Dynamic structure of unentangled polymer chains in the vicinity of non-attractive nanoparticles.

Using coarse-grained molecular dynamics simulation, we study the motion of unentangled polymer chains dynamically confined by non-attractive nanoparticles (NPs). Both normal mode and dynamic structure factor S(q, t) analysis are adopted to analyze chain's dynamics. Relaxation behaviors of chains are found to be significantly slowed down by NPs. The relaxation times of chain's normal modes are monotonically increasing with the NP volume fraction ϕ. At the same time, chains' dynamics are becoming non-Gaussian. Inspection of S(q, t) reveals that chain's dynamics can be attributed to two 'phases', a bulk polymer phase and a confined polymer phase between NPs. The dynamics of a confined polymer is slower than that of a bulk polymer, while still exhibiting high mobility. The amount of the bulk polymer phase is found to exponentially decay with increasing ϕ. With this figure at hand, we establish a simple relationship between NP and confined/interphase polymer volume fractions. This work seems to provide the first quantitative prediction on the relationship between NP and confined/interphase polymer volume fractions.

Ternary and senary representations using DNA double-crossover tiles.

The information capacity of DNA double-crossover (DX) tiles was successfully increased beyond a binary representation to higher base representations. By controlling the length and the position of DNA hairpins on the DX tile, ternary and senary (base-3 and base-6) digit representations were realized and verified by atomic force microscopy. Also, normal mode analysis was carried out to study the mechanical characteristics of each structure.

Synthesis of nanodiamond-daunorubicin conjugates to overcome multidrug chemoresistance in leukemia.

Nanodiamonds (NDs) are promising candidates in nanomedicine, demonstrating significant potential as gene/drug delivery platforms for cancer therapy. We have synthesized ND vectors capable of chemotherapeutic loading and delivery with applications towards chemoresistant leukemia. The loading of Daunorubicin (DNR) onto NDs was optimized by adjusting reaction parameters such as acidity and concentration. The resulting conjugate, a novel therapeutic payload for NDs, was characterized extensively for size, surface charge, and loading efficiency. A K562 human myelogenous leukemia cell line, with multidrug resistance conferred by incremental DNR exposure, was used to demonstrate the efficacy enhancement resulting from ND-based delivery. While resistant K562 cells were able to overcome treatment from DNR alone, as compared with non-resistant K562 cells, NDs were able to improve DNR delivery into resistant K562 cells. By overcoming efflux mechanisms present in this resistant leukemia line, ND-enabled therapeutics have demonstrated the potential to improve cancer treatment efficacy, especially towards resistant strains. FROM THE CLINICAL EDITOR: The authors of this study demonstrate superior treatment properties of resistant leukemia cell lines by utilizing nanodiamond vectors loaded with daunorubicin, paving the way to clinical studies in the hopefully not too distant future.

HiDeNN-FEM: A seamless machine learning approach to nonlinear finite element analysis.

The hierarchical deep-learning neural network (HiDeNN) (Zhang et al, Computational Mechanics, 67:207-230) provides a systematic approach to constructing numerical approximations that can be incorporated into a wide variety of Partial differential equations (PDE) and/or Ordinary differential equations (ODE) solvers. This paper presents a framework of the nonlinear finite element based on HiDeNN approximation (nonlinear HiDeNN-FEM). This is enabled by three basic building blocks employing structured deep neural networks: 1) A partial derivative operator block that performs the differentiation of the shape functions with respect to the element coordinates, 2) An r-adaptivity block that improves the local and global convergence properties and 3) A materials derivative block that evaluates the material derivatives of the shape function. While these building blocks can be applied to any element, specific implementations are presented in 1D and 2D to illustrate the application of the deep learning neural network. Two-step optimization schemes are further developed to allow for the capabilities of r-adaptivity and easy integration with any existing FE solver. Numerical examples of 2D and 3D demonstrate that the proposed nonlinear HiDeNN-FEM with r-adaptivity provides much higher accuracy than regular FEM. It also significantly reduces element distortion and suppresses the hourglass mode.

Nanoparticle effect on the dynamics of polymer chains and their entanglement network.

We explore the dynamics of entangled polymer chains embedded into nanocomposites. From primitive path analysis, highly entangled polymer chains are found to be significantly disentangled during increment of the volume fraction of spherical nonattractive nanoparticles (NPs) from 0 to 42%. A critical volume fraction, ϕ(c)=31%, is found to control the crossover from polymer chain entanglements to "NP entanglements." While below ϕ(c), the polymer chain relaxation accelerates upon filling, above ϕ(c), the situation reverses: polymer dynamics becomes geometrically constrained upon adding NPs. Our findings provide a microscopic understanding of the dynamics of entangled polymer chains inside their composites, and offer an explanation for the unusual rheological properties of polymer composites.

