3D Particles Research Articles

Continuous 3D particles manipulation based on cooling thermal convection

Techniques that can dexterously manipulate particles/cells are significant for biological detection, analytical chemistry, and material synthesis applications. Although there are many methods to realize the 3D manipulation in a static condition, there is a lack of sufficient 3D manipulating strategies in a continuous flow. We develop an integrated microsystem with a cooling system based on thermal convection to realize 3D manipulation in continuous flow. Theoretical modeling and experiments demonstrate that with increasing the temperature of a microheater, the moving direction of particles transfers from the y-axis to the z-axis. Based on the principle, focusing flow and single particles are manipulated in the y-axis and z-axis. Besides, five outlets in different z-axis are fabricated to demonstrate the application of particle sorting. This novel strategy offers a new direction for particle manipulation vertically and benefits to reveal the heat transfer in micro-scale, which can pave a path for generating the next integrated microfluidic system for particles/cells sorting.

Experimental study on anisotropy of hydraulic conductivity for municipal solid waste.

Anisotropy of hydraulic conductivity is an important parameter controlling fluid movement in municipal solid waste (MSW) landfills, while measurements of anisotropy are rare. In this study, a laboratory-scale enhanced reactor was built to create MSW samples with different degrees of degradation. Vertical and horizontal hydraulic conductivities of these samples were measured in a self-designed permeameter to study the effects of compression and degradation on anisotropy of MSW. CT scanning was performed to observe the internal pore-structure of MSW under compression. A prediction model of anisotropy under compression was established. It was found that as degradation time increased from 0month to 18months, the dry mass percent of 0D particles increased from 12.3% to 38.8%, while 2D particles content decreased from 78.7% to 47.2%. As vertical stress increased from 50kPa to 400kPa, dry unit weight (γd) increased from 3.26 kN/m3 to 5.51 kN/m3, anisotropy (A) increased from 1.26 to 5.17. It was because that the size and continuity of pores decreased and the angle of pore arrangement tended to be horizontal as the vertical stress increased. The relation between anisotropy and vertical stress could be well fitted with the prediction model. When degradation time increased from 0month to 18months, A decreased linearly from 5.02 to 2.75 due to the decreasing content of 2D particles. Anisotropy also decreased with the decreasing C/L. Compression has much greater influence on waste anisotropy than that of degradation. Anisotropy of MSW at different depths of landfills could be determined based on the trend lines in this study.

Enhanced water permeability in nanofiltration membranes using 3D accordion-like MXene particles with random orientation of 2D nanochannels

A random 2D nanochannel orientation in a polyamide membrane provides superior membrane permeability and selectivity to a horizontally aligned one.

Eulerian incompressible smoothed particle hydrodynamics on multiple GPUs

Recent advances in the development of Eulerian incompressible smoothed particle hydrodynamics (EISPH), such as high-order convergence and natural coupling with Lagrangian formulations, demonstrate its potential as a meshless alternative to traditional computational fluid dynamics (CFD) methods. This work aims to address one of the major outstanding limitations of EISPH, its relatively high computational cost, by providing an implementation that can be deployed on multiple graphics processing units (GPUs). To this end, a pre-existing multi-GPU version of the open-source Lagrangian weakly-compressible code DualSPHysics is converted to an EISPH formulation and integrated with an open-source multi-GPU multigrid solver (AmgX) to treat the pressure Poisson equation implicitly. The integration of AmgX within DualSPHysics presents a significant challenge, since AmgX is designed for distributed systems and therefore conflicts with the single-node shared memory design of the multi-GPU DualSPHysics code. The present implementation is validated against well-known test cases, showing excellent agreement with benchmark solutions and demonstrating second-order convergence. A detailed profiling and performance testing is also presented to investigate memory consumption and scaling characteristics. The results show approximately 87%–95% strong scaling efficiency and 92%–94% weak scaling efficiency in both 2D and 3D on up to four GPUs. Large spikes in memory consumption during the initialisation of the linear solver library are found to impede full utilisation of the device memory. Nevertheless, the present implementation is shown to permit problem sizes on the order of 69.3 million (2D) and 20.8 million (3D) particles on four GPUs (128 GB total device memory), which is beyond what has previously been reported for incompressible SPH on GPUs, where the PPE is treated implicitly, and demonstrates its potential as an alternative to traditional CFD methods.

2D Colloids: Size- and Shape-Controlled 2D Materials at Fluid-Fluid Interfaces.

