Machine Learning-Powered Ultrahigh Controllable and Wearable Magnetoelectric Piezotronic Touching Device.

Recent advancements in nanomaterials have enabled the application of nanotechnology to the development of cutting-edge sensing and actuating devices. For instance, nanostructures' collective and predictable responses to various stimuli can be monitored to determine the physical environment of the nanomaterial, such as temperature or applied pressure. To achieve optimal sensing and actuation capabilities, the nanostructures should be controllable. However, current applications are limited by inherent challenges in controlling nanostructures that counteract many sensing mechanisms that are reliant on their area or spacing. This work presents a technique utilizing the piezo-magnetoelectric properties of nanoparticles to enable strain sensing and actuation in a flexible and wearable patch. The alignment of nanoparticles has been achieved using demagnetization fields with computational simulations confirming device characteristics under various types of deformation followed by experimental demonstrations. The device exhibits favorable piezoelectric performance, hydrophobicity, and body motion-sensing capabilities, as well as machine learning-powered touch-sensing/actuating features.

Artificial Neural Networks for Predicting Mechanical Properties of Crystalline Polyamide12 via Molecular Dynamics Simulations.

Predicting material properties of 3D printed polymer products is a challenge in additive manufacturing due to the highly localized and complex manufacturing process. The microstructure of such products is fundamentally different from the ones obtained by using conventional manufacturing methods, which makes the task even more difficult. As the first step of a systematic multiscale approach, in this work, we have developed an artificial neural network (ANN) to predict the mechanical properties of the crystalline form of Polyamide12 (PA12) based on data collected from molecular dynamics (MD) simulations. Using the machine learning approach, we are able to predict the stress-strain relations of PA12 once the macroscale deformation gradient is provided as an input to the ANN. We have shown that this is an efficient and accurate approach, which can provide a three-dimensional molecular-level anisotropic stress-strain relation of PA12 for any macroscale mechanics model, such as finite element modeling at arbitrary quadrature points. This work lays the foundation for a multiscale finite element method for simulating semicrystalline polymers, which will be published as a separate study.

A machine learning-based probabilistic computational framework for uncertainty quantification of actuation of clustered tensegrity structures.

Clustered tensegrity structures integrated with continuous cables are lightweight, foldable, and deployable. Thus, they can be used as flexible manipulators or soft robots. The actuation process of such soft structure has high probabilistic sensitivity. It is essential to quantify the uncertainty of actuated responses of the tensegrity structures and to modulate their deformation accurately. In this work, we propose a comprehensive data-driven computational approach to study the uncertainty quantification (UQ) and probability propagation in clustered tensegrity structures, and we have developed a surrogate optimization model to control the flexible structure deformation. An example of clustered tensegrity beam subjected to a clustered actuation is presented to demonstrate the validity of the approach and its potential application. The three main novelties of the data-driven framework are: (1) The proposed model can avoid the difficulty of convergence in nonlinear Finite Element Analysis (FEA), by two machine learning methods, the Gauss Process Regression (GPR) and Neutral Network (NN). (2) A fast real-time prediction on uncertainty propagation can be achieved by the surrogate model, and (3) Optimization of the actuated deformation comes true by using both Sequence Quadratic Programming (SQP) and Bayesian optimization methods. The results have shown that the proposed data-driven computational approach is powerful and can be extended to other UQ models or alternative optimization objectives.

Isothermal Crystallization Kinetics of Poly(ethylene oxide)/Poly(ethylene glycol)-g-silica Nanocomposites.

In this work, the crystallization kinetics of poly(ethylene oxide) (PEO) matrix included with poly(ethylene glycol) (PEG) grafted silica (PEG-g-SiO2) nanoparticles and bare SiO2 were systematically investigated by differential scanning calorimetry (DSC) and polarized light optical microscopy (PLOM) method. PEG-g-SiO2 can significantly increase the crystallinity and crystallization temperature of PEO matrix under the non-isothermal crystallization process. Pronounced effects of PEG-g-SiO2 on the crystalline morphology and crystallization rate of PEO were further characterized by employing spherulitic morphological observation and isothermal crystallization kinetics analysis. In contrast to the bare SiO2, PEG-g-SiO2 can be well dispersed in PEO matrix at low P/N (P: Molecular weight of matrix chains, N: Molecular weight of grafted chains), which is a key factor to enhance the primary nucleation rate. In particular, we found that the addition of PEG-g-SiO2 slows the spherulitic growth fronts compared to the neat PEO. It is speculated that the interfacial structure of the grafted PEG plays a key role in the formation of nuclei sites, thus ultimately determines the crystallization behavior of PEO PNCs and enhances the overall crystallization rate of the PEO nanocomposites.