Hydrodynamics of capillary imbibition under nanoconfinement.

Understanding fluid flow in nanoconfined geometries is crucial for a broad range of scientific problems relevant to the behavior of porous materials in biology, nanotechnology, and the built environment. Because of the dominant importance of surface effects at the nanoscale, long-standing assumptions that are valid for macroscopic systems must be revisited when modeling nanoconfined fluids, because boundary conditions and the confined behavior of liquids are challenging to discern from experiments. To address this issue, here we present a novel coarse-grained model that combines parameters calibrated for water with a dissipative particle dynamics thermostat for the purpose of investigating hydrodynamics under confinement at scales exceeding current capabilities with all-atomistic simulations. Conditions pertaining to slip boundary conditions and confinement emerge naturally from particle interactions, with no need for assumptions a priori. The model is used to systematically investigate the imbibition dynamics of water into cylindrical nanopores of different diameters. Interestingly, we find that the dynamic contact angle depends on the size of the nanopore in a way that cannot be explained through a relationship between contact line velocity and dynamic contact angle, suggesting nonlocal effects of the flow field may be important. Additionally, a size-dependent characteristic time scale for imbibition is found, which could be useful for the interpretation of experiments and design of novel nanofluidic devices. We present the first systematic study that explains how contact angle dynamics and imbibition dynamics vary with nanopore radius. Our modeling approach lays the foundation for broader investigations on the dynamics of fluids in nanoporous materials in conjunction with experimental efforts.

DNA nanotube formation based on normal mode analysis.

Ever since its inception, a popular DNA motif called the cross tile has been recognized to self-assemble into addressable 2D templates consisting of periodic square cavities. Although this may be conceptually correct, in reality certain types of cross tiles can only form planar lattices if adjacent tiles are designed to bind in a corrugated manner, in the absence of which they roll up to form 3D nanotube structures. Here we present a theoretical study on why uncorrugated cross tiles self-assemble into counterintuitive 3D nanotube structures and not planar 2D lattices. Coarse-grained normal mode analysis of single and multiple cross tiles within the elastic network model was carried out to expound the vibration modes of the systems. While both single and multiple cross tile simulations produce results conducive to tube formations, the dominant modes of a unit of four cross tiles (one square cavity), termed a quadruplet, fully reflect the symmetries of the actual nanotubes found in experiments and firmly endorse circularization of an array of cross tiles.

Dielectrophoretic concentration of low-abundance nanoparticles using a nanostructured tip.

Electric field-induced concentration has the potential for application in highly sensitive detection of nanoparticles (NPs) for disease diagnosis and drug discovery. Conventional two-dimensional planar electrodes, however, have shown limited sensitivity in NP concentration. In this paper, the dielectrophoretic (DEP) concentration of low-abundance NPs is studied using a nanostructured tip where a high electric field of 3 × 10(7) V m(-1) is generated. In experimental studies, individual 2, 10, and 100 nm Au NPs are concentrated to a nanotip using DEP concentration and are detected by scanning transmission and scanning electron microscopes. The DEP force on Au NPs near the end of a nanotip is computed according to the distance, and then compared with Brownian motion-induced force. The computational study shows qualitative agreement with the experimental results. When the experimental conditions for DEP concentration are optimized for 8 nm-long oligonucleotides, the sensitivity of a nanotip is 10 aM (10 attomolar; nine copies in a 1.5 µl sample volume). This DEP concentrator using a nanotip can be used for molecular detection without amplification.

Electric field guided assembly of one-dimensional nanostructures for high performance sensors.

Various nanowire or nanotube-based devices have been demonstrated to fulfill the anticipated future demands on sensors. To fabricate such devices, electric field-based methods have demonstrated a great potential to integrate one-dimensional nanostructures into various forms. This review paper discusses theoretical and experimental aspects of the working principles, the assembled structures, and the unique functions associated with electric field-based assembly. The challenges and opportunities of the assembly methods are addressed in conjunction with future directions toward high performance sensors.

Data-driven discovery of dimensionless numbers and governing laws from scarce measurements.

