3D Particles Research Articles

Deep Learning-Based Reconstruction of 3D Morphology of Geomaterial Particles from Single-View 2D Images.

The morphology of particles formed in different environments contains critical information. Thus, the rapid and effective reconstruction of their three-dimensional (3D) morphology is crucial. This study reconstructs the 3D morphology from two-dimensional (2D) images of particles using artificial intelligence (AI). More than 100,000 particles were sampled from three sources: naturally formed particles (desert sand), manufactured particles (lunar soil simulant), and numerically generated digital particles. A deep learning approach based on a voxel representation of the morphology and multi-dimensional convolutional neural networks was proposed to rapidly upscale and reconstruct particle morphology. The trained model was tested using the three particle types and evaluated using different multi-scale morphological descriptors. The results demonstrated that the statistical properties of the morphological descriptors were consistent for the real 3D particles and those derived from the 2D images and the model. This finding confirms the model's validity and generalizability in upscaling and reconstructing diverse particle samples. This study provides a method for generating 3D numerical representations of geological particles, facilitating in-depth analysis of properties, such as mechanical behavior and transport characteristics, from 2D images.

Fabrication of metal‐organic frameworks macro-structures for adsorption applications in water treatment: A review

Metal-organic frameworks (MOFs) are emerging porous organic–inorganic hybrid materials composed of metal centers/clusters and various organic ligands, which are widely used in water and wastewater treatment applications due to their large specific surface area, high porosity, tunable chemical properties and abundant active sites. However, pure MOFs powders have limitations such as poor recovery, blocked pipelines and secondary contamination in practical water treatment processes. Enabling the magnetization of MOFs or immobilizing MOFs as macroscopic MOFs materials offers new opportunities to improve solid–liquid separation performances of powdered MOFs. Besides, macroscopic MOFs materials with outstanding adsorption performance and mechanical properties by the combining of 3D particles and 2D nanosheets MOFs with other substrates are widely used for water treatment. This paper provides a comprehensive review of recent advancements in MOFs macro-structures. We firstly summarized the synthesis strategies of magnetic MOFs composites and various MOFs macro-structures such as hydrogels/aerogels, membranes, beads/spheres, others composites, etc. which can be constructed by various methods, including in-situ growth, solvothermal method, direct mixing, self-assembly, layer-by-layer growth, etc. Innovative preparation strategies to improve the mechanical properties of MOFs macro-structures were highlighted. Next the article discusses its application for adsorptive removal of various pollutants (i.e., dyes, heavy metal ions, oils and organic pollutants) from water. Finally, the challenges of macroscopic MOFs materials and their practical applications are presented, while future research directions to overcome these existing limitations are also pointed out.

Electronic and Structural Properties of Thin Iron Oxide Films on CeO2.

Modification of CeO2 (ceria) with 3d transition metals, particularly iron, has been proven to significantly enhance its catalytic efficiency in oxidation or combustion reactions. Although this phenomenon is widely reported, the nature of the iron-ceria interaction responsible for this improvement remains debated. To address this issue, we prepared well-defined model FeOx/CeO2(111) catalytic systems and studied their structure and interfacial electronic properties using photoelectron spectroscopy, scanning tunneling microscopy, and low-energy electron diffraction, coupled with density functional theory (DFT) calculations. Our results show that under ultrahigh vacuum conditions, Fe deposition leads to the formation of small FeOx clusters on the ceria surface. Subsequent annealing results in the growth of large amorphous FeOx particles and a 2D FeOx layer. Annealing in an oxygen-rich atmosphere further oxidizes iron up to the Fe3+ state and improves the crystallinity of both the 2D layer and the 3D particles. Our DFT calculations indicate that the 2D FeOx layer interacts strongly with the ceria surface, exhibiting structural corrugations and transferred electrons between Fe2+/Fe3+ and Ce4+/Ce3+ redox pairs. The novel 2D FeOx/CeO2(111) phase may explain the enhancement of the catalytic properties of CeO2 by iron. Moreover, the corrugated 2D FeOx layer can serve as a template for the ordered nucleation of other catalytically active metals, in which the redox properties of the 2D FeOx/CeO2(111) system are exploited to modulate the charge of the supported metals.

