3D Particles Research Articles

Stochastic 3D reconstruction of cracked polycrystalline NMC particles using 2D SEM data

Abstract Li-ion battery performance is strongly influenced by the 3D microstructure of its cathode particles. Cracks within these particles develop during calendaring and cycling, reducing connectivity but increasing reactive surface, making their impact on battery performance complex. Understanding these contradictory effects requires a quantitative link between particle morphology and battery performance. However, informative 3D imaging techniques are time-consuming, costly and rarely available, such that analyses often have to rely on 2D image data. This paper presents a novel stereological approach for generating virtual 3D cathode particles exhibiting crack networks that are statistically equivalent to those observed in 2D sections of experimentally measured particles. Consequently, 2D image data suffices for deriving a full 3D characterization of cracked cathodes particles. Such virtually generated 3D particles could serve as geometry input for spatially resolved electro-chemo-mechanical simulations to enhance our understanding of structure-property relationships of cathodes in Li-ion batteries.

Study on Solid and Pore Structures of Borehole Municipal Solid Waste Samples by X-Ray CT Scanning

The microscale solid and pore structures of waste is crucial for the bio-hydro-mechanical behaviors of landfilled municipal solid waste (MSW). The quantitative analysis of the structural characteristics of MSW is still limited. In this study, borehole MSW samples at different depths (i.e., 0 m, 2.5 m, 5 m, 7.5 m, 10 m, and 12.5 m) were drilled from a landfill. The waste composition and basic physical properties of these samples were tested in laboratory. Solid and pore structural characteristics were studied through computed tomography (CT) analysis. The results indicate that the ratio of cellulose content to lignin content (i.e., C/L) decreased from 0.85 to 0.47 with increasing depth. For solid particles, two-dimensional (2D) particles constituted the greatest fraction (60.22~72.16%), which showed a decrease with increasing depth. The deeper sample tended to have more fine particles. For pores, the void ratio decreased from 1.68 to 1.10 with increasing depth, with more small pore channels. Meanwhile, the average pore diameter coefficient (λ) decreased from 0.209 to 0.190, the pore angle (θe) decreased from 29.6° to 17.8°, the tortuosity (τ) increased from 1.129 to 1.184, and the connectivity (ce) decreased from 12.0 to 4.1. These quantitative findings can further the understanding of fluid flow behaviors in landfilled waste.

Fine-scale hydrodynamics around St. John, U.S. Virgin Islands. Part II: variability in residence time in coastal bays

Coastal currents can vary dramatically in space and time, influencing advection and residence time of larvae, nutrients and contaminants in coastal environments. However, spatial and temporal variabilities of the residence time of these materials in coastal environments, such as coastal bays, are rarely quantified in ecological applications. Here, we use a particle tracking model built on top of the high-resolution hydrodynamic model described in Part 1 to simulate the dispersal of particles released in coastal bays around a key and model island study site, St. John, USVI without considering the impact of surface waves. Motivated to provide information for future coral and fish larval dispersal and contaminant spreading studies, this first step of the study toward understanding fine-scale dispersal variability in coastal bays aimed to characterize the cross-bay variability of particle residence time in the bays. Both three-dimensionally distributed (3D) and surface-trapped (surface) particles are considered. Model simulations show pronounced influences of winds, intruding river plumes, and bay orientation on the residence time. The residence times of 3D particles in many of the bays exhibit a clear seasonality, correlating with water column stratification and patterns of the bay-shelf exchange flow. When the water column is well-mixed, the exchange flow is laterally sheared, allowing a significant portion of exported 3D particles to re-enter the bays, resulting in high residence times. During stratified seasons, due to wind forcing or intruding river plumes, the exchange flows are vertically sheared, reducing the chance of 3D particles returning to the bays and their residence time in the bays. For a westward-facing bay with the axis aligned the wind, persistent wind-driven surface flows carry surface particles out of the bays quickly, resulting in a low residence time in the bay; when the bay axis is misaligned with the wind, winds can trap surface particles on the west coast in the bay and dramatically increase their residence time. The strong temporal and inter-bay variation in the duration of particles staying in the bays, and their likely role in larval and contaminant dispersal, highlights the importance of considering fine-scale variability in the coastal circulation when studying coastal ecosystems and managing coastal resources.

