposed. While developing an effective vaccine can take months up to years, detection of infected patients seems like one of the best ideas for controlling the situation. The role of biosensors in containing highly pathogenic viruses, saving lives and economy is evident. A new competitive numerical platform specifically for designing microfluidic-integrated biosensors is developed and presented in this work. Properties of the biosensor, sample, buffer fluid and even the microfluidic channel can be modified in this model. This feature provides the scientific community with the ability to design a specific biosensor for requested point-of-care (POC) applications. First, the validation of the presented numerical platform against experimental data and then results and discussion, highlighting the important role of the design parameters on the performance of the biosensor is presented. For the latter, the baseline case has been set on the previous studies on the biosensors suitable for SARS-CoV, which has the highest similarity to the 2019 nCoV. Subsequently, the effects of concentration of the targeted molecules in the sample, installation position and properties of the biosensor on its performance were investigated in 11 case studies. The presented numerical framework provides an insight into understanding of the virus reaction in the design process of the biosensor and enhances our preparation for any future outbreaks. Furthermore, the integration of biosensors with different devices for accelerating the process of defeating the pandemic is proposed.

Numerical Simulation of a Core-Shell Polymer Strand in Material Extrusion Additive Manufacturing.

Material extrusion additive manufacturing (ME-AM) techniques have been recently introduced for core-shell polymer manufacturing. Using ME-AM for core-shell manufacturing offers improved mechanical properties and dimensional accuracy over conventional 3D-printed polymer. Operating parameters play an important role in forming the overall quality of the 3D-printed manufactured products. Here we use numerical simulations within the framework of computation fluid dynamics (CFD) to identify the best combination of operating parameters for the 3D printing of a core-shell polymer strand. The objectives of these CFD simulations are to find strands with an ultimate volume fraction of core polymer. At the same time, complete encapsulations are obtained for the core polymer inside the shell one. In this model, the deposition flow is controlled by three dimensionless parameters: (i) the diameter ratio of core material to the nozzle, d/D; (ii) the normalised gap between the extruder and the build plate, t/D; (iii) the velocity ratio of the moving build plate to the average velocity inside the nozzle, V/U. Numerical results of the deposited strands' cross-sections demonstrate the effects of controlling parameters on the encapsulation of the core material inside the shell and the shape and size of the strand. Overall we find that the best operating parameters are a diameter ratio of d/D=0.7, a normalised gap of t/D=1, and a velocity ratio of V/U=1.

Molecular Dynamics Simulations of Deformation Mechanisms in the Mechanical Response of Nanoporous Gold.

The mechanical behaviour of nanoporous gold has so far been the subject of studies for bicontinuous morphologies, while the load transfer between ligaments is the primary challenge for using nanoporous structures-especially membranes with nanopores-in single-molecule sensors. This work studies the pore shape effect on deformation mechanisms of nanoporous gold membranes through molecular dynamics simulations. Tension and compression tests are carried out for nanoporous gold with circular, elliptical, square and hexagonal pore shapes. A significant pore shape effect on the mechanical properties is observed with distinct load transfer capabilities. A uniform stress transfer between ligaments constitutes a distinguished set of mechanical responses for structures with the hexagonal pore shape under tension, while a unique stress distribution in nanoporous with the circular pore shape introduces a high strength and ductile structure under compression. Further to shed light on the existing experimental observations, this work provides a comprehensive study on load transfer capabilities in the mechanical behaviour of nanoporous gold for sensing applications.

Numerical framework for simulating bio-species transport in microfluidic channels with application to antibody biosensors.

Diagnosis is a fundamental stage in health care and medical treatment. Microfluidic biosensors and lab-on-a-chip devices are amongst the few practical tools for achieving this goal. A new computational code, specifically for designing microfluidic-integrated biosensors is developed, the details of which is presented in this work. This new approach is developed using control-volume based finite-element (CVFEM) method and solves bio-recognition chemical reactions and full Navier-Stokes equations. The results of the proposed platform are validated against the experimental data for a microfluidic based biosensor, where excellent agreement is achieved. The properties of the biosensor, sample, buffer fluid and even the microfluidic channel can easily be modified in this platform. This feature provides the scientific community with the ability to design a specific biosensor for requested point-of-care applications.•A new approach is developed using control-volume based finite-element (CVFEM) method for investigating flow inside a microfluidic-integrated biosensor. It is also used to study the influence of surface functionalization on binding cycle.•The proposed model solves bio-recognition chemical reactions as well as full Navier-Stokes and energy equations. Experimental-based or personalized equations of the chemical reactions and flow behaviour are adoptable to this code.•The developed model is Fortran-based and has the potential to be used in both industry and academia for biosensing technology.