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Abstract Light‐based additive manufacturing methods are widely used to print high‐resolution 3D structures for applications in tissue engineering, soft robotics, photonics, and microfluidics, among others. Despite this progress, multi‐material printing with these methods remains challenging due to constraints associated with hardware modifications, control systems, cross‐contamination, waste, and resin properties. Here, a new printing platform coined Meniscus‐enabled Projection Stereolithography (MAPS) is reported, a vat‐free method that relies on generating and maintaining a resin meniscus between a crosslinked structure and bottom window to print lateral, vertical, discrete, or gradient multi‐material 3D structures with no waste and user‐defined mixing between layers. MAPS is compatible with a wide range of resins shown and can print complex multi‐material 3D structures without requiring specialized hardware, software, or complex washing protocols. MAPS's ability to print structures with microscale variations in mechanical stiffness, opacity, surface energy, cell densities, and magnetic properties provides a generic method to make advanced materials for a broad range of applications. 

This capsule contains the data analysis of lattice Boltzmann simulations of the structural response of bijels stabilized by magnetic ellipsoidal particles. Analysis includes the average and directional microstructure change observed after application or removal of magnetic fields. 

This capsule contains the data analysis of lattice Boltzmann simulations of the formation of bijels stabilized by magnetic ellipsoidal particles. Using data from hybrid Lattice Boltzmann-Molecular Dynamics simulations of a binary liquid with suspended magnetic ellipsoidal particles, we analyze the bond orientational order within the interfacial particle layer, the mean and Gaussian curvature of the interfaces, and the topological properties of the emulsion morphology. 

Mechanical size reduction is a critical pretreatment for hydrometallurgical recovery of valuable metals in electronic waste. The particle size resulting from milling ranges from a few micrometers to a few millimeters, presenting challenges of achieving sufficient leaching percolation in portions occupied by fine particles. This work investigates the hydrodynamics of percolation through micrometer-sized fine particle beds by using many-body dissipative particle dynamics flow simulations. The results show that higher effective pore size resulting from high aspect-ratio particle packing contributes to higher permeability than spherical particle packing. Increasing surface wettability enhances maximum saturation rates but reduces permeability. Moreover, increasing tortuosity negatively impacts permeability and the degree of reduction in permeability caused by increased surface wettability decreases with increasing tortuosity. These findings imply possible complex relationships between tortuosity, pore size, and surface wettability that collectively impact percolation in loosely packed fine particle beds and can be used to guide improvement in pretreatment. 

This capsule contains the data analysis of lattice Boltzmann simulations of the formation of bijels stabilized by magnetic ellipsoidal particles. The analysis includes the scaling of the structure factor and domain size, and the dependence of the domain size and tortuosity on the magnetic field. The orientational order of the magnetic dipoles and the interface alignment is analylzed as well as the particle packing in the interface. 

Lattice Boltzmann simulations of bijels stabilized by ellipsoidal magnetic particles in external magnetic fields demonstrate the potential of magnetic particles for fabrication of emulsion systems with tunable, anisotropic properties. 

An extended population balance model (PBM) and a deep learning-based enhanced deep neural operator (DNO+) model are introduced for predicting particle size distribution (PSD) of comminuted biomass through a large knife mill. Experimental tests using corn stalks with varied moisture contents, mill blade speeds, and discharge screen sizes are conducted to support model development. A novel mechanism in the extended PBM allows for including additional input parameters such as moisture content, which is not possible in the original PBM. The DNO+ model can include influencing factors of different data types such as moisture content and discharge screen size, which significantly extends the engineering applicability of the standard DNO model that only admits feed PSD and outcome PSD. Test results show that both models are remarkably accurate in the calibration or training parameter space and can be used as surrogate models to provide effective guidance for biomass preprocessing design. 

The molten sand that is a mixture of calcia, magnesia, alumina and silicate, known as CMAS, is characterized by its high viscosity, density and surface tension. The unique properties of CMAS make it a challenging material to deal with in high-temperature applications, requiring innovative solutions and materials to prevent its buildup and damage to critical equipment. Here, we use multiphase many-body dissipative particle dynamics simulations to study the wetting dynamics of highly viscous molten CMAS droplets. The simulations are performed in three dimensions, with varying initial droplet sizes and equilibrium contact angles. We propose a parametric ordinary differential equation (ODE) that captures the spreading radius behaviour of the CMAS droplets. The ODE parameters are then identified based on the physics-informed neural network (PINN) framework. Subsequently, the closed-form dependency of parameter values found by the PINN on the initial radii and contact angles are given using symbolic regression. Finally, we employ Bayesian PINNs (B-PINNs) to assess and quantify the uncertainty associated with the discovered parameters. In brief, this study provides insight into spreading dynamics of CMAS droplets by fusing simple parametric ODE modelling and state-of-the-art machine-learning techniques. 

The dynamics of a soft particle suspended in a viscous fluid can be changed by the presence of an elastic boundary. Understanding the mechanisms and dynamics of soft–soft surface interactions can provide valuable insights into many important research fields, including biomedical engineering, soft robotics development, and materials science. This work investigates the anomalous transport properties of a soft nanoparticle near a visco-elastic interface, where the particle consists of a polymer assembly in the form of a micelle and the interface is represented by a lipid bilayer membrane. Mesoscopic simulations using a dissipative particle dynamics model are performed to examine the impact of micelle’s proximity to the membrane on its Brownian motion. Two different sizes are considered, which correspond to ≈10−20nm in physical units. The wavelengths typically seen by the largest micelle fall within the range of wavenumbers where the Helfrich model captures fairly well the bilayer mechanical properties. Several independent simulations allowed us to compute the micelle trajectories during an observation time smaller than the diffusive time scale (whose order of magnitude is similar to the membrane relaxation time of the largest wavelengths), this time scale being hardly accessible by experiments. From the probability density function of the micelle normal position with respect to the membrane, it is observed that the position remains close to the starting position during ≈0.05τd (where τd corresponds to the diffusion time), which allowed us to compare the negative excess of mean-square displacement (MSD) to existing theories. In that time range, the MSD exhibits different behaviors along parallel and perpendicular directions. When the micelle is sufficiently close to the bilayer (its initial distance from the bilayer equals approximately twice its gyration radius), the micelle motion becomes quickly subdiffusive in the normal direction. Moreover, the temporal evolution of the micelle MSD excess in the perpendicular direction follows that of a nanoparticle near an elastic membrane. However, in the parallel direction, the MSD excess is rather similar to that of a nanoparticle near a liquid interface.