Large 3D Research Articles

Stack growth of wafer-scale van der Waals superconductor heterostructures.

Two-dimensional (2D)van der Waals (vdW) heterostructures have attracted considerable attention in recent years1-5. The most widely used method of fabrication is to stack mechanically exfoliated micrometre-sized flakes6-18, but this process is not scalable for practical applications. Despite thousands of 2D materials being created, using various stacking combinations1-3,19-21, hardly any large 2D superconductors can be stacked intact into vdW heterostructures, greatly restricting the applications for such devices. Here we report a high-to-low temperature strategy for controllably growing stacks of multiple-layered vdW superconductor heterostructure (vdWSH) films at a wafer scale. The number of layers of 2D superconductors in the vdWSHs can be precisely controlled, and we have successfully grown 27double-block, 15 triple-block, 5 four-block and 3 five-block vdWSH films(where one block represents one 2D material). Morphological, spectroscopic and atomic-scale structural analyses reveal the presence of parallel, clean and atomicallysharp vdW interfaces on a large scale, with very little contamination between neighbouring layers. The intact vdW interfaces allow us to achieve proximity-induced superconductivity and superconducting Josephson junctions on a centimetre scale. Our process for making multiple-layered vdWSHs can easily be generalized to other situations involving 2D materials, potentially accelerating the design of next-generation functional devices and applications22-24.

Lewis base anchored DhaTph covalent-organic framework for effective capture of thorium(IV) ion

Capture of radioactive thorium (Th) ion from nuclear wastewater is highly necessary. However, current adsorbents show drawback in either adsorption capacity or adsorption rate. In this study, a DhaTph (Dha, 2,5-dihydroxyterephthalaldehyde; Tph, tetra-(p-aminophenyl)porphyrin) covalent-organic framework (COF) with Lewis base sites (C = N and –OH) was applied to capture Th(IV) ions from aqueous solution. DhaTph has a large (d, ∼2 nm) and transparent 1D channel, which makes the active Lewis bases interact with Th(IV) ion easily. Benefited from that, DhaTph exhibits a larger adsorption capacity of 525.0 mg g−1 and rapid adsorption speed (90 min for equilibrium) simultaneously. Besides, this COF owns superior adsorption for Th(IV) ion than various rare earth ions and some other common ions. The systematical characterization and theoretical calculation indicate that the strong chemical chelation interaction from C = N and –OH dominantly contributes to the effective capture of Th(IV) ion. Thus, our work indicates that DhaTph can serve as a potential adsorbent for selective capture of Th(IV) ion, and provides a guideline for designing effective COFs-based adsorbents. We expect this work will promote the application of highly stable COFs in the field of Th(IV) ion capture.

2DeteCT - A large 2D expandable, trainable, experimental Computed Tomography dataset for machine learning

Recent research in computational imaging largely focuses on developing machine learning (ML) techniques for image reconstruction, which requires large-scale training datasets consisting of measurement data and ground-truth images. However, suitable experimental datasets for X-ray Computed Tomography (CT) are scarce, and methods are often developed and evaluated only on simulated data. We fill this gap by providing the community with a versatile, open 2D fan-beam CT dataset suitable for developing ML techniques for a range of image reconstruction tasks. To acquire it, we designed a sophisticated, semi-automatic scan procedure that utilizes a highly-flexible laboratory X-ray CT setup. A diverse mix of samples with high natural variability in shape and density was scanned slice-by-slice (5,000 slices in total) with high angular and spatial resolution and three different beam characteristics: A high-fidelity, a low-dose and a beam-hardening-inflicted mode. In addition, 750 out-of-distribution slices were scanned with sample and beam variations to accommodate robustness and segmentation tasks. We provide raw projection data, reference reconstructions and segmentations based on an open-source data processing pipeline.