Resolving Collisions in Dense 3D Crowd Animations

We propose a novel contact-aware method to synthesize highly-dense 3D crowds of animated characters. Existing methods animate crowds by, first, computing the 2D global motion approximating subjects as 2D particles and, then, introducing individual character motions without considering their surroundings. This creates the illusion of a 3D crowd, but, with density, characters frequently intersect each other since character-to-character contact is not modeled. We tackle this issue and propose a general method that considers any crowd animation and resolves existing residual collisions. To this end, we take a physics-based approach to model contacts between articulated characters. This enables the real-time synthesis of 3D high-density crowds with dozens of individuals that do not intersect each other, producing an unprecedented level of physical correctness in animations. Under the hood, we model each individual using a parametric human body incorporating a set of 3D proxies to approximate their volume. We then build a large system of articulated rigid bodies, and use an efficient physics-based approach to solve for individual body poses that do not collide with each other while maintaining the overall motion of the crowd. We first validate our approach objectively and quantitatively. We then explore relations between physical correctness and perceived realism based on an extensive user study that evaluates the relevance of solving contacts in dense crowds. Results demonstrate that our approach outperforms existing methods for crowd animation in terms of geometric accuracy and overall realism.

Wireless flexi-sensor using narrow band quasi colloidal 3D tin telluride (SnTe) for respiratory, environment, and proximity sensing

We developed a wireless and flexible humidity sensor based on three-dimensional (3D) narrow band quasi colloidal (NBQC) tin telluride (SnTe) material. Pristine SnTe granules were exfoliated into a homogeneous quasi colloidal (HQC) solution through mechanical liquid phase exfoliation (MLPE). Resulting HQC solution had a higher surface area with randomly shaped 3D NBQC SnTe particles that increased its humidity sensing ability by rendering more active sites and irregular pathways for interaction with water molecules. Fabrication of SnTe humidity sensor included highly conductive (2.5 μΩ/cm) and precise plasma sputtered gold (Au) inter-digital electrodes (IDEs) using a shadow mask on the flexible substrate followed by coating the thin film of 3D HQC SnTe. The SnTe humidity sensor showed variance in both capacitance and impedance with changing relative humidity (%RH). Impedance response of SnTe humidity sensor was more sensitive compared to its capacitance. We adjusted the operating frequency of our humidity sensor (1, 10, and 100 kHz) to maximize sensitivity, enhance accuracy, and reduce interference. The developed humidity sensor exhibited efficient performance, including a wide operating range (5–92 %RH), high sensitivity (85.6 kΩ/%RH), consistent stability (>48 days), and a fast response/recovery time (1.7/3.2 s). The narrow band gap of SnTe enabled strong interaction with water molecules, leading to a highly sensitive impedance response to %RH. To improve its mobility, portability, and measurability, the SnTe humidity sensor was made wireless for easy integration into a variety of devices. Our 3D NBQC SnTe humidity sensor showed its potential applications in environment monitoring, health monitoring, and proximity sensing.

3D Neutronic and Secondary Particles Analysis on YBa$_{2}$Cu$_{3}$O$_{7-\delta }$ Tapes for Compact Fusion Reactors

3D Neutronic and Secondary Particles Analysis on YBa$_{2}$Cu$_{3}$O$_{7-\delta }$ Tapes for Compact Fusion Reactors

Morphological analysis of ballast particles: Characterization and simplified analysis of particle morphology using imaging data

This paper introduces a novel algorithm for the rigorous characterization of three-dimensional (3D) particles, particularly for railway ballast. Degraded railway ballast must be replaced with fresh material for efficient functioning. This study examined the shape and form of degraded (used) ballast to guide future maintenance efforts. Laboratory-generated used ballast, obtained via the Los Angeles abrasion test, was compared to fresh ballast. Thirteen fundamental morphological parameters of fresh and used ballasts were investigated by utilizing the shape information obtained through 3D scanning. The algorithm efficiently processed datasets comprising multiple irregular particles and monitored the morphological characteristics of ballasts based on the shape of the particles. The trimesh library was imported for 3D processing, facilitating the mathematical calculation of diverse parameters using the developed algorithm. The algorithm also incorporated mechanisms for simultaneously storing parameters provided in various 3D configuration models. With the support of the trimesh library, a morphology analyzer was used to analyze various 3D model file formats, such as .stl, .obj, and csg. This method demonstrated its efficacy with reduced runtime and computation cost. Thus, the proposed algorithm has emerged as a valuable resource for researchers investigating the influence of ballast particle shape on the mechanical behavior of granular assemblies.

Real-Time 3D Tracking of Multi-Particle in the Wide-Field Illumination Based on Deep Learning.