Surface slip on rotating graphene membrane enables the temporal selectivity that breaks the permeability-selectivity trade-off.

Membrane separation technology is dictated by the permeability-selectivity trade-off rule, because selectivity relies on membrane pore size being smaller than that of hydrated ions. We discovered a previously unknown mechanism that breaks the permeability-selectivity trade-off in using a rotating nanoporous graphene membrane with pores of 2 to 4 nanometers in diameter. The results show that the rotating membrane exhibits almost 100% salt rejection even when the pore size is larger than that of hydrated ions, and the surface slip at the liquid/graphene interface of rotating membrane enables concurrent ultra-selectivity and unprecedented high permeability. A novel concept of "temporal selectivity" is proposed to attribute the unconventional selectivity to the time difference between the ion's penetration time through the pore and the bypass time required for ion's sliding across the pore. The newly discovered temporal selectivity overcomes the limitation imposed by pore size and provokes a novel theory in designing high-performance membranes.

A Molecular Dynamics Study on Rotational Nanofluid and Its Application to Desalination.

In this work, we systematically study a rotational nanofluidic device for reverse osmosis (RO) desalination by using large scale molecular dynamics modeling and simulation. Moreover, we have compared Molecular Dynamics simulation with fluid mechanics modeling. We have found that the pressure generated by the centrifugal motion of nanofluids can counterbalance the osmosis pressure developed from the concentration gradient, and hence provide a driving force to filtrate fresh water from salt water. Molecular Dynamics modeling of two different types of designs are performed and compared. Results indicate that this novel nanofluidic device is not only able to alleviate the fouling problem significantly, but it is also capable of maintaining high membrane permeability and energy efficiency. The angular velocity of the nanofluids within the device is investigated, and the critical angular velocity needed for the fluids to overcome the osmotic pressure is derived. Meanwhile, a maximal angular velocity value is also identified to avoid Taylor-Couette instability. The MD simulation results agree well with continuum modeling results obtained from fluid hydrodynamics theory, which provides a theoretical foundation for scaling up the proposed rotational osmosis device. Successful fabrication of such rotational RO membrane centrifuge may potentially revolutionize the membrane desalination technology by providing a fundamental solution to the water resource problem.

Nanoengineering Microstructure of Hybrid C-S-H/Silicene Gel.

Two-dimensional (2D) materials have been incorporated into calcium silicate hydrate (C-S-H) gel to enhance its mechanical performance for decades, while the modified C-S-H gel exhibits poor toughness, tensile strength, and ductility. In this work, we report a new design strategy and synthesis route to strengthen C-S-H interface by intercalating a silicene sheet of one atom thickness. The hybrid C-S-H/Silicene gel shows superb mechanical properties, with a remarkable enhancement in strength and other functional properties. By using density functional theory (DFT) and molecular dynamics (MD) simulations, we have demonstrated that Si-O bonds between silicene and C-S-H are stable and covalent, and the interaction energy of this bilayer gel nearly doubles by forming a 3D covalent network with a strong bridging effect. Owing to its better crystallinity enrichment and its induced dislocation dissipation mechanism, the hybrid C-S-H/Silicene gel possesses a higher tensile ductility (∼118% average enhancement and ∼228% in the c direction) and a much smaller elastic stiffness (59.04 GPa for average Young's modulus). This work offers an ingenuous route in turning brittle C-S-H gel into a soft gel, which provides opportunities for fabricating ultrahigh performance cementitious materials.

Influence of functionalized core-shell structure on the thermodynamic and shape memory properties of nanocomposites.

