Multi-physical Properties Research Articles

An empirical study on the persuasive particle size effects over the multi-physical properties of monophase MWCNT-Al2O3 hybridized nanofluids

Hybrid nanofluids are colloidal suspensions containing two different nanoparticles whose enhanced thermal properties rely upon different parameters such as properties of the base fluid and nanoparticles, in addition to particle size and shape. While few studies have reported the best particle weight ratio (PWR) based on the optimal properties, no results have been found to declare the efficient PWR based on particle sizes. The current study focuses on measuring the fluid properties of hybrid (MWCNT-Al2O3) nanofluids with 5 nm and 30 nm Al2O3 at different temperatures (15 to 55 °C), particle concentrations (0.025 to 0.5%), and particle weight ratio (90:10, 80:20, 60:40, 40:60. 20:80, 10:90). When 0.1% volume concentrated nanofluids were compared based on particle size, the maximum enhancement for pH and electrical conductivity (σ) was 9.8 to 15.9% and 195.5 to 229.1% owing to the 80:20 ratio nanofluids with 5 nm Al2O3. In contrast, the viscosity and thermal conductivity enhancement were higher with 30 nm Al2O3 nanofluids by 17.5 to 21.7% for 10:90 ratio and 7.3 to 25.9% for 40:60 ratio. After measuring the properties of hybrid nanofluids with 5 nm Al2O3 at different PWR, temperature, and volume concentrations, the 90:10 ratio possessed the maximum enhancement in properties. The maximum pH, and σ enhancement were 14.5 to 23.3%, and 238.2 to 248.3% at 0.2% concentration, whereas viscosity and thermal conductivity enhanced by 40.9 to 57.3%, and 13.7 to 45.1% at 0.5% concentration. Correlations were computed to predict relative values that have R-square values between 0.9160 and 0.9926, with a margin of deviation away from experimental values by −9.42 and + 6.83%.

Multi-fidelity surrogate modeling through hybrid machine learning for biomechanical and finite element analysis of soft tissues

Biomechanical simulation enables medical researchers to study complex mechano-biological conditions, although for soft tissue modeling, it may apply highly nonlinear multi-physics theories commonly implemented by expensive finite element (FE) solvers. This is a significantly time-consuming process on a regular computer and completely inefficient in urgent situations. One remedy is to first generate a dataset of the possible inputs and outputs of the solver in order to then train an efficient machine learning (ML) model, i.e., the supervised ML-based surrogate, replacing the expensive solver to speed up the simulation. But it still requires a large number of expensive numerical samples. In this regard, we propose a hybrid ML (HML) method that uses a reduced-order model defined by the simplification of the complex multi-physics equations to produce a dataset of the low-fidelity (LF) results. The surrogate then has this efficient numerical model and an ML model that should increase the fidelity of its outputs to the level of high-fidelity (HF) results. Based on our empirical tests via a group of diverse training and numerical modeling conditions, the proposed method can improve training convergence for very limited training samples. In particular, while considerable time gains comparing to the HF numerical models are observed, training of the HML models is also significantly more efficient than the purely ML-based surrogates. From this, we conclude that this non-destructive HML implementation may increase the accuracy and efficiency of surrogate modeling of soft tissues with complex multi-physics properties in small data regimes.

Manufacturing, characteristics and applications of auxetic foams: A state-of-the-art review

Auxetic foams counter-intuitively expand (shrink) under stretching (compression). These foams can exhibit superior mechanical properties such as resistance to shear and indentation, improved toughness and energy absorption (EA) under several types of loadings. Their unique deformation mechanism and manufacturing process lead to special multiphysics properties such as variable permeability, synclastic curvature and shape memory. Except for traditional energy absorber stuff, the potential applications of auxetic foams have involved biomedicine, aerospace, smart sensing, etc. However, most of the potential applications are restrained in the theoretical stage due to complicated fabrication and a deficiency of stability. For removing the barrier for practical application, a series of issues remain to be resolved, though the explorations of the manufacture methodologies and potential applications are fruitful in the past decades. We present here a review article discussing the state-of-the-art for manufacturing, characterization and applications of auxetic foams. We also provide a view of the existing challenges and possible future research directions, aiming to state the perspective and inspire researchers to further develop the field of auxetic foams.