VoxelScape: Large Scale Simulated 3D Point Cloud Dataset of Urban Traffic Environments

Having a profound understanding of the surrounding environment is considered one of the crucial tasks for the reliable operation of future self-driving cars. Light Detection and Ranging (LiDAR) sensor plays a critical role in achieving such understanding due to its capability to perceive the world in 3D. Similar to 2D perception tasks, current state-of-the-art methods in 3D perception tasks rely on deep neural networks (DNNs). However, the performance of 3D perception tasks, specially point-wise semantic segmentation, is not on par with their 2D counterparts. One of the main reasons is the lack of publicly available labelled 3D point cloud datasets (PCDs) from 3D LiDAR sensors. In this work, we are introducing the VoxelScape dataset, a large-scale simulated 3D PCD with 100K annotated point cloud scans. The annotations in the VoxelScape dataset include both point-wise semantic labels and 3D bounding box labels. Additionally, we used a number of baseline approaches to validate the transferability of VoxelScape to real 3D PCD for two challenging 3D perception tasks. The promising results have shown that training DNNs on VoxelScape boosted the performance of the 3D perception tasks on the real PCD. Furthermore, we are also releasing the proposed data generation pipeline for the research community to facilitate realistic simulation of 3D LiDAR point cloud data for different scenarios beyond those covered in our VoxelScape dataset. The VoxelScape dataset and the corresponding LiDAR simulation codes are publicly available at https://voxel-scape.github.io/dataset

3D-printed scaffolds with perfusable channels for low-cost large construct 3D cell culture

Abstract This study presents a novel approach for threedimensional (3D) cell culture using a two-component system consisting of a 3D-printed scaffold structure combined with a perfusion channel. A polymeric scaffold structure with an overall size of 9 mm x 9 mm x 1 mm composed of a cubic lattice with a web thickness of 200 μm and pore size of 600 μm was 3D-printed using a hot UV-stereolithography (SLA) system. The perfusion channel with an inner diameter of 800 μm and channel wall pores of 300 μm for cell culture medium supply was 3D-printed with the same system and material. Scaffolds were investigated with respect to the printing accuracy by digital microscopy. Cytotoxicity of the materials was assessed using MTT-assay and Live/Dead staining. Scaffold were subsequently seeded with 3T3- fibroblasts within a fibrin-based hydrogel and then conditioned either statically or under passive perfusion using a hydrostatic pressure driven flow system. The results show that the scaffold structure and perfusion channel can be produced with high accuracy and stability allowing a supply of nutrient and oxygen via perfusion channel to the cells within the scaffold. This approach has potential for nutrient supply within larger constructs for tissue engineering and regenerative medicine applications.

Large-scale hollow-core 3D printing (HC3DP): A polymer 3D printing technology for large-scale ultralightweight components

Material extrusion (ME) has become a popular 3D printing method for large-scale applications such as in the automotive, naval, aerospace, and architecture sectors. It allows the production of complex, customized parts without the need of labour-intensive molds. The increasing need to produce larger and larger parts has been addressed with the introduction of large pellet extrusion systems to increase the extrusion rate. With these extruders the production time is not limited by the extrusion rate of the extruder (kg/h) but by the time the material needs to cool down before the next layer can be printed consecutively. Furthermore, larger bead dimensions result in increased material usage and, therefore, might affect the environmental impact of the printed element. In this study, a method for large extrusion 3D printing based on hollow beads is proposed, to address the limitations of speed, cooldown and maximize the extrusion rate of thermoplastic 3D printing. This paper introduces Hollow-Core 3D printing (HC3DP) and compares it to off-the shelf large-scale pellet extruders and their maximum achievable extrusion rate for a given geometry. A comparative analysis of cooldown characteristics between conventional thermoplastic extrusion 3D printing and HC3DP validates that the latter increases the maximum possible extrusion rate. Additionally, the successful 3D printing of large-scale beads with multiple polymer feedstocks is demonstrated. Then the data associated with printing speed and material usage is presented and discussed in comparison with thermoplastic and other large-scale 3D printing methods. The fabrication of large-scale hollow-core beads at a dimension of 24×20mm is showcased with an extrusion rate of 7250 mm3/s (1308 material, 5941 air). HC3DP for large-scale applications is validated through the production of 2-meter tall cylinders with a diameter of 400 mm in less than two hours. Furthermore, bespoke die geometries that subdivide the inside of hollow 3D-printed beads are presented. To successfully print those, the concept of differentiated internal air pressure per print point is introduced. Finally, 3PT bending tests are conducted to compare different die geometries. In conclusion, this study presents a printing method that highlights the benefits of hollow core 3D printing for large-scale applications, showcasing its positive impact on both the printing process and resulting part properties. By addressing the limitations of traditional approaches, HC3DP pushes polymer 3D printing into a scale relevant for architecture and construction, offering similar extrusion rates as concrete 3DP.