Filler/matrix interfacial cohesion exerts a straightforward effect on stress transfer at the interface in composite structures, thereby significantly affecting their integrated mechanical properties. Thus, controlling the interface interaction of polymers/fillers is essential for the fabrication of high-performance polymer composites. In this work, a functionalized core-shell structured hybrid was prepared via charge attraction and applied as a novel filler in the trans-1,4-polyisoprene matrix to improve the interfacial interaction of the filler/matrix. A series of tests on the micro- and macroscale was performed to investigate its thermal, mechanical and shape memory performances. The obtained results show that while guaranteeing the shape memory properties of the composites, the utilization of the core-shell structured hybrid not only improved the heat resistant performance, but also contributed to better mechanical properties. This provides solid evidence for the potential of the innovative method presented herein, which may shed some light on the improvement of the interface design strategy and the development of composites with high performances.

Unidirectional Self-Driving Liquid Droplet Transport on a Monolayer Graphene-Covered Textured Substrate.

Controllable directional transport of liquid droplets on a functionalized surface has been a challenge in the field of microfluidics because it does not require energy supply, and the physical mechanism of such self-driving transport exhibits extraordinary contribution to fundamental understanding of some biological processes and the design of microfluidic apparatus. In this paper, we report a novel design of a surface microstructure that can realize unidirectional self-driving liquid mercury (Hg) droplet transport on a graphene-covered copper (Cu) substrate with a three-dimensional surface microstructure. We have demonstrated that a liquid Hg droplet spontaneously propagates on a grooved Cu substrate covered by a monolayer graphene without any external force fields. Classical molecular dynamics results provide a profound insight on the self-driving process of Hg droplets. It shows that the Hg droplet undergoes acceleration, deceleration, and return stages successively from the narrow to wide ends of the gradient groove. Intriguingly, Hg droplets can move continuously and unidirectionally on the three-dimensional graphene-covered surface microstructure when they accumulate enough kinetic energy from the gradient groove to break the energy barrier at the step junctions between the two neighboring unit cells. The design of the zigzag textured surface covered by a monolayer graphene artfully uses the facts; (1) the monolayer graphene can effectively reduce the droplet pinning on the textured surface, (2) the hydrophobic graphene layer reduces the friction between Hg droplets and the substrate, and (3) the textured surface can permeably interact with the droplets through the monolayer graphene to achieve a continuous self-driving process. The findings reported here open a door to explore the graphene-covered functional surface to directional transport of liquid droplets and provide an in-depth understanding of the self-driving mechanism for liquid droplets on graphene-covered textured substrates.

A novel graphene oxide/trans-1,4-polyisoprene (GO/TPI) shape memory polymer nanocomposite and its multifunctional properties.

In this work, we have synthesized a novel graphene oxide/trans-1,4-polyisoprene (GO/TPI) shape memory polymer (SMP) nanocomposite by adding GO (0.0-1.5 phr) to bulk TPI polymer to enhance its mechanical properties. We have studied and examined its mechanical, thermal and thermomechanical properties through systematic tests from microscale to macroscale. The shape memory properties and the effect of the GO content on the thermal and mechanical properties of the manufactured SMP nanocomposites were studied by differential scanning calorimetry, dynamic mechanical analysis, thermogravimetric analysis, thermal conductivity, and static tensile and mechanical as well as thermomechanical cyclic loading tests. The experimental results indicate that the SMP nanocomposite with 0.9 phr GO has superior thermal and mechanical properties compared to all other cases, and good shape memory was obtained in the GO/TPI nanocomposites.

Dynamics of delay-coupled FitzHugh-Nagumo neural rings.

This paper studies the dynamical behaviors of a pair of FitzHugh-Nagumo neural networks with bidirectional delayed couplings. It presents a detailed analysis of delay-independent and delay-dependent stabilities and the existence of bifurcated oscillations. Illustrative examples are performed to validate the analytical results and to discover interesting phenomena. It is shown that the network exhibits a variety of complicated activities, such as multiple stability switches, the coexistence of periodic and quasi-periodic oscillations, the coexistence of periodic and chaotic orbits, and the coexisting chaotic attractors.

Understanding the Aqueous Stability and Filtration Capability of MoS2 Membranes.