Impact of coastal currents and eddies on particle dispersion in the Baltic Sea: a Lagrangian approach to marine ecosystems

This study analyzes the dynamics of coastal currents and eddies in the Baltic Sea, focusing on their role in particle dispersion and ecosystem connectivity. Combining the General Estuarine Transport Model (GETM) and Lagrangian methods, it examines both single and paired particle dynamics, initially deployed in coastal areas of the Baltic Sea, for 2D and 3D simulations. Results show significant variability in transit times as it takes for 3D particles from the eastern coastal zone over 700 days to reach the central Gotland Basin, while those from the western coastal zone arrive 90 days faster. Longer transit times in the eastern coastal areas can influence the distribution of nutrients and pollutants, potentially exacerbating eutrophication, harmful algal blooms, and hypoxic conditions. In contrast, shorter transit times in the western Baltic accelerate dispersal, reducing localized impacts while increasing the spread of contaminants. In addition, (sub-)mesoscale eddies and vertical advection play a key role in particle transport, particularly in the northern Gotland Basin, where complex circulation slows movement and prolongs exposure to nutrients and pollutants. Moreover, relative dispersion analysis shows an initial nonlocal growth regime lasting up to 25 days in 3D but only 4–10 days in 2D, affecting connectivity between marine habitats. The subsequent ballistic regime, lasting 350 days in 2D but only 75 days in 3D, suggests enhanced mixing in 3D, influencing species recruitment and the dispersion of pollutants. 3D simulation results show that, depending on the region, absolute dispersion exhibits ballistic growth for the first 7 days, followed by a transition to a super-diffusion regime before normal diffusion sets in after 70–85 days. Furthermore, particle exit times vary also significantly, with those from the Gulf of Finland taking over 1,300 days to exit the Baltic Sea, compared to less than 700 days for particles from western regions. These findings highlight the role of physical processes such as eddies, coastal currents and mesoscale structures in shaping species dispersal, nutrient cycling, and pollution transport. Understanding these mechanisms is crucial for marine conservation, sustainable fisheries, and climate adaptation strategies in coastal marine protected areas (MPAs) of the Baltic Sea, particularly as circulation patterns evolve due to climate change.

Shape properties validation of virtual 3D particles created from AIMS 2D images for railway ballast DEM modeling

Shape properties validation of virtual 3D particles created from AIMS 2D images for railway ballast DEM modeling

MXene-Derived Potassium-Preintercalated Bilayered Vanadium Oxide Nanostructures for Cathodes in Nonaqueous K-Ion Batteries.

Bilayered vanadium oxides (BVOs) are promising cathode materials for beyond-Li-ion batteries due to their tunable chemistries and high theoretical capacities. However, the large size of beyond-Li+ ions limits electrochemical cycling and rate capability of BVO electrodes. Recent reports of MXene-derived BVOs with nanoscale flower-like morphology have shown improved electrochemical stability at high rates up to 5C in nonaqueous lithium-ion batteries. Here, we report how morphological stabilization can lead to improved rate capability in potassium-ion batteries (PIBs) through the synthesis and electrochemical characterization of MXene-derived K-preintercalated BVOs (MD-KVOs), which were derived from two V2CT x precursor materials prepared using two different etching protocols. We show that the etching conditions affect the surface chemistry of the MXene, which plays a role in the MXene-to-oxide transformation process. MXene derived from a milder etchant transformed into a nanoflower MD-KVO with two-dimensional (2D) nanosheet petals (KVO-DMAE) while a more aggressive etchant produced a MXene that transformed into a MD-KVO with one-dimensional (1D) nanorod morphology (KVO-CMAE). Electrochemical cycling of the produced MD-KVOs after drying at 200 °C under vacuum (KVO-DMAE-200 and KVO-CMAE-200) in PIBs showed that electrochemical stability of MD-KVO at high rates improved through the morphological stabilization of 2D particles combined with the control of interlayer water and K+ ion content. Structure refinement of KVO-DMAE-200 further corroborates the behavior observed during K+ ion cycling, connecting structural and compositional characteristics to the improved rate capability. This work demonstrates how proper synthetic methodology can cause downstream effects in the control of structure, chemical composition, and morphology of nanostructured layered oxide materials, which is necessary for development of future materials for beyond-Li-ion battery technologies.

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.

Capillary breakup of molybdenum disulfide particle-laden viscoelastic fluids

The exceptional mechanical, electrical, and optical properties of two-dimensional (2D) particles, such as molybdenum disulfide (MoS2), have driven their incorporation into functional inks for advanced printing techniques. In many of these processes, extensional deformation governs the separation of ink from the feeding system to the substrate, with capillary breakup significantly influencing print quality and resolution. This study investigates the filament thinning dynamics of MoS2 suspensions under varying electric fields aligned with the flow direction. The results reveal that increasing particle concentration accelerates the thinning rate in the inertio-capillary regime, leading to a shorter filament lifetime. Furthermore, the critical Ohnesorge number defining the transition between inertio-capillary and visco-capillary regimes is shown to depend on the particle concentration and electric field strength. Interestingly, the apparent extensional relaxation time decreases with increasing particle concentration, disappearing entirely at 0.50% and 0.75% w/w, before reemerging at higher concentrations in the absence of an electric field. These findings provide new insights into the complex interplay of particle concentration, electric fields, and extensional flow, with implications for optimizing 2D particle-laden inks in printing applications.