Hygrothermal Behavior of a Washing Fines-Hemp Wall under French and Tunisian Summer Climates: Experimental and Numerical Approach.

This study experimentally and numerically investigates the hygrothermal behavior of a wall made of washing fines hemp composite under typical French and Tunisian summer climates. Actually, insulating bio-based building materials are designed in order to reduce energy and non-renewable resources consumptions. Once their multiphysical properties are characterized at material scale, it is necessary to investigate their behavior at wall scale. Washing fines hemp composite shows low thermal conductivity and high moisture buffer ability. The test wall is implemented as separating wall of a bi-climatic device, which allows simulating indoor and outdoor climates. The numerical simulations are performed with WUFI Pro 6.5 Software. The results are analyzed from the temperature, relative humidity and vapor pressure kinetics and profiles and from heat and moisture transfer and storage. The thermal conductive resistance calculated at the end of the stabilization phase is consistent with the theoretical one. The hygric resistance is consistent for simulation up to steady state. The dynamic phase under daily cyclic variation shows that for such cycles two thirds of the thickness of the wall on the exterior side are active. It also highlights sorption-desorption phenomena in the wall.

High-Accuracy Cancer Cell Viability Evaluation Based on Multi-Physical Properties Extraction

High-Accuracy Cancer Cell Viability Evaluation Based on Multi-Physical Properties Extraction

Flexoelectric properties of multilayer two-dimensional material MoS2

Two-dimensional (2D) materials exhibit a high strength and flexibility along with unique electrical–mechanical multiphysics properties. In this study, we experimentally demonstrated the electromechanical response of a multilayer 2D material, 2H-phase MoS2, by using a piezoresponse force microscope. In particular, the dominant physical quantity of the deformation response was determined by independently controlling the electric field and electric field gradient by changing the probe shape and material thickness (number of layers). The multilayer MoS2 exhibited an out-of-plane electrical–mechanical deformation response that followed and was inverted with respect to positive and negative voltages, respectively. Moreover, the relationships between the electric field gradient and strain were similar for all shapes of the probe tip and film thickness values. This result indicated that the electrical–mechanical response of this material was dominated by the electric field gradient, and the strain could be attributed to the converse flexoelectric effect. The findings can provide guidance for the realization of ultrathin electromechanical devices.

Photostrictive effect in 1-3 composites of photovoltaic and piezoelectric phases: A numerical study

Coalesce of photovoltaic effect with converse piezoelectric effect will turn into a photostrictive phenomenon. The current study conceptualizes a 1-3 photostrictive composite consists of a photovoltaic polymer as matrix and fibers of piezoelectric material. The proposed artificial photostrictive composite is capable of replacing lead-based naturally occurring photostrictive material, not only opening a potential for new applications but also caters to tailor the desired properties. Present study employs poly{4,8-bis[5-(2-ethyl-hexyl)thiophen-2-yl]benzo[1,2-b:4,5-b’]dithiophene-2,6-diyl-alt-3-fluoro-2-[(2-ethylhexyl) carbonyl] thieno[3,4-b] thiophene-4,6-diyl} (PTB7-Th) as organic photovoltaic polymer and Pb(Mg1/3Nb2/3)O3-0.35PbTiO3 (PMN-35PT) as the fibers. A representative volume element technique (RVE) is employed to embrace the local variation of multi-physics properties. The actuation response of cantilever and simply supported beam bonded to photostrictive composite patch is accurately predicted by finite element method, while discretizing the structure with degenerated shell element. Photostrictive composite with 60% volume fraction of fibers, arranged in square pattern have deflected the cantilever tip to 1.95 mm. Therefore, we provide 1-3 photostrictive composite as a solution for future wireless and lightweight vibration control applications.

Recent Advances on SEM-Based In Situ Multiphysical Characterization of Nanomaterials.