Membranes made of layer-stacked two-dimensional molybdenum disulfide (MoS2) nanosheets have recently shown great promise for water filtration. At present, the reported water fluxes vary significantly, while the accountable structure and properties of MoS2 nanochannels are largely unknown. This paper aims to mechanistically relate the performance of MoS2 membranes to the size of their nanochannels in different hydration states. We discovered that fully hydrated MoS2 membranes retained a 1.2 nm interlayer spacing (or 0.9 nm free spacing), leading to high water permeability and moderate-to-high ionic and molecular rejection. In comparison, completely dry MoS2 membranes had a 0.62 nm interlayer spacing (or 0.3 nm free spacing) due to irreversible nanosheet restacking and were almost impermeable to water. Furthermore, we revealed that the interlayer spacing of MoS2 membranes in aqueous solution is maintained by comparable van der Waals and hydration forces, thereby ensuring the aqueous stability of MoS2 membranes without the need of cross-linking. In addition, we attributed the high water flux (30-250 L m-2 h-1 bar-1) of MoS2 membranes to the low hydraulic resistance of smooth, rigid MoS2 nanochannels. We also concluded that compaction of MoS2 membranes with a high pressure helps create a more neatly stacked nanostructure with minimum voids or looseness, leading to stable water flux and separation performance. Besides, this paper systematically compares MoS2 membranes with the widely studied graphene oxide membranes to highlight the uniqueness and advantages of MoS2 membranes for water-filtration applications.

Swelling of Graphene Oxide Membranes in Aqueous Solution: Characterization of Interlayer Spacing and Insight into Water Transport Mechanisms.

Graphene oxide (GO) has recently emerged as a promising 2D nanomaterial to make high-performance membranes for important applications. However, the aqueous-phase separation capability of a layer-stacked GO membrane can be significantly limited by its natural tendency to swell, that is, absorb water into the GO channel and form an enlarged interlayer spacing (d-spacing). In this study, the d-spacing of a GO membrane in an aqueous environment was experimentally characterized using an integrated quartz crystal microbalance with dissipation and ellipsometry. This method can accurately quantify a d-spacing in liquid and well beyond the typical measurement limit of ∼2 nm. Molecular simulations were conducted to fundamentally understand the structure and mobility of water in the GO channel, and a theoretical model was developed to predict the d-spacing. It was found that, as a dry GO membrane was soaked in water, it initially maintained a d-spacing of 0.76 nm, and water molecules in the GO channel formed a semiordered network with a density 30% higher than that of bulk water but 20% lower than that of the rhombus-shaped water network formed in a graphene channel. The corresponding mobility of water in the GO channel was much lower than in the graphene channel, where water exhibited almost the same mobility as in the bulk. As the GO membrane remained in water, its d-spacing increased and reached 6 to 7 nm at equilibrium. In comparison, the d-spacing of a GO membrane in NaCl and Na2SO4 solutions decreased as the ionic strength increased and was ∼2 nm at 100 mM.

Rotating carbon nanotube membrane filter for water desalination.

We have designed a porous nanofluidic desalination device, a rotating carbon nanotube membrane filter (RCNT-MF), for the reverse osmosis desalination that can turn salt water into fresh water. The concept as well as design strategy of RCNT-MF is modeled, and demonstrated by using molecular dynamics simulation. It has been shown that the RCNT-MF device may significantly improve desalination efficiency by combining the centrifugal force propelled reverse osmosis process and the porous CNT-based fine scale selective separation technology.

On multiscale moving contact line theory.

In this paper, a multiscale moving contact line (MMCL) theory is presented and employed to simulate liquid droplet spreading and capillary motion. The proposed MMCL theory combines a coarse-grained adhesive contact model with a fluid interface membrane theory, so that it can couple molecular scale adhesive interaction and surface tension with hydrodynamics of microscale flow. By doing so, the intermolecular force, the van der Waals or double layer force, separates and levitates the liquid droplet from the supporting solid substrate, which avoids the shear stress singularity caused by the no-slip condition in conventional hydrodynamics theory of moving contact line. Thus, the MMCL allows the difference of the surface energies and surface stresses to drive droplet spreading naturally. To validate the proposed MMCL theory, we have employed it to simulate droplet spreading over various elastic substrates. The numerical simulation results obtained by using MMCL are in good agreement with the molecular dynamics results reported in the literature.