Dimensionless numbers and scaling laws provide elegant insights into the characteristic properties of physical systems. Classical dimensional analysis and similitude theory fail to identify a set of unique dimensionless numbers for a highly multi-variable system with incomplete governing equations. This paper introduces a mechanistic data-driven approach that embeds the principle of dimensional invariance into a two-level machine learning scheme to automatically discover dominant dimensionless numbers and governing laws (including scaling laws and differential equations) from scarce measurement data. The proposed methodology, called dimensionless learning, is a physics-based dimension reduction technique. It can reduce high-dimensional parameter spaces to descriptions involving only a few physically interpretable dimensionless parameters, greatly simplifying complex process design and system optimization. We demonstrate the algorithm by solving several challenging engineering problems with noisy experimental measurements (not synthetic data) collected from the literature. Examples include turbulent Rayleigh-Bénard convection, vapor depression dynamics in laser melting of metals, and porosity formation in 3D printing. Lastly, we show that the proposed approach can identify dimensionally homogeneous differential equations with dimensionless number(s) by leveraging sparsity-promoting techniques.

Atomistic simulation and measurement of pH dependent cancer therapeutic interactions with nanodiamond carrier.

In this work, we have combined constant-pH molecular dynamics simulations and experiments to provide a quantitative analysis of pH dependent interactions between doxorubicin hydrochloride (DOX) cancer therapeutic and faceted nanodiamond (ND) nanoparticle carriers. Our study suggests that when a mixture of faceted ND and DOX is dissolved in a solvent, the pH of this solvent plays a controlling role in the adsorption of DOX molecules on the ND. We find that the binding of DOX molecules on ND occurs only at high pH and requires at least ∼10% of ND surface area to be fully titrated for binding to occur. As such, this study reveals important mechanistic insight underlying an ND-based pH-controlled therapeutic platform.

Prediction of anisotropic behavior of nano/micro composite based on damage mechanics with cell modeling.

New advanced composite materials have recently been of great interest. Especially, many researchers have studied on nano/micro composites based on matrix filled with nano-particles, nano-tubes, nano-wires and so forth, which have outstanding characteristics on thermal, electrical, optical, chemical and mechanical properties. Therefore, the need of numerical approach for design and development of the advanced materials has been recognized. In this paper, finite element analysis based on multi-resolution continuum theory is carried out to predict the anisotropic behavior of nano/micro composites based on damage mechanics with a cell modeling. The cell modeling systematically evaluates constitutive relationships from microstructure of the composite material. Effects of plastic anisotropy on deformation behavior and damage evolution of nano/micro composite are investigated by using Hill's 48 yield function and also compared with those obtained from Gurson-Tvergaard-Needleman isotropic damage model based on von Mises yield function.

Microscale Structure to Property Prediction for Additively Manufactured IN625 through Advanced Material Model Parameter Identification.

Challenge 4 of the Air Force Research Laboratory additive manufacturing modeling challenge series asks the participants to predict the grain-average elastic strain tensors of a few specific challenge grains during tensile loading, based on experimental data and extensive characterization of an IN625 test specimen. In this article, we present our strategy and computational methods for tackling this problem. During the competition stage, a characterized microstructural image from the experiment was directly used to predict the mechanical responses of certain challenge grains with a genetic algorithm-based material model identification method. Later, in the post-competition stage, a proper generalized decomposition (PGD)-based reduced order method is introduced for improved material model calibration. This data-driven reduced order method is efficient and can be used to identify complex material model parameters in the broad field of mechanics and materials science. The results in terms of absolute error have been reported for the original prediction and re-calibrated material model. The predictions show that the overall method is capable of handling large-scale computational problems for local response identification. The re-calibrated results and speed-up show promise for using PGD for material model calibration.

Macroscale Property Prediction for Additively Manufactured IN625 from Microstructure through Advanced Homogenization.

Design of additively manufactured metallic parts requires computational models that can predict the mechanical response of parts considering the microstructural, manufacturing, and operating conditions. This article documents our response to Air Force Research Laboratory (AFRL) Additive Manufacturing Modeling Challenge 3, which asks the participants to predict the mechanical response of tensile coupons of IN625 as function of microstructure and manufacturing conditions. A representative volume element (RVE) approach was coupled with a crystal plasticity material model solved within the Fast Fourier Transformation (FFT) framework for mechanics to address the challenge. During the competition, material model calibration proved to be a challenge, prompting the introduction in this manuscript of an advanced material model identification method using proper generalized decomposition (PGD). Finally, a mechanistic reduced order method called Self-consistent Clustering Analysis (SCA) is shown as a possible alternative to the FFT method for solving these problems. Apart from presenting the response analysis, some physical interpretation and assumptions associated with the modeling are discussed.

Universal scaling laws of keyhole stability and porosity in 3D printing of metals.