Diffusion–convection model of runaway electrons due to large magnetohydrodynamic perturbations in post-thermal quench plasmas

Systematic test particle tracing simulations for runaway electrons (REs) are performed for six post-thermal quench equilibria from DIII-D and ITER, where large scale, kink-like n = 1 (n is the toroidal mode number) magnetohydrodynamic (MHD) instabilities are found. The modeled particle guiding center orbits allow extraction of the effective diffusion–convection coefficients of REs in the presence of large three-dimensional (3D) perturbations up to 10% of the equilibrium toroidal field. With a fixed spatial distribution of the field perturbation, the RE transport coefficients along the plasma radial coordinate track reasonably well with the surface-averaged perturbation level. A substantial variation in the value of the transport coefficients—by three orders of magnitude in most cases, however, occurs with varying launching location of REs along the plasma radius. Large 3D perturbations almost always lead to comparable diffusion and convection processes, meaning that diffusion alone is insufficient to describe the particle motion. At lower (but still high) level of perturbation, the RE convection is found to be dominant over diffusion. A similar observation is made when the perturbation is too strong. In the presence of large perturbation, the dependence of the RE transport on the particle energy is sensitive to the spatial distribution of the perturbation. Based on numerically obtained RE transport coefficients, an analytic fitting model is proposed to quantify the particle diffusion and convection processes due to large MHD events in post-thermal quench plasmas. The model is shown to reasonably well reproduce the direct test particle tracing results for the RE loss fraction and can, thus, be useful for incorporating into other kinetic RE codes in order to simulate the RE beam evolution in the presence of large 3D perturbations.

Remediation of multilayer soils contaminated by heavy chlorinated solvents using biopolymer-surfactant mixtures: Two-dimensional flow experiments and simulations.

To assess the efficiency of remediating dense non-aqueous phase liquids (DNAPLs), here heavy chlorinated solvents, through injection of xanthan solutions with or without surfactant (sodium dodecylbenzenesulfonate: SDBS), we conducted a comprehensive investigation involving rheological measurements, column (1D) and two-dimensional (2D) sandbox experiments, as well as numerical simulations on two-layers sand packs. Sand packs with grain sizes of 0.2-0.3mm and 0.4-0.6mm, chosen to represent the low and high permeable soil layers respectively, were selected to be representative of real polluted field. The rheological analysis of xanthan solutions showed that the addition of SDBS to the solution reduced its viscosity due to repulsive electrostatic forces and hydrophobic interactions between the molecules while preserving its shear-thinning behavior. Results of two-phase flow experimentsdepicted that adding SDBS to the polymer solution led to a reduced differential pressure along the soil and improvements of the DNAPL recovery factor of approximately 0.15 and 0.07 in 1D homogeneous and 2D layered systems, respectively. 2D experiments revealed that the displacement of DNAPL in multilayer zones was affected by permeability difference and density contrast in a heterogeneous soil. Simulation of multiphase flow in a multilayered system was performed by incorporating non-Newtonian properties and coupling the continuity equation with generalized Darcy's law. The results of modeling and experiments are very consistent. Numerical simulations showed that for an unconfined soil, the recovery of DNAPL by injection of xanthan solution can be reduced for more than 50%. In a large 2D experimental system, the combination of injecting xanthan with blocking the contaminated zone led to a promising remediation of DNAPL-contaminated layered zones, with a recovery of 0.87.

Artificial Intelligence–Triaged 3-Dimensional Pathology to Improve Detection of Esophageal Neoplasia While Reducing Pathologist Workloads

Early detection of esophageal neoplasia via evaluation of endoscopic surveillance biopsies is the key to maximizing survival for patients with Barrett’s esophagus, but it is hampered by the sampling limitations of conventional slide-based histopathology. Comprehensive evaluation of whole biopsies with 3-dimensional (3D) pathology may improve early detection of malignancies, but large 3D pathology data sets are tedious for pathologists to analyze. Here, we present a deep learning-based method to automatically identify the most critical 2-dimensional (2D) image sections within 3D pathology data sets for pathologists to review. Our method first generates a 3D heatmap of neoplastic risk for each biopsy, then classifies all 2D image sections within the 3D data set in order of neoplastic risk. In a clinical validation study, we diagnose esophageal biopsies with artificial intelligence–triaged 3D pathology (3 images per biopsy) vs standard slide-based histopathology (16 images per biopsy) and show that our method improves detection sensitivity while reducing pathologist workloads.