From molecular systems to continuum solids: A multiscale structure and dynamics.

We propose a concurrent multiscale molecular dynamics for molecular systems in order to apply macroscale mechanical boundary conditions such as traction and average displacement for solid state materials, which is difficult to do in traditional molecular dynamics where boundary conditions are applied in terms of forces and displacements on selected particles. The multiscale model is systematically constructed in terms of multiscale structures of kinematics, force field, and dynamical equations. The idea is to extend the Anderson-Parrinello-Rahman molecular dynamics to the cases that have arbitrary finite domain and boundary, thus the model is capable of solving inhomogeneous, non-equilibrium problems. The macroscale stress loading on a representative volume element with periodic boundary condition is generalized to all kinds of macroscale mechanical boundary conditions. Unlike most multiscale techniques, our theory is aimed at understanding fundamental physics rather than achieving computing efficiency. Examples of problems with prescribed average displacements and surface tractions are presented to demonstrate the validity of the proposed multiscale molecular dynamics.

Modeling universal dynamics of cell spreading on elastic substrates.

A three-dimensional (3D) multiscale moving contact line model is combined with a soft matter cell model to study the universal dynamics of cell spreading over elastic substrates. We have studied both the early stage and the late stage cell spreading by taking into account the actin tension effect. In this work, the cell is modeled as an active nematic droplet, and the substrate is modeled as a St. Venant Kirchhoff elastic medium. A complete 3D simulation of cell spreading has been carried out. The simulation results show that the spreading area versus spreading time at different stages obeys specific power laws, which is in good agreement with experimental data and theoretical prediction reported in the literature. Moreover, the simulation results show that the substrate elasticity may affect force dipole distribution inside the cell. The advantage of this approach is that it combines the hydrodynamics of actin retrograde flow with moving contact line model so that it can naturally include actin tension effect resulting from actin polymerization and actomyosin contraction, and thus it might be capable of simulating complex cellular scale phenomenon, such as cell spreading or even crawling.

Modelling and simulation of substrate elasticity sensing in stem cells.

Recently, we have developed a multiscale soft matter cell model aiming at improving the understanding of mechanotransduction mechanism of stem cells, which is responsible for information exchange between cells and their extracellular environment. In this paper, we report the preliminary results of our research on multiscale modelling and simulation of soft contact and adhesion of stem cells. The proposed multiscale soft matter cell model may be used to model soft contact and adhesion between cells and their extracellular substrates. To the authors' best knowledge, this may be the first time that a soft matter model has been developed for cell contact and adhesion. Moreover, we have developed and implemented a Lagrange-type meshfree Galerkin formulation and related computational algorithms for the proposed cell model. Comparison study with experimental data has been conducted to validate the parameters of the cell model. By using the soft matter cell model, we have simulated the soft adhesive contact process between cells and extracellular substrates. The simulation shows that the cell can sense substrate elasticity by responding it in different manners from cell spreading to cell contact configuration and molecular conformation changes.

Multiscale modeling and simulation of soft adhesion and contact of stem cells.

Recently, we have developed a multiscale soft matter model for stem cells or primitive cells in general, aiming at improving the understanding of mechanotransduction mechanism of cells that is responsible for information exchange between cells and their extracellular environment. In this paper, we report the preliminary results of our research on multiscale modeling and simulation of soft contact and adhesion of cells. The proposed multiscale soft matter cell model may be used to model soft contact and adhesion between cells and their extracellular substrates. This model is a generalization of the Fluid Mosaic Model (Singer and Nicolson, 1972), or an extension of Helfrich's liquid crystal membrane model (Helfrich, 1973). To the best of the authors' knowledge, this may be the first time that a soft matter model is developed for cell contact and adhesion. Moreover we have developed and implemented a Lagrange type meshfree Galerkin formulation and the computational algorithm for the proposed cell model. Comparison study with experimental data has been conducted to validate the parameters of the model. By using the soft matter cell model, we have simulated the soft adhesive contact process between cells and their extracellular substrates. The simulation shows that the cell can sense substrate elasticity by responding in different manners from cell spreading motion to cell contact configurations.