In diverse realms of research, such as holographic optical tweezer mechanical measurements, colloidal particle motion state examinations, cell tracking, and drug delivery, the localization and analysis of particle motion command paramount significance. Algorithms ranging from conventional numerical methods to advanced deep-learning networks mark substantial strides in the sphere of particle orientation analysis. However, the need for datasets has hindered the application of deep learning in particle tracking. In this work, we elucidated an efficacious methodology pivoted toward generating synthetic datasets conducive to this domain that resonates with robustness and precision when applied to real-world data of tracking 3D particles. We developed a 3D real-time particle positioning network based on the CenterNet network. After conducting experiments, our network has achieved a horizontal positioning error of 0.0478 μm and a z-axis positioning error of 0.1990 μm. It shows the capability to handle real-time tracking of particles, diverse in dimensions, near the focal plane with high precision. In addition, we have rendered all datasets cultivated during this investigation accessible.

Enhanced mesoscale and macroscale parameters for accurate 3D shape characterization

This paper investigates the limitations of widely used 3D Wadell's roundness and true sphericity metrics for characterizing granular materials. It introduces two improved indices: the mesoscale interlocking index and the radial distance factor. The study involves diverse particles scanned using optical interferometry and X-ray computed tomography. A mean curvature method is employed for surface segmentation, and a computationally efficient 3D mesh simplification procedure is proposed. Python scripts are developed for surface segmentation, mesh simplification, and 3D shape characterization. The new indices are validated by assessing angular and rounded 3D particles and changes in the shape of aggregates through the Los Angeles abrasion test. The results demonstrate the ability of the proposed indices to differentiate visually similar particles with identical Wadell's roundness and true sphericity, showcasing their effectiveness in accurately describing particle shape. The statistical distribution of 3D particle curvatures offers an alternative method for understanding particles' shape and abrasion.

Is it possible to develop a digital twin for noise monitoring in manufacturing?

Noise monitoring is important in the context of manufacturing because it can help maintain a safe and healthy workspace for employees. Current approaches for noise monitoring in manufacturing are based on acoustic sensors, whose measured sound pressure levels (SPL) are shown as bar/curve charts and acoustic heat maps. In such a way, the noise emission and propagation process is not fully addressed. This paper proposes a digital twin (DT) for noise monitoring in manufacturing using augmented reality (AR) and the phonon tracing method (PTM). In the proposed PTM/AR-based DT, the noise is represented by 3D particles (called phonons) emitting and traversing in a spatial domain. Using a mobile AR device (HoloLens 2), users are able to visualize and interact with the noise emitted by machine tools. To validate the feasibility of the proposed PTM/AR-based DT, two use cases are carried out. The first use case is an offline test, where the noise data from a machine tool are first acquired and used for the implementation of PTM/AR-based DT with different parameter sets. The result of the first use case is the understanding between the AR performance of HoloLens 2 (frame rate) and the setting of the initial number of phonons and sampling frequency. The second use case is an online test to demonstrate the in-situ noise monitoring capability of the proposed PTM/AR-based DT. The result shows that our PTM/AR-based DT is a powerful tool for visualizing and assessing the real-time noise in manufacturing systems.

Firebrands Generated During WUI Fires: A Novel Framework for 3D Morphology Characterization

The goal of the present work is to establish a framework for firebrand morphology characterization. Central to this framework is the development of a simple firebrand shape classification model using multi-dimensional particle shape descriptors. This classification model is built upon a series of synthetically generated 3D particles whose shapes and sizes are chosen to be representative of actual firebrands typically encountered during vegetative and structural fuel burns. Principal Component Analysis (PCA) is applied to the synthetic dataset and used to structure the classification model. The model is then verified using 3D digital representations of real-world particles (firebrands collected during tree burns and unburned bark pieces from oak trees). The classification model, which will allow meaningful comparisons of firebrand morphological features by shape class, is expected to be gradually refined as more datasets are made available throughout the Wildland–Urban Interface (WUI) fire research community.

Silk Fibroin Materials: Biomedical Applications and Perspectives.

The golden rule in tissue engineering is the creation of a synthetic device that simulates the native tissue, thus leading to the proper restoration of its anatomical and functional integrity, avoiding the limitations related to approaches based on autografts and allografts. The emergence of synthetic biocompatible materials has led to the production of innovative scaffolds that, if combined with cells and/or bioactive molecules, can improve tissue regeneration. In the last decade, silk fibroin (SF) has gained attention as a promising biomaterial in regenerative medicine due to its enhanced bio/cytocompatibility, chemical stability, and mechanical properties. Moreover, the possibility to produce advanced medical tools such as films, fibers, hydrogels, 3D porous scaffolds, non-woven scaffolds, particles or composite materials from a raw aqueous solution emphasizes the versatility of SF. Such devices are capable of meeting the most diverse tissue needs; hence, they represent an innovative clinical solution for the treatment of bone/cartilage, the cardiovascular system, neural, skin, and pancreatic tissue regeneration, as well as for many other biomedical applications. The present narrative review encompasses topics such as (i) the most interesting features of SF-based biomaterials, bare SF's biological nature and structural features, and comprehending the related chemo-physical properties and techniques used to produce the desired formulations of SF; (ii) the different applications of SF-based biomaterials and their related composite structures, discussing their biocompatibility and effectiveness in the medical field. Particularly, applications in regenerative medicine are also analyzed herein to highlight the different therapeutic strategies applied to various body sectors.