Functional nanomaterials possess exceptional mechanical, electrical, and optical properties which have significantly benefited their diverse applications to a variety of scientific and engineering problems. In order to fully understand their characteristics and further guide their synthesis and device application, the multiphysical properties of these nanomaterials need to be characterized accurately and efficiently. Among various experimental tools for nanomaterial characterization, scanning electron microscopy- (SEM-) based platforms provide merits of high imaging resolution, accuracy and stability, well-controlled testing conditions, and the compatibility with other high-resolution material characterization techniques (e.g., atomic force microscopy), thus, various SEM-enabled techniques have been well developed for characterizing the multiphysical properties of nanomaterials. In this review, we summarize existing SEM-based platforms for nanomaterial multiphysical (mechanical, electrical, and electromechanical) in situ characterization, outline critical experimental challenges for nanomaterial optical characterization in SEM, and discuss potential demands of the SEM-based platforms to characterizing multiphysical properties of the nanomaterials.

A Stochastic Microstructure Reconstruction-Based Mechanical and Transport Modeling Approach for Learning the Microstructure-Property Relationship of Li-Ion Battery Graphite Anodes

Understanding the relationships between microstructural characteristics and multiphysics properties is key to designing battery electrode materials for desired properties. Significant efforts have been made to achieve a quantitative understanding of the relationship between mass transport properties and Li-ion microstructure characteristics [1-3] in literature, but it is still challenging to predict the coupled mechanical and electrochemical behaviors based on microstructure characteristics.We seek to further this understanding through an investigation of how deformation affects the transport properties of the Li-ion battery graphite anode microstructure, which features packed particles and irregular pore networks. We propose a statistical Microstructure Characterization and Reconstruction (MCR) approach to characterize statistical microstructure features from 2D microscopic images and then reconstruct 3D stochastic microstructures. MCR is an effective tool for material property prediction [4] and microstructure-mediated material design [5]. The proposed MCR approach generates microstructure designs that are beyond the scope of empirical data.By exploring the input microstructure feature space, 3D microstructure samples are reconstructed and simulated to investigate relationships between the microstructural characteristics and properties of Li-ion battery graphite anodes. The selection of microstructure characteristics is informed by an external open access battery microstructures library. For each microstructure reconstruction, compression and transport simulations are conducted to determine the Young’s modulus and to understand the transport properties (e.g. diffusivity) of the undeformed and deformed microstructures. Convergence studies are conducted to establish a Representative Volume Element (RVE) size. With the simulation dataset, the microstructure-property relationship is examined. A feature selection algorithm is used to examine the size of the effects of microstructural characteristics on multiphysics properties. Machine learning models are established to predict the microstructure-property relationship.

Physical properties-based microparticle sorting at submicron resolution using a tunable acoustofluidic device

Acoustophoretic manipulation has been drawing great attention in the area of microfluidics-based cell/particle isolation and sorting due to its advantages of high biocompatibility, contactless manipulation and fabrication simplicity. Most label-free microfluidic particle sorting methodologies are solely based on a single physical property for example particle size, density or material composition, which hinder their broad usage in various applications. In this work, we demonstrate a tunable acoustofluidic device for continuous particle separation based on multi-physical properties such as size, density, compressibility and speed of sound. The tunable acoustofluidic system utilizes an elasto-inertial particle focusing technique to align particles into a single line and integrates a slanted interdigitated transducer (SFIT) that generates a tunable travelling surface acoustic wave to separate the particles with improved sorting accuracy and efficiency. The presented acoustofluidic sorting device allows the generation of a variable frequency from 99 MHz to 247 MHz corresponding to the wavelength 40 μm and 16 μm using the SFIT. We have successfully sorted 5.26 μm from 5 μm polystyrene particles at 90 % accuracy with a relative size difference of 5.2 %. Furthermore, a theoretical analysis of acoustic radiation force (ARF) exerted on rigid particles of different materials (i.e., polystyrene, PLGA, PMMA) is presented. Based on the optimal frequencies identified in the theoretical analysis, we have also experimentally demonstrated effective sorting of the three above-mentioned materials of particles with the same particle size.