Enhancement and assessment of large vision models for 3D particle reconstruction from X-ray tomography

Three-dimensional (3D) particle reconstruction from X-ray micro-computed tomography (µCT) images is essential for digital twins and understanding the micromechanical behaviors of granular media. Despite large vision models (LVMs) having shown remarkable effectiveness across various domains, their application to accurate 3D particle reconstruction remains underexplored. This study proposes a systematic framework that enhances and leverages LVMs for the reconstruction of arbitrary 3D particles. The proposed framework includes three key steps: (1) enhancing LVMs with higher computational efficiency for two-dimensional (2D) label extraction, (2) mapping stacked 2D labels to 3D, and (3) extracting particle surfaces to generate 3D models. The enhanced approach is applied to reconstruct four distinct samples to validate feasibility. Six conventional and four lightweight LVMs are selected to explore the influence of model size and the number of prompts on reconstruction accuracy. The H-extreme watershed method is chosen as a benchmark for comparison. The results demonstrate that the enhanced framework can accurately reconstruct irregular and complex samples, such as carbonate sands, with a greater than 50% improvement in accuracy compared to the benchmark prediction. Additionally, the framework effectively reduces over- and under-segmentation errors, resulting in accurate reconstruction of microstructural characteristics. This versatile framework presents a promising alternative for investigating complex micromechanical mechanisms of granular media.

3D Quantum Gravity, Localization and Particles beyond Standard Model

3D Quantum Gravity, Localization and Particles beyond Standard Model

The Effect of Combining 2D and 3D Moving Particles on Hybrid Motion Graphics

Objectives: This study aimed to evaluate the level of complexity involved in integrating 2D and 3D techniques and evaluating the utility of this approach for designers in generating innovative motion designs,. It also aims to examine the design methodology employed in the creation of 2D and 3D motion graphics. Methods: The study used the descriptive- analytical approach. It also used the quantitative approach based on understanding the effect of combining 2D and 3D moving particles in designing new and innovative designs and determining the relevant terminology and concepts to create 2D and 3D moving graphics. A questionnaire was also distributed to 200 professional and specialized motion graphic designers to test the possibility of combining contemporary designs together. Results: The study’s quantitative results showed a positive and significant reality for employing the combination of the 2D and 3D elements in motion graphics with a high likelihood of implementation by the designer. The study results also showed a high average in producing cutting-edge styles via combining 2D and 3D elements in motion graphics. Conclusion: The study highlights the importance of conducting an accurate evaluation of the various variables to achieve the best results during the process of designing hybrid motion graphics which combine 2D and 3D particles. The study also states that the success of the combination process depends on the effectiveness of the visual style of the hybrid graphic designs and the effects, structure and motion in creating integrated images that effectively transmit the emotional story behind those designs.

Improving the performance of microbial fuel cell stacks via capacitive-hydrogel bioanodes

Improving the performance of microbial fuel cell stacks via capacitive-hydrogel bioanodes

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.

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.

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

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

Electronicand StructuralProperties of Thin IronOxide Films on CeO2

Modification of CeO2 (ceria) with 3d transitionmetals,particularly iron, has been proven to significantly enhance its catalyticefficiency in oxidation or combustion reactions. Although this phenomenonis widely reported, the nature of the iron–ceria interactionresponsible for this improvement remains debated. To address thisissue, we prepared well-defined model FeOx/CeO2(111) catalytic systems and studied their structureand interfacial electronic properties using photoelectron spectroscopy,scanning tunneling microscopy, and low-energy electron diffraction,coupled with density functional theory (DFT) calculations. Our resultsshow that under ultrahigh vacuum conditions, Fe deposition leads tothe formation of small FeOx clusters onthe ceria surface. Subsequent annealing results in the growth of largeamorphous FeOx particles and a 2D FeOx layer. Annealing in an oxygen-rich atmospherefurther oxidizes iron up to the Fe3+ state and improvesthe crystallinity of both the 2D layer and the 3D particles. Our DFTcalculations indicate that the 2D FeOx layer interacts strongly with the ceria surface, exhibiting structuralcorrugations and transferred electrons between Fe2+/Fe3+ and Ce4+/Ce3+ redox pairs. The novel2D FeOx/CeO2(111) phase mayexplain 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 othercatalytically active metals, in which the redox properties of the2D FeOx/CeO2(111) system areexploited to modulate the charge of the supported metals.

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

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