Advances in synthesis of model 3D colloidal particles with exotic shapes and physical properties have enabled discovery of new 3D colloidal phases not observed in atomic systems, and simulations and quasi-2D studies suggest 2D colloidal systems have an even richer phase behavior. However, a model 2D (one-atom-thick) colloidal system has yet to be experimentally realized because of limitations in solution-phase exfoliation of 2D materials and other 2D particle fabrication technologies. Herein, we use a photolithography-based methodology to fabricate size- and shape-controlled monolayer graphene particles, and then transfer the particles to an air-water interface to study their dynamics and self-assembly in real-time using interference reflection microscopy. Results suggest the graphene particles behave as "hard" 2D colloidal particles, with entropy influencing the self-assembled structures. Additional evidence suggests the stability of the self-assembled structures manifests from the edge-to-edge van der Waals force between 2D particles. We also show graphene discs with diameters up to 50 μm exhibit significant Brownian motion under optical microscopy due to their low mass. This work establishes a facile methodology for creating model experimental systems of colloidal 2D materials, which will have a significant impact on our understanding of fundamental 2D physics. Finally, our results advance our understanding of how physical particle properties affect the interparticle interactions between monolayer 2D materials at fluid-fluid interfaces. This information can be used to guide the development of scalable synthesis techniques (e.g., solution-phase processing) to produce bulk suspensions of 2D materials with desired physical particle properties that can be used as building blocks for creating thin films with desired structures and properties via interfacial film assembly.

A novel Arcs-based discrete element modeling of arbitrary convex and concave 2D particles

This study presents a novel Arcs-based discrete element method (ArcDEM) for efficient simulation of realistic granules with arbitrary convex and concave 2D particle outlines. In the proposed ArcDEM, a series of computational geometry algorithms are first developed to identify the convex corners and concave troughs of an arbitrary-shaped particle outline. Then, the circle-growing technique and the least squares method are combined to establish the Arcs-based particle that can represent the whole particle outline with multi-connected inward and outward arcs. Next, a new algorithm for efficient overlapping detection and precise contact resolution is developed for the Arcs-based particles. Finally, the ArcDEM is developed as a simulation tool with several implemented contact force laws and particle motions solved by an explicit time integration. To validate the feasibility and efficiency of the proposed ArcDEM, several numerical examples are performed, including (1) random allocation of non-overlapping irregular particles, (2) random packing of super-elliptical particles of different aspect ratios and blockiness, and (3) simulation of repose angle and biaxial compression tests of realistic rock particles with various roundness. The developed ArcDEM tool shows a powerful capability of numerically investigating the macro- and micromechanical properties of realistic convex and/or concave granular particles.

Effects of Modification Degrees on the Colloidal Stability of Amphiphilic Janus Graphene Oxide in Aqueous Solution with and without Electrolytes.

Colloidal stability of modified graphene oxide (GO) is fundamental for its practical applications. Meanwhile, most of the investigations mainly focused on the nanosheets modified by a certain amount of modifiers and neglected the effects of the modification degree, which could vary the physical and chemical properties of modified GO and significantly affect its stability in solution. To the best of our knowledge, this study initially investigated the impact of modification degrees on the colloidal stability of graphene-based amphiphilic Janus nanosheets (JGO) via both experimental and theoretical approaches. The prepared JGO, asymmetrically grafted by dodecylamine, exhibited a direct relation between the modification degree and nanosheet thickness, refractive index, electrostatic properties, hydrophobicity, and the ultimate colloidal stability. In addition, the ionic strength imposed distinctive influences on the aggregation behavior of JGO. Based on the comparison between experimental results and theoretical calculation, it was revealed that the JGO should be modeled as two-dimensional (2D) nanosheets in pure water and be treated as 3D spherical particles in electrolyte solutions for the prediction with the extended Derjaguin-Landau-Verwey-Overbeek theory.