Hierarchical kirigami-inspired graphene and carbon nanotube metamaterials: Tunability of thermo-mechanic properties

Tuning and programming the multiphysical properties of advanced materials are of critical importance for developing the next generation of adaptable multifunctional metamaterials. This study demonstrates the tunability of thermo-mechanical properties of graphene sheets and carbon nanotubes by inspiring from hierarchical kirigami mechanical metamaterials. The theoretical investigation, multiscale simulation, and experimentation show that the thermo-mechanical properties of nano-architected kirigami metamaterials can be tuned by altering geometrical parameters and introducing the hierarchical cutting patterns. Additionally, the thermal conductivity of kirigami-inspired graphene and carbon nanotube metamaterials can be regulated by an external mechanical tension. We develop closed-form formulations for predicting the mechanical behavior of kirigami graphene sheets and carbon nanotubes. Molecular dynamics and finite element simulations are conducted to evaluate theoretical predictions. By analyzing and comparing the results from atomistic and continuum-based simulations, the effect of length scale on the thermo-mechanical properties is explored. We realize that the stress–strain response, thermal conductivity, and buckling-induced 3D patterns of nano-architected graphenes can be programmed by utilizing kirigami building blocks, nano-architectural hierarchy, and heterogeneous material design.

Current Rectification, Resistive Switching, and Stable NDR Effect in BaTiO3/CeO2 Heterostructure Devices

AbstractElectronic devices with simultaneous manifestation of multiphysical properties are of great interest due to their possible application in multifunctional devices. In the present study, simultaneous execution of negative differential resistance (NDR) effect, current rectification (≈105), and resistive switching characteristics (≈103) are demonstrated in BaTiO3/CeO2 heterostructure. Although the negative differential effect has gained huge attention, its instability and poor reproducibility at room temperature are the main obstacles to its possible application in electronic devices. However, in this report, the NDR is observed even after hundreds of cycles in BaTiO3/CeO2 heterostructure at 300 K. For a detailed analysis of the NDR effect, AC conductance spectroscopy is performed, which reveals that the presence of NDR is associated with trapping/detrapping of charge carriers at interface states formed at the BaTiO3/CeO2 interface. In addition, the resistive switching and rectification effect are demonstrated due to a barrier at the BaTiO3/CeO2 interface, which can be strongly modulated by thickness (20, 50, and 80 nm) based ferroelectric polarization of BaTiO3. However, the results show that the remarkable performance of these devices makes them a potential candidate for application in multifunctional devices.

Sparse machine learning assisted deep computational insights on the mechanical properties of graphene with intrinsic defects and doping

Despite the tremendous capabilities of Molecular dynamics (MD) simulations, they suffer from the limitation of computationally intensive and time-consuming nature. This hinders the seamless discovery of nanomaterials with adequate computational insights. Over the last decade, graphene has received widespread attention from the scientific community due to its extra-ordinary multi-physical properties, primarily focusing on the fundamental physics and chemistry along with the notion of scalable synthesis. However, the recent advances in machine learning have opened new frontiers in the research of such exceptional two-dimensional materials where the boundaries of different multi-physical properties could be pushed further efficiently with the assistance of deep computational intelligence. Here we propose a coupled machine learning (ML) based approach to investigate the critical mechanical responses of graphene by capturing the underlying physics of the system through an (D-)optimally minimum number of MD simulations. We have investigated five different internal and external controlling input features like temperature, strain rate, nanostructural defects, doping, and chirality, which influence the critical mechanical properties of graphene such as the constitutive behavior including fracture strength, failure strain and Young's modulus. Even though the aspect of computational intensiveness in molecular dynamics simulations is addressed through the coupled ML based approach, in a data-driven comprehensive analysis, it is often difficult to get complete information about the concerning input parameters including their statistical distributions and occurrence bounds. Thus, it becomes difficult to carry out computational investigations based on powerful techniques like Monte Carlo simulation. To mitigate this lacuna, here we have exploited the ML-assisted approach for developing a level-based fuzzy framework at nano-scale under sparse input descriptions. In this article, we first demonstrate the computational efficiency achieved through the proposed ML based framework without compromising quality of the analysis, followed by data-intensive correlation analysis, sensitivity and uncertainty quantification considering various levels of the influencing system parameters, revealing detailed computational insights on mechanical properties of graphene including the coupled interactive influence of intrinsic defects and doping.