Metal three-dimensional (3D) printing includes a vast number of operation and material parameters with complex dependencies, which significantly complicates process optimization, materials development, and real-time monitoring and control. We leverage ultrahigh-speed synchrotron X-ray imaging and high-fidelity multiphysics modeling to identify simple yet universal scaling laws for keyhole stability and porosity in metal 3D printing. The laws apply broadly and remain accurate for different materials, processing conditions, and printing machines. We define a dimensionless number, the Keyhole number, to predict aspect ratio of a keyhole and the morphological transition from stable at low Keyhole number to chaotic at high Keyhole number. Furthermore, we discover inherent correlation between keyhole stability and porosity formation in metal 3D printing. By reducing the dimensions of the formulation of these challenging problems, the compact scaling laws will aid process optimization and defect elimination during metal 3D printing, and potentially lead to a quantitative predictive framework.

Data-driven characterization of thermal models for powder-bed-fusion additive manufacturing.

Computational modeling for additive manufacturing has proven to be a powerful tool to understand physical mechanisms, predict fabrication quality, and guide design and optimization. Varieties of models have been developed with different assumptions and purposes, and these models are sometimes difficult to choose from, especially for end-users, due to the lack of quantitative comparison and standardization. Thus, this study is focused on quantifying model uncertainty due to the modeling assumptions, and evaluating differences based on whether or not selected physical factors are incorporated. Multiple models with different assumptions, including a high-fidelity thermal-fluid flow model resolving individual powder particles, a low-fidelity heat transfer model simplifying the powder bed as a continuum material, and a semi-analytical thermal model using a point heat source model, were run with a variety of manufacturing process parameters. Experiments were performed on the National Institute of Standards and Technology (NIST) Additive Manufacturing Metrology Testbed (AMMT) to validate the models. A data analytics-based methodology was utilized to characterize the models to estimate the error distribution. The cross comparison of the simulation results reveals the remarkable influence of fluid flow, while the significance of the powder layer varies across different models. This study aims to provide guidance on model selection and corresponding accuracy, and more importantly facilitate the development of AM models.

Numerical simulation of a nanoparticle focusing lens in a microfluidic channel by using immersed finite element method.

Lap-on-a-chip system is one of challenging parts in nano and bio engineering fields, for instance, microfluidic channels on the chip are useful for selecting a target particle and mass transferring of boiomolecules in fluid. However, since experimental approach is highly expensive both in time and cost, alternative reliable methods are required to conceive optimized channels. The purpose of this research is to simulate a nanoparticle focusing lens in a microfluidic channel from nanoparticle control point of view. A promising immersed finite element method is expanded to estimate the path of randomly moving nanoparticles through a focusing lens. The channel flow is assumed as incompressible viscous fluid and Brownian motion effects as well as initial position of particle are quantitatively examined. As a representative result, while the nanoparticles with/without Brownian motion were focused along the center of the channel, the concentration factor representing focusing efficiency was calculated. Therefore, it is expected that the newly proposed numerical method considering Brownian motion will be efficiently applicable to design the microfluidic channel containing various particles, molecules and so forth in the near future.

Dielectrophoretic assembly of nanowires.

Nanowire (NW) assembly is currently of great interest, partly because NWs are considered as a fundamental component in the fabrication of a variety of devices. A powerful method has been developed to model the assembly of NWs. The three-dimensional dielectrophoretic (DEP) assembly of NWs across opposing electrodes is, for the first time, comprehensively studied using this new method. It is found that the DEP force reaches a maximum when the ratio of gap size to NW length is in the range 0.85-1.0. Both the magnitude and sign of the DEP torque on each NW varies with this ratio, and also with the orientation angle and the geometry and configuration of the electrode. The simulation of the dynamic assembly of individual and bundled NWs agrees with experiment. This method is of sufficient power that it will be of direct use in modeling DEP-based assembly and thus the manufacturing of nanoelectronic devices.

Immersed finite element method and its applications to biological systems.

This paper summarizes the newly developed immersed finite element method (IFEM) and its applications to the modeling of biological systems. This work was inspired by the pioneering work of Professor T.J.R. Hughes in solving fluid-structure interaction problems. In IFEM, a Lagrangian solid mesh moves on top of a background Eulerian fluid mesh which spans the entire computational domain. Hence, mesh generation is greatly simplified. Moreover, both fluid and solid domains are modeled with the finite element method and the continuity between the fluid and solid subdomains is enforced via the interpolation of the velocities and the distribution of the forces with the reproducing Kernel particle method (RKPM) delta function. The proposed method is used to study the fluid-structure interaction problems encountered in human cardiovascular systems. Currently, the heart modeling is being constructed and the deployment process of an angioplasty stent has been simulated. Some preliminary results on monocyte and platelet deposition are presented. Blood rheology, in particular, the shear-rate dependent de-aggregation of red blood cell (RBC) clusters and the transport of deformable cells, are modeled. Furthermore, IFEM is combined with electrokinetics to study the mechanisms of nano/bio filament assembly for the understanding of cell motility.