RETRACTED: Creating zinc vacancy within 3D hierarchical ZnIn2S4 particles for boosted photocatalytic performance towards H2 evolution reaction

Zinc atomic vacancies were created within ZnIn 2 S 4 particles by reactive ion etching (RIE). The as-prepared ZnIn 2 S 4 particles exhibited a flower-like 3D hierarchical structure. Zn vacancies had significant impacts on the S chemical environment but had little impact on the In chemical environment. The cubic crystal phase of ZnIn 2 S 4 was not affected by the Zn vacancies, while the bandgap of ZnIn 2 S 4 was gradually decreased with the increased concentration of Zn vacancies. The ZnIn 2 S 4 particles with the optimal Zn vacancy concentration exhibited an enhanced photocatalytic H 2 evolution rate, which is 2.7 times of the bare ZnIn 2 S 4 . The stability of the optimized ZnIn 2 S 4 photocatalyst was tested. Nyquist plots and transient photocurrent plots demonstrated that, the existence of Zn vacancies resulted in a decreased charge carrier transfer resistance and an increased charge separation rate. The radical trapping experiments proved that, superoxide was the crucial active species during the photocatalytic reaction.

Development of a novel high throughput brain-on-chip with 3D structure and its application in evaluation of pesticide-induced-neurotoxicity

We designed and fabricated a novel high throughput brain-on-chip with three dimensional structure with the aim to simulate the in vivo three-dimensional growth environment for brain tissues. The chip consists of a porous filter and 3D brain cell particles, and is loaded into a conventional 96-well plate for use. The filter and the particle molds were fabricated by using computer modeling, 3D printing of positive mold and agarose-PDMS double reversal mold. The 3D cell particles were made by pouring and solidifying a suspension of mouse embryonic brain cells with sodium alginate into a cell particle mold, and then cutting the resulting hydrogel into pieces. The loaded brain-on-chip was used to determine the neurotoxicity of pesticides. The cell particles were exposed to 0, 10, 30, 50, 100 and 200 µmol/L of chlorpyrifos or imidacloprid, separated conveniently from the medium by removing the porous filter after cultivation. Subsequently, cell proliferation, acetylcholinesterase activity and lactate dehydrogenase release were determined for toxicity evaluation. The embryonic brain cells were able to grow and proliferate normally in the hydrogel particles loaded into the filter in a 96-well plate. Pesticide neurotoxicity test showed that both chlorpyrifos and imidacloprid presented dose-dependent inhibition on cell growth and proliferation. Moreover, the pesticides showed inhibition on acetylcholinesterase activity and increase release of lactate dehydrogenase. However, the effect of imidacloprid was significantly weaker than that of chlorpyrifos. In conclusion, a novel brain-on-chip was developed in this study, which can be used to efficiently assess the drug neurotoxicity, pharmacodynamics, and disease mechanism by combining with a microtiterplate reader.

Estimating Size and Number Density of Three-Dimensional Particles Using Truncated Cross-Sectional Data

Abstract The need for estimating three-dimensional (3D) information based on two-dimensional (2D) images has been increasing in numerous fields. It is essential in quality assessment, quality control, and process optimization. However, all the existing methods have not considered the data truncation issue, which is commonly faced in metrology. This paper proposes a new statistical approach to infer size distribution and volume number density (VND) of 3D particles based on 2D cross-sectional images with data truncation considered. In order to estimate the size distribution, a linkage is established between 3D particles and 2D observations with the existence of data truncation. Subsequently, this paper derives the likelihood function of 2D observations and an efficient Monte Carlo expectation-maximization algorithm is developed to estimate the parameters of size distribution. In addition, an explicit relationship between the 3D and 2D particle number densities is established and leveraged to estimate the VND and volume fraction. The effectiveness of the proposed method is demonstrated through both simulation study and real case studies in metal additive manufacturing and metal-matrix nanocomposites manufacturing.

Development of a Camera Self-calibration Method for 10-parameter Mapping Function

Tomographic particle image velocimetry (PIV) is a widely used method that measures a three-dimensional (3D) flow field by reconstructing camera images into voxel images. In 3D measurements, the setting and calibration of the camera’s mapping function significantly impact the obtained results. In this study, a camera self-calibration technique is applied to tomographic PIV to reduce the occurrence of errors arising from such functions. The measured 3D particles are superimposed on the image to create a disparity map. Camera self-calibration is performed by reflecting the error of the disparity map to the center value of the particles. Vortex ring synthetic images are generated and the developed algorithm is applied. The optimal result is obtained by applying self-calibration once when the center error is less than 1 pixel and by applying self-calibration 2–3 times when it was more than 1 pixel; the maximum recovery ratio is 96%. Further self-correlation did not improve the results. The algorithm is evaluated by performing an actual rotational flow experiment, and the optimal result was obtained when self-calibration was applied once, as shown in the virtual image result. Therefore, the developed algorithm is expected to be utilized for the performance improvement of 3D flow measurements.