Multi-Resolution 3D Rendering for High-Performance Web AR.

In the context of web augmented reality (AR), 3D rendering that maintains visual quality and frame rate requirements remains a challenge. The lack of a dedicated and efficient 3D format often results in the degraded visual quality of the original data and compromises the user experience. This paper examines the integration of web-streamable view-dependent representations of large-sized and high-resolution 3D models in web AR applications. The developed cross-platform prototype exploits the batched multi-resolution structures of the Nexus.js library as a dedicated lightweight web AR format and tests it against common formats and compression techniques. Built with AR.js and Three.js open-source libraries, it allows the overlay of the multi-resolution models by interactively adjusting the position, rotation and scale parameters. The proposed method includes real-time view-dependent rendering, geometric instancing and 3D pose regression for two types of AR: natural feature tracking (NFT) and location-based positioning for large and textured 3D overlays. The prototype achieves up to a 46% speedup in rendering time compared to optimized glTF models, while a 34 M vertices 3D model is visible in less than 4 s without degraded visual quality in slow 3D networks. The evaluation under various scenes and devices offers insights into how a multi-resolution scheme can be adopted in web AR for high-quality visualization and real-time performance.

Fast Multigrid Simulations of Pinning in REBCO With Highly Resistive Nanorods

A geometric multigrid method is applied to the frozen-field time-dependent Ginzburg–Landau equations for anisotropic superconductors with spatially varying material properties. This reduces the simulation time for critical current determination in large 3D systems by up to two orders of magnitude compared with a single grid. Critical current density calculations are presented for a model of REBCO tape with nanorod artificial pinning centers. The model consists of an anisotropic superconducting matrix with embedded nanorods which are treated as isotropic highly resistive cylinders. The effects of varying the density, splay, and conductivity of the nanorods are considered. Increasing the resistivity of the nanorods profoundly affects the critical current density in high fields, where current paths are percolative in the vicinity of matrix-pin interfaces and persist well above the upper critical field of the superconducting matrix.

Global Stability Analysis of Full-Aircraft Transonic Buffet at Flight Reynolds Numbers

Fully three-dimensional (3D) global stability analysis (GSA) is performed on the NASA Common Research Model at turbulent transonic buffet conditions. The framework here proposed is based on a Jacobian-free approach that enables GSA on large 3D grids, making this the first stability study on a full-aircraft at typical flight Reynolds numbers. The Reynolds-averaged Navier–Stokes solutions compare reasonably well with the available experiments and are used as base flows for the stability analyses. GSA is first performed at wind tunnel Reynolds number conditions, and a buffet-cell mode localized in the wing outboard region is found to be responsible for the onset. When the side-of-body (SOB) separation becomes larger at higher angles of attack, two additional modes are detected: a high-frequency mode localized in the SOB region and a low-frequency long-wavelength buffet-cell mode that may represent the link with the shock-oscillation instability found in two-dimensional airfoils. The existence of the buffet-cell mode is confirmed at flight Reynolds numbers. However, due to the presence of large SOB separation at the onset angle of attack, this mode is distributed along the whole wing and an SOB separation mode also appears. As well as characterizing buffet on industry-relevant geometries and flow conditions, this study proves that the proposed GSA framework is feasible for large 3D numerical grids and can represent a useful tool for buffet onset prediction during design and certification phases of commercial aircraft.

Grain boundary effects in high-temperature liquid-metal dealloying: a multi-phase field study

A multi-phase field model is employed to study the microstructural evolution of an alloy undergoing liquid dealloying, specifically considering the role of grain boundaries. A semi-implicit time-stepping algorithm using spectral methods is implemented, which enables simulating large 2D and 3D domains over long time scales while still maintaining a realistic interfacial thickness. Simulations reveal a mechanism of coupled grain–boundary migration to maintain equilibrium contact angles with the topologically complex solid–liquid interface, which locally accelerates diffusion-coupled growth of a liquid channel into the precursor. This mechanism asymmetrically disrupts the ligament connectivity of the dealloyed structure in qualitative agreement with published experimental observations. The grain boundary migration-assisted corrosion channels form even for precursors with small amounts of the dissolving alloy species, below the parting limit. The activation of this grain boundary dealloying mechanism depends strongly on grain boundary mobility.