Oil-in-oil droplet stability dependence on dimensions of 2D Pickering particles of controlled size

Control in 2D polymer particle size results in control over the droplet diameter and stability in oil-in-oil Pickering emulsions.

Clay micromechanics: Numerical modelling of electrical double-layer interactions to develop particle-based models for clay

Discrete element modelling of clays requires defining the interaction energy between two particles. The Derjaguin, Landau, Verwey and Overbeek (DLVO) theory combining the effect of the van der Waals forces and the Coulombic forces due to the double layer of counterions provides a widely accepted framework to characterise the pair potential energy. Solutions of the Poisson-Boltzmann (PB) equation to quantify the Coulombic forces are only available for the case of infinitely extended and uniformly charged facing plates (1D conditions). However, these assumptions are not representative of a clay particle system. Particles should be represented by platelets of finite size and finite thickness, with different charges between the edge and the basal planes. This paper addresses the problem of deriving the Coulombic interaction forces for plates of finite size and thickness in 3D configuration by solving the Poisson-Boltzmann equation numerically via the Finite Element Method (FEM). It is shown that 2D particles (plates of infinitesimal thickness) provide an adequate representation of Coulombic interaction as long as the particles are uniformly charged. The advantage of 2D particles is to reconcile numerical modelling with analytical solutions available in the literature. The use of 2D particles is questionable when considering different charges between basal planes and edges.

Alginate-CaCO3 hybrid colloidal hydrogel with tunable physicochemical properties for cell growth

Biomaterials composed of food polysaccharides are of great interest for future biomedical applications due to their great biocompatibility, tunable mechanical properties, and complex architectural designs that play a crucial role in the modulation of cell adhesion and proliferation. In this work, a facile approach was designed to obtain novel 3D alginate-CaCO3 hybrid hydrogel particles in situ. Controlling the gel concentration from 3 to 20 mg·mL−1 allows us to control the alginate-CaCO3 hydrogel particles' size and density (size variation from 1.86 to 2.34 mm and density from 1.22 to 1.29 mg/mm3). This variable also has a considerable influence on the mineralization process resulting in CaCO3 particles with varied sizes and amounts within the hydrogel beads. The measurements of Young's modulus showed that the inclusion of CaCO3 particles into the alginate hydrogel improved its mechanical properties, and Young's modulus of these hybrid hydrogel particles had a linear relationship with alginate content and hydrogel particle size. Cell experiments indicated that alginate-CaCO3 hybrid hydrogel particles can support osteoblastic cell proliferation and growth. In particular, the amount of hydroxyapatite deposition on the cell membrane significantly increased after the treatment of cells with hybrid hydrogel particles, up to 20-fold. This work offers a strategy for constructing inorganic particle-doped polysaccharide hybrid hydrogel scaffolds that provide the potential to support cell growth.

Contribution of the Fe-rich phase particles to the high temperature mechanical behaviour of an Al-Cu-Fe alloy

The high temperature strengthening mechanisms in Al-Cu-Fe aluminium alloy was analysed basis on a comprehensive microstructural study of the as-cast alloy and mechanical testing results. An approach which extracts the specific contribution of solid solution atoms, precipitates, and Fe-rich particles to the creep strengthening was utilized. Additionally, the Shear Lag model, was employed to account for the contribution to strengthening of the Fe-rich intermetallic particles through a load transfer mechanism. For this study, an as-cast Al-Cu alloy, which does not contain Fe impurities, has been also used for comparative purposes. The findings suggest that the large and 3D interconnected Fe-rich phase particles (Al6(MnFe) and α-Fe) generate the main strengthening effect at high temperature through a load transfer mechanism, whereas the contribution of the nanometric Al2CuMg precipitates were very limited.