Degenerate Four-Wave Mixing in Phycoerythrin Dye-Doped Nanoparticles

We have generated a phase-conjugate (PC) wave from nanoparticles with a new microscopic system proposed. The microscope includes a confocal system with a degenerate four-wave mixing (DFWM) system, which plays a major role in generating the phase-conjugate wave to compensate phase distortion in the optical path toward targets. The proposed optical system detects feeble PC wave and imagines 3D particles while improving the inplane contrast resolution of the microscopic image.

A novel approach of random packing generation of complex-shaped 3D particles with controllable sizes and shapes

A novel approach of random packing generation of complex-shaped 3D particles with controllable sizes and shapes

Large-scale holographic particle 3D imaging with the beam propagation model.

We develop a novel algorithm for large-scale holographic reconstruction of 3D particle fields. Our method is based on a multiple-scattering beam propagation method (BPM) combined with sparse regularization that enables recovering dense 3D particles of high refractive index contrast from a single hologram. We show that the BPM-computed hologram generates intensity statistics closely matching with the experimental measurements and provides up to 9× higher accuracy than the single-scattering model. To solve the inverse problem, we devise a computationally efficient algorithm, which reduces the computation time by two orders of magnitude as compared to the state-of-the-art multiple-scattering based technique. We demonstrate the superior reconstruction accuracy in both simulations and experiments under different scattering strengths. We show that the BPM reconstruction significantly outperforms the single-scattering method in particular for deep imaging depths and high particle densities.

Photogrammetry based computational fluid dynamics of erosion of sand particles in water pipeline: Dynamic shape factors of 3D particles and minimization of erosion activity

Extraction and delivery of raw materials in engineering industries are inevitably polluted with sand particles that caused several flow assurance problems. Besides, designing a pipeline and estimating sand erosion is very complex in a multiphase flow due to several important factors needed to be considered, such as impact velocity, impact angle, size of particle, shape of particle, properties of particle, and properties of the target material. In this work, a modified E/CRC erosion model was developed to address the shape randomness in sand particles distribution. The photogrammetry technique was adopted to digitize sand particles into 3D models within the range of 400–500 μm. After quantitative and qualitative analysis, a database of shape factor for different spherical and roundness of sand particles was tabulated. Meanwhile, a high-fidelity simulation of a 90-degree elbow pipeline was conducted using the CFD-DEM method. Hydrodynamic characteristics of liquid flow, sand particle trajectories, the sensitivity of velocity, shape factor, and flow rate were comprehensively explored. Modification of the particle shape factor based on the generated database from photogrammetry resulted in overestimated erosion rate of the original E/CRC model with 124.4% accuracy improvement in predicting erosion rate on pipe bend at different collision angles. Lastly, through response surface methodology (RSM), a flow threshold condition was established to achieve minimum erosion damage.

Self-assembly of amphiphilic Janus spheres using the lattice Boltzmann method

We discuss the relevance of the lattice Boltzmann method (LBM) for soft matter alongside other simulation methods. We set up a popular problem of self-assembly of Janus spheres by combining native fluid and particle models in LBM with a simple amphiphilic pair potential. Thermal fluctuations and close contact corrections are also incorporated along with a novel periodic boundary condition for finite-sized 3D particles. Preliminary results of Janus sphere self-assembly are presented and compared with similar works in the literature. We also comment on the difficulties that LBM faces tackling such problems.

Fe-cation Doping in NiSe2 as an Effective Method of Electronic Structure Modulation towards High-Performance Lithium-Sulfur Batteries.

The commercialization of Li-S batteries is hindered by the shuttling of lithium polysulfides (LiPSs), the sluggish sulfur redox kinetics as well as the low sulfur utilization during charge/discharge processes. Herein, a free-standing cathode material was developed, based on Fe-doped NiSe2 nanosheets grown on activated carbon cloth substrates (Fe-NiSe2 /ACC) for high-performance Li-S batteries. Fe-doping in NiSe2 plays a key role in the electronic structure modulation of NiSe2 , enabling improved charge transfer with the adsorbed LiPSs molecules, stronger interactions with the active sulfur species and higher electrical conductivity. Effective promotion of the sulfur redox kinetics and enhanced sulfur utilization were achieved under high areal sulfur loadings. The stronger interactions with LiPSs together with the unique 3D structure of Fe-NiSe2 /ACC also induced the transformation of Li2 S2 /Li2 S growth from conventional 2D films to 3D particles, significantly eliminating the barriers of solid nucleation and growth during the phase transition of liquid LiPSs to solid Li2 S2 /Li2 S. With a high sulfur loading of 9.9 mg cm-2 , the Fe-NiSe2 /ACC cathode enabled a high area capacity of 9.14 mAh cm-2 with a low average decay of 0.11 % per cycle over 200 cycles at 0.1 C.