Crosstalk-free large aperture electromagnetic 2D micromirror for LiDAR application

This paper presents a novel design of a 2D electromagnetic micromirror without crosstalk. The proposed micromirror uses a flexible printed circuit board (FPCB) and four layers of coils embedded in the polyimide layers. The insulated layers of the coil allow for independent actuation of the mirror plate to rotate about two orthogonal axes. The diamond shaped micromirror uses a hyperbola-shaped magnetic field on the coils under the mirror plate and a 45-degrees magnetic field on the coils embedded in the FPCB frame to eliminate the mechanical crosstalk. Finite element analysis was used to predict the novel 2D micromirror’s behavior. The novel 2D micromirror prototype is used in scanning LiDAR, The results indicate that the crosstalk-free pattern yielded significantly clearer results, particularly for detecting object boundaries and reducing barrel distortion. The experimental test has verified the novel crosstalk-free 2D micromirror working principle and showed good scanning quality: no crosstalk and an improvement in the horizontal field of view up to 19% But with the cost of reducing the vertical field of view by up to 12%. The novel 2D micromirror prototype has a large aperture of 19 × 19 mm2, which is very suitable for coaxial scanning LiDAR.

A 64k pixel CMOS-DEPFET module for the soft X-rays DSSC imager operating at MHz-frame rates

The 64k pixel DEPFET module is the key sensitive component of the DEPFET Sensor with Signal Compression (DSSC), a large area 2D hybrid detector for capturing and measuring soft X-rays at the European XFEL. The final 1-megapixel camera has to detect photons with energies between 250,text{eV} and 6,text{keV}, and must provide a peak frame rate of 4.5,text{MHz} to cope with the unique bunch structure of the European XFEL. This work summarizes the functionalities and properties of the first modules assembled with full-format CMOS-DEPFET arrays, featuring 512,times ,128 hexagonally-shaped pixels with a side length of 136 μm. The pixel sensors utilize the DEPFET technology to realize an extremely low input capacitance for excellent energy resolution and, at the same time, an intrinsic capability of signal compression without any gain switching. Each pixel of the readout ASIC includes a DEPFET-bias current cancellation circuitry, a trapezoidal-shaping filter, a 9-bit ADC and a 800-word long digital memory. The trimming, calibration and final characterization were performed in a laboratory test-bench at DESY. All detector features are assessed at 18,^{circ }text{C}. An outstanding equivalent noise charge of 9.8e−rms is achieved at 1.1-MHz frame rate and gain of 26.8 Analog-to-Digital Unit per keV (,text {ADU}/text{keV}). At 4.5,text{MHz} and 3.1,,text {ADU}/text{keV}, a noise of 25.5 e−rms and a dynamic range of 26,text{k}text {e}^{-} are obtained. The highest dynamic range of 1.345,text{M}text {e}^{-} is reached at 2.25,text{MHz} and 1.6,text {ADU}/text{keV}. These values can fulfill the specification of the DSSC project.

A rigorous random field-based framework for 3D stratigraphic uncertainty modelling

Sustainable development goals for site-specific and customized design have brought needs to assess and quantify the stratigraphic uncertainty in three-dimensional (3D) modelling. The interpretation of uncertainty distribution with limited boreholes has always been challenging in 3D space, which is curial in engineering applications such as borehole sampling. In this study, we propose a rigorous random field-based framework to model the 3D stratigraphic uncertainty distribution with improved physical interpretation. Rather than offering constants or physically ambiguous models for Scales of Fluctuation (SoFs) as previous research, the framework adopts a series of SoFs calibrated by fitting stratum characteristics, to cope with local site-specificity in large geological sites. To evaluate the performance of the proposed framework, hypothetical cases are firstly used to demonstrate its applications in both 2D and 3D scenarios. The effectiveness of the proposed model is verified by borehole layout scheme optimization. Then, the proposed framework is applied to the 4 km-long geological site of the immersed tunnel of Hong Kong-Zhuhai-Macao Bridge using 246 boreholes, which is to date the first study on large 3D stratigraphic uncertainty modelling. Finally, a comprehensive discussion is presented for correct physical interpretation of SoFs, comparative studies and determination of important parameters, aiming at evaluating the compatibility in all scenarios. The novel method is proved to be effective in 3D uncertainty modelling for geological stratigraphy with improved physical interpretability, which provides guidelines for underground design.