A Review of the Multi-Physical Characteristics of Plant Aggregates and Their Effects on the Properties of Plant-Based Concrete

The use of plant aggregates obtained from agricultural co-products mixed with mineral binders to form eco-friendly insulating building materials has been initiated for a few years to bring environmentally friendly solutions to the construction sector. Several studies on different agro-resources have already been carried out, providing various information about the properties of plant aggregates and plant-based concrete. However, the characteristics of the agricultural co-product, which allow it to qualify as a plant aggregate for plant-based concrete, are not yet very clear despite the multitude of data, especially on hemp concrete. Therefore, it is important to gather numerous but very disparate pieces of information available in the literature concerning the properties of plant aggregates and their correlations with composites. This review is based on the results of 120 articles and aims to identify the characterization methods and the multi-physical properties of plant aggregates affecting those of plant-based concrete and to propose additional factors that could influence the properties of the composites. A total of 18 plant aggregates of different origins used for plant-based concrete have been listed in the literature. In France, hemp shiv is the most studied one, but its quantity is quite low unlike cereal or oilseed straws and wood transformation residues. With the existence of several characterization methods, properties like microstructure, particle size distribution, bulk density, water absorption capacity, and chemical composition of aggregates are easily and frequently determined. In contrast, data on the apparent density of particles, the skeleton density, and the hygro-thermal properties of aggregates are rare. The particle size, density, and porosity have been identified as important parameters influencing the properties of the composites. Other parameters related to the behavior of the aggregates under wet compaction and compression of their stacking can also predict the physical and mechanical properties of the obtained plant-based concrete. Dosages of the constituents should be preferred as formulation parameters for future studies assessing the impact of the aggregate properties on the composites.

A Review of Vat Photopolymerization Technology: Materials, Applications, Challenges, and Future Trends of 3D Printing.

Additive manufacturing (3D printing) has significantly changed the prototyping process in terms of technology, construction, materials, and their multiphysical properties. Among the most popular 3D printing techniques is vat photopolymerization, in which ultraviolet (UV) light is deployed to form chains between molecules of liquid light-curable resin, crosslink them, and as a result, solidify the resin. In this manuscript, three photopolymerization technologies, namely, stereolithography (SLA), digital light processing (DLP), and continuous digital light processing (CDLP), are reviewed. Additionally, the after-cured mechanical properties of light-curable resin materials are listed, along with a number of case studies showing their applications in practice. The manuscript aims at providing an overview and future trend of the photopolymerization technology to inspire the readers to engage in further research in this field, especially regarding developing new materials and mathematical models for microrods and bionic structures.

Representation of the Multiphysical Properties of SiO2-Al2O3-CaO Slags by Deep Neural Networks

Representation of the Multiphysical Properties of SiO2-Al2O3-CaO Slags by Deep Neural Networks

Experimental Investigation of the Multi-Physical Properties of an Energy Efficient Translucent Concrete Panel for a Building Envelope

The multi-physical properties of the building envelope play a major role in the energy efficiency of buildings. Translucent concrete panels (TCPs) with various volumetric ratios of optical fibers (OFs) were cast. To understand the multi-physical properties of the TCP for the building envelope, compressive strength, thermal and light transmittance tests were carried out. The compressive strength test showed that TCP with light-weight mortar (LWM) has higher strength compared to that with normal-weight mortar (NWM), but it did not exhibit an apparent ductile behavior. The U-values of the plain panel were 4.25 and 5.45 W/(m2 K) for TCPs with the LWM and NWM, respectively. The existence of the OFs improved the thermal insulation property. The K-values of the LWM TCP were smaller than that of the common façade, which proved its excellent energy-efficient performance. The solar heat gain coefficients (G-values) of the two tested TCP types—LWM and NWM—were 0.198 and 0.242, respectively. The visible light transmission test showed that the light transmitted by the TCP was proportional to the density of the OFs in a matrix of concrete. The experimental light acceptance angle of the OF was close to the computational value (35 °C). Therefore, all the experimental results demonstrated that TCPs can improve the energy efficiency of buildings.