Empirical Shape-Based Estimation of Settling Microplastic Particles Drag Coefficient

Microplastics (MP) are found in nature in various forms. The transport and settling process of MP particles in water streams is highly influenced by particle size and shape. In order to investigate the impact of particle shape, an experimental analysis of settling behavior of flat square particles and 3D cubic particles in flowing water was conducted. The analysis is based on tracking the settling trajectories of particles of various size and using this information for calculating the drag coefficient cd in relation to the Reynolds number Re in the range of 80 to 280 for cubes and 50 to 110 for flat particles. The results were correlated with two shape parameters, sphericity ϕ and a newly proposed shape descriptor dimensionality δ. The final output is a model for particle shape-based cd estimation, mainly serving as an illustration of the potential of the proposed methodology.

Predicting 3D particles shapes based on 2D images by using convolutional neural network

In order to accurately determine the three-dimensional (3D) shape of granular particles, one laborious approach is to employ X-ray microtomography and image processing techniques. As an alternative, in this study a 3D convolutional neural network (3D-CNN) methodology is presented to generate realistic 3D models of particles from a number of distinct two-dimensional (2D) projections. Initially, 3D synthetic particles with the targeted statistical distributions of the relevant geometric parameters, such as sphericity, elongation, aspect ratio, and flatness, were randomly generated using Fourier shape descriptors (FSDs). The database based on the statistical distributions of geometric parameters of Avicel® PH 200, obtained through dynamic image analysis. Following that, projections from multiple angles of 2D particle images were extracted for model training. By using a convolutional neural network (CNN) the shape of the particles was predicted, which involves the following sequential steps: a) model training on 2D images; b) a decoder is built to generate the 3D voxel model after an encoder extracts the properties of 2D images; c) a marching cube algorithm that converts the particle 3D voxel model into a mesh model; d) the accuracy of the prediction is evaluated by comparing the geometric parameters of the prediction models that were generated by using FSDs. The methodology was validated using a database of 3D synthetic particles with targeted statistical distributions of geometric parameters, such as sphericity, elongation, aspect ratio, and flatness. The mean absolute percentage error (MAPE) of the AI model for these parameters was 2.24%, 7.01%, 10.93%, and 7.67%, respectively. The 3D-CNN methodology has the potential for broad applications in various fields, such as rock and mineral analysis, battery materials, pharmaceuticals, and space exploration, offering a practical and affordable solution for shape analysis of relevant materials. It can significantly improve the efficiency and accuracy of numerical simulations and analyses, leading to new insights and innovations in a wide range of fields.

Fabrication of Plasmonic Microcubes by Laser Ablation of Au‐Nanoparticles‐Loaded Acrylate

The fabrication of plasmonic 3D microstructures is still a challenge. In this study, the fabrication of gold‐nanoparticle (Au‐NP)‐filled acrylate‐based micron‐scale cuboids forming plasmonic 3D particles is investigated. UV curable acrylate is mixed with HAuCl4 and spin‐coated onto a fused silica wafer. UV light and heat treatment of the layer result in polymerization of the acrylate and formation of Au‐NPs with a plasmonic resonance ranging from 535 to 550 nm. The plasmonic resonance wavelength decreases slightly with increasing UV dose. The resonance wavelength can be shifted by a subsequent annealing process. The decreasing resonance wavelength with increasing annealing temperature is discussed as a result of the increasing Au‐NPs. The size and the distribution of the resulting gold particles in the acrylate matrix is investigated by transmission electron microscopy. The Au‐NPs‐filled acrylate layer is laser structured using a UV‐femtosecond laser, which allows the fabrication of adjustable micro‐cuboids with edge lengths down to 15 μm. The laser‐cut micro‐cuboids are transferred into isopropanol and the plasmonic properties of the structures in the liquid are successfully demonstrated. The presented concept allows easy and large‐scale fabrication of plasmonic microstructures with independently adjustable plasmonic properties and microstructural size and shape.

Synthetic modelling of railway trackbed for improved understanding of ground penetrating radar responses due to varying conditions

AbstractGround penetrating radar (GPR) is a commonly used tool for railway trackbed inspection due to its ability to collect information about subsurface materials at high resolution and high speed. Although GPR recording systems allow for the collection of vast quantities of data (hundreds of kilometres per day), accurate ground truth information is difficult to obtain. Models of trackbed can be used to generate synthetic radargrams to provide a better understanding and predictability of GPR responses to a wide range of trackbed conditions. In this research, we produced models of ballast using randomly shaped 3D particles, with a range of particle size distributions to represent various stages of ballast breakdown. Additionally, void spaces are partially filled with a constant dielectric material to represent ballast contamination. We used gprMax to simulate the GPR response for a 2 GHz horn antenna over the trackbed models. These simulations resulted in radargrams that are visually indistinct from real recorded data in known conditions. These radargrams, along with their formative models, have provided valuable insights into how variations in trackbed conditions can impact GPR data.