Design and fabrication of large area vat photopolymerization 3D printing system using a 32-inch quasi-collimated visible backlight module with local dimming control

In this paper, we accomplished the first 32-inch liquid crystal display (LCD) type vat photopolymerization 3D printing system based on the quasi-collimated visible backlight module with local dimming control. The large building area (40x70cm) of the 3D printing system can greatly increase the size of printing objects and the production rate. We focused on the design of quasi-collimated backlights and analyzed their optical properties and the appearance of 3D printing objects fabricated by them. The optical performances of the quasi-collimated backlight using stacks of optical films feature no large angle leakage light, 86% intensity uniformity, and less than ±15° angular distribution of emitting light. After the optimization process by combining a variety of optical films, the uniformity, and collimation of the backlight module are greatly improved. Moreover, combining a local dimming algorithm with 3D printing technology can completely suppress unexpected photopolymerization residues, increase resin utilization rate, save energy, and greatly improve the probability of success in the 3D printing process, especially in large printing objects.

Field Model Identification and Control of a Mobile Electromagnet for Remote Actuation of Soft Robots

The actuation of miniaturized robots through external magnetic fields has great potential for medical applications. The controllability properties of the miniaturized robots are affected by magnetic field generation modality. In this work, the magnetic field of a mobile electromagnet, notably capable to generate a desired magnetic field in large 3D workspaces, has been identified first. Then, a control model of the field generation system has been developed to produce a desired magnetic field designed to generate a locomotion gait in a legged miniaturized robot. Preliminary experiments prove the viability of the approach.

RGB-D Face Recognition With Identity-Style Disentanglement and Depth Augmentation

Deep learning approaches achieve highly accurate face recognition by training the models with huge face image datasets. Unlike 2D face image datasets, there is a lack of large 3D face datasets available to the public. Existing public 3D face datasets were usually collected with few subjects, leading to the over-fitting problem. This paper proposes two CNN models to improve the RGB-D face recognition task. The first is a segmentation-aware depth estimation network, called DepthNet, which estimates depth maps from RGB face images by exploiting semantic segmentation for more accurate face region localization. The other is a novel segmentation-guided RGB-D face recognition model that contains an RGB recognition branch, a depth map recognition branch, and an auxiliary segmentation mask branch. In our multi-modality face recognition model, a feature disentanglement scheme is employed to factorize the feature representation into identity-related and style-related components. DepthNet is applied to augment a large 2D face image dataset to a large RGB-D face dataset, which is used for training our RGB-D face recognition model. Our experimental results show that DepthNet can produce more reliable depth maps from face images with the segmentation mask. Our multi-modality face recognition model fully exploits the depth map and outperforms state-of-the-art methods on several public 3D face datasets with challenging variations.

Generation of Hybrid Lead Halide CH3NH3PbI3-xClx Perovskite Crystals via Convective Self-Assembly

In this study, we have employed a film deposition technique based on convective forces acting at the triple air–solution–substrate contact line to tune the rate of solvent evaporation and, thus, to exert control over the morphology of the resulting mixed lead halide perovskite CH3NH3PbI3-xClx films. By varying the speed of film deposition while processing at two different temperatures, perovskite crystals of various shapes and dimensions were generated upon the crystallization of the initial perovskite precursor ink. More precisely, longer than half a millimeter 3D needle-like perovskite crystals exhibiting sharp edges co-existing with large cross-like 3D perovskite crystals could only be obtained upon the slow deposition of films at a low sample processing temperature of 17 °C, i.e., under conditions of low solvent evaporation rate. On the contrary, the utilization of higher film deposition speeds and/or processing temperatures led to smaller 3D needle-like or quasi-2D rectangular perovskite crystals that often appeared interconnected and coalesced. Moreover, as it was revealed by the photoluminescence measurements, the emission intensity of perovskite crystals was larger and dominated by a shorter wavelength peak, as compared to the uncrystallized material that emitted much less light, but at a longer wavelength.