Widening UV–Vis absorption band by Cu doped ZnO and ZnO/CuO composite

ZnO, Cu doped ZnO, and CuO were synthesized by solvothermal method. Pure ZnO and CuO show two-dimensional (2D) particles with flake-like shape morphology and well-defined crystalline phases. The Cu incorporation slightly modifies the ZnO particle shape and size for low dopant. However, ZnO/CuO composite presents the co-existing morphologies with flake-like and small-rods shapes. The visible‐light absorption increases, directly proportional to the Cu content. However, ZnO/CuO composite shows wide ultraviolet–visible absorption between 200 and 1000 nm. The bandgap energy determined was 3.22 and 1.45 eV for ZnO and CuO, respectively.

Efficient Chitosan/Nitrogen-Doped Reduced Graphene Oxide Composite Membranes for Direct Alkaline Ethanol Fuel Cells.

Herein, we prepared a series of nanocomposite membranes based on chitosan (CS) and three compositionally and structurally different N-doped graphene derivatives. Two-dimensional (2D) and quasi 1D N-doped reduced graphene oxides (N-rGO) and nanoribbons (N-rGONRs), as well as 3D porous N-doped graphitic polyenaminone particles (N-pEAO), were synthesized and characterized fully to confirm their graphitic structure, morphology, and nitrogen (pyridinic, pyrrolic, and quaternary or graphitic) group contents. The largest (0.07%) loading of N-doped graphene derivatives impacted the morphology of the CS membrane significantly, reducing the crystallinity, tensile properties, and the KOH uptake, and increasing (by almost 10-fold) the ethanol permeability. Within direct alkaline ethanol test cells, it was found that CS/N rGONRs (0.07 %) membrane (Pmax. = 3.7 mWcm−2) outperformed the pristine CS membrane significantly (Pmax. = 2.2 mWcm−2), suggesting the potential of the newly proposed membranes for application in direct ethanol fuel cells.

DRPnet: automated particle picking in cryo-electron micrographs using deep regression

BackgroundIdentification and selection of protein particles in cryo-electron micrographs is an important step in single particle analysis. In this study, we developed a deep learning-based particle picking network to automatically detect particle centers from cryoEM micrographs. This is a challenging task due to the nature of cryoEM data, having low signal-to-noise ratios with variable particle sizes, shapes, distributions, grayscale variations as well as other undesirable artifacts.ResultsWe propose a double convolutional neural network (CNN) cascade for automated detection of particles in cryo-electron micrographs. This approach, entitled Deep Regression Picker Network or “DRPnet”, is simple but very effective in recognizing different particle sizes, shapes, distributions and grayscale patterns corresponding to 2D views of 3D particles. Particles are detected by the first network, a fully convolutional regression network (FCRN), which maps the particle image to a continuous distance map that acts like a probability density function of particle centers. Particles identified by FCRN are further refined to reduce false particle detections by the second classification CNN. DRPnet’s first CNN pretrained with only a single cryoEM dataset can be used to detect particles from different datasets without retraining. Compared to RELION template-based autopicking, DRPnet results in better particle picking performance with drastically reduced user interactions and processing time. DRPnet also outperforms the state-of-the-art particle picking networks in terms of the supervised detection evaluation metrics recall, precision, and F-measure. To further highlight quality of the picked particle sets, we compute and present additional performance metrics assessing the resulting 3D reconstructions such as number of 2D class averages, efficiency/angular coverage, Rosenthal-Henderson plots and local/global 3D reconstruction resolution.ConclusionDRPnet shows greatly improved time-savings to generate an initial particle dataset compared to manual picking, followed by template-based autopicking. Compared to other networks, DRPnet has equivalent or better performance. DRPnet excels on cryoEM datasets that have low contrast or clumped particles. Evaluating other performance metrics, DRPnet is useful for higher resolution 3D reconstructions with decreased particle numbers or unknown symmetry, detecting particles with better angular orientation coverage.