Tailored mechanical response and mass transport characteristic of selective laser melted porous metallic biomaterials for bone scaffolds

Porous metallic biomaterials developed from pentamode metamaterials (PMs) were rationally designed to mimic the topological, mechanical, and mass transport properties of human bones. Here, a series of diamond-based PMs with different strut parameters were fabricated from a Ti-6Al-4V powder by selective laser melting (SLM) technique. The morphological features, mechanical properties and permeability of PM samples were then characterized. In terms of morphology, the as-built PMs were well consistent with the as-designed ones, although the slight surface deviations were presented in overhanging areas. The PM scaffolds showed a switchable deformation pattern controlled by the slenderness ratio of struts. The double-cone strut topology increases the tortuosity and thereby accelerates the nutrients supply, waste removal, and cell migration to the whole scaffold region and circumambient bone tissue. The measured mechanical properties (i.e., E: 0.59-2.90 GPa, σy: 20.59-112.63 MPa) and computational permeability values (k: 9.87-49.19 × 10−9 m2) of PM scaffolds were all in accordance with those of trabecular bone. The experimental permeability values were linearly dependent on the results of simulations. This study showed the great potential of PMs as bone scaffolds. Moreover, we demonstrated that PM-based porous biomaterials can decouple the mass transport and mechanical properties of bone scaffolds, so as to achieve an unprecedented level of tailoring their multi-physics properties. Statement of SignificanceThe topological diversity can significantly improve the adaptability of the implant to the primary bone tissue. Previous studies revealed that the mechanical and mass transport properties of porous biomaterials are correlated to the material types, porosities and lattice topologies but neglected effects of strut design. We show here the influence of strut morphology on the mechanical and mass transport properties which are independently tailored, that is, the mass transport properties can be markedly increased while maintaining the mechanical properties of mimicking specific bones, vice versa. This study emphasizes the importance of strut topological design in the development of AM porous biomaterials.

Tailored Mechanical Response and Mass Transport Characteristic of Selective Laser Melted Porous Metallic Biomaterials for Bone Scaffolds

Porous metallic biomaterials developed from pentamode metamaterials (PMs) were rationally designed to mimic the topological, mechanical, and mass transport properties of human bones. Here, A series of diamond-based PMs with different strut parameters were fabricated from a Ti-6Al-4V powder by selective laser melting (SLM) technique. The morphological features, mechanical properties and permeability of PM samples were then characterized. In terms of morphology, the as-built PMs were well consistent with the as-designed ones, although the slight surface deviations were presented in overhanging areas. The PM scaffolds showed a switchable deformation pattern controlled by the slenderness ratio of struts and volume fractions. The double-cone topology strut also increases the tortuosity and thereby accelerates the nutrients supply, waste removal, and cell migration to the whole scaffold region and circumambient bone tissue. The measured mechanical properties (i.e., E: 0.59-2.90 GPa, σy: 20.59-112.63 MPa) and computational permeability values (k: 9.87-49.19 x10-9 m2) of PM scaffolds are all in accordance with those of trabecular bone. The experimental permeability values were linearly dependent on the results of simulations. This study clearly shows the great potential of PMs as bone scaffolds. Moreover, we demonstrated that PM-based porous biomaterials can decouple the mass transport and mechanical properties of bone scaffolds, so as to achieve an unprecedented level of tailoring their multi-physics properties.

Effect of compaction on multi-physical properties of hemp-black liquor composites

This study aims to develop fully agro-based insulating building materials. Previous studies investigated several types of aggregates and binders to develop composites. The thermal conductivity of such composite appears mainly impacted by the density. This paper aims to investigate the effect of density on various multi-physical properties of composites, using the same formulation and adjusting the forming step. Thus, specimens are produced with the same hemp shiv to black liquor ratio but different compaction stress. The effect of compaction on bulk density, porosity, mechanical resistance, thermal conductivity and Moisture Buffer Value (MBV) is analyzed. It is shown that it is possible to reach thermal conductivity that is low enough to be considered as building insulating material. Thanks to this study, the required compaction stress to ensure targeted values of density, then thermal conductivity and MBV, is identified.