Large 3D Research Articles

Large 3D network of concave Fe-N-C nanoparticles by nano-welding for high-power fuel cells

Large 3D network of concave Fe-N-C nanoparticles by nano-welding for high-power fuel cells

Comparison of teleportation and walking in virtual reality in a declarative learning task

IntroductionVirtual reality (VR) offers new possibilities for learning in educational settings by navigating through large 3D virtual environments. When designing VR-based learning applications, developers have to decide between different locomotion techniques to navigate through VR. Since physical activity and walking have been shown to enhance learning, physical walking in VR should increase learning compared to locomotion techniques without physical activity.MethodsIn this study, we examined if learners who are able to walk freely in VR differ regarding their declarative knowledge acquisition from learners who are teleported in VR.ResultsLearning outcomes did not differ between these two conditions, neither immediately after learning in VR nor after a one-day delay. Also, participants’ sense of presence in the virtual environment did not differ between the two conditions.DiscussionThese findings suggest that both teleportation and walking are suitable for declarative knowledge acquisition in VR, and that teleportation may be sufficient enough.

Foams-to-Films: A Facile Approach Towards Space-Confined CVD Growth of MoS2.

2D materials have rapidly become the building blocks for the next generation of semiconducting materials and devices, with Chemical vapor deposition (CVD) emerging as a prefered method for their synthesis. However, the predictable and reproducible growth of high quality, large 2D monolayers remains challenging. An important facet is controlling the local environment at the surface of the substrate - here, space-confinement techniques have emerged as promising candidates. We demonstrate that space-confined CVD growth using microstructured MoOx grown on Ni foam is an appealing approach for rapid growth of high quality MoS2 monolayers; a very important subset of 2D materials. This method eschews the use of powders which can be more difficult to control. By incorporation of a porous barrier in the Ni foam support, the rate of delivery of both the Mo and S source to the substrate is dampened, leading to coverage of large, high quality, mono-to-few layer triangular domains as confirmed by Raman and photoluminescence (PL) spectroscopies together with atomic force microscopy (AFM) height measurements.

Development of a method for estimating asari clam distribution by combining three-dimensional acoustic coring system and deep neural network

Developing non-contact, non-destructive monitoring methods for marine life is crucial for sustainable resource management. Recent monitoring technologies and machine learning analysis advancements have enhanced underwater image and acoustic data acquisition. Systems to obtain 3D acoustic data from beneath the seafloor are being developed; however, manual analysis of large 3D datasets is challenging. Therefore, an automatic method for analyzing benthic resource distribution is needed. This study developed a system to estimate benthic resource distribution non-destructively by combining high-precision habitat data acquisition using high-frequency ultrasonic waves and prediction models based on a 3D convolutional neural network (3D-CNN). The system estimated the distribution of asari clams (Ruditapes philippinarum) in Lake Hamana, Japan. Clam presence and count were successfully estimated in a voxel with an ROC-AUC of 0.9 and a macro-average ROC-AUC of 0.8, respectively. This system visualized clam distribution and estimated numbers, demonstrating its effectiveness for quantifying marine resources beneath the seafloor.

Initial stages of silver electrodeposition on a model Pt(111) single-crystal substrate from ethaline

Deep eutectic solvents (DES) are a new class of solvents with unique properties that are very promising media for the electroplating of metals and alloys. Knowledge about the regularities of metal electrodeposition is noticeably promoted by studies on model single crystal electrodes with a known well-ordered surface structure. In this work, we investigate the regularities of under- and overpotential deposition (upd and opd, respectively) of silver from a silver chloride solution in ethaline on a Pt(111) single crystal surface. For a better understanding of the occurring processes, we employ a complex approach involving electrochemical and in situ and ex situ microscopic techniques. We show that the process of gradual surface poisoning is observed on Pt(111) in the potential range in which no massive solvent degradation is yet observed. We also observe that the extent of this process largely depends on the employed potential of contact between the electrode and the solution and the studied potential range.The presence of Ag upd on Pt(111) in ethaline is demonstrated using voltammetric and in situ STM data. The available results and charge analysis are used to compare the Ag upd processes in ethaline and in aqueous solutions. We discuss the number of Ag adlayers on Pt(111), the effect of the potential of initial contact of the electrode with the solution and cycling history, and the possibility of anion upd and formation of coadsorbate between Ag adatoms and upd chloride anions (chlorine adatoms). We also demonstrate that upd Ag is not completely desorbed until high anodic potentials, where it seems to catalyze the solvent oxidation. Phase deposition of silver from ethaline on Pt(111) starts at almost zero overpotentials, which is probably related to the presence of the already deposited Ag adlayer. Even at very low overpotentials the deposit includes both large planar 2D crystallites and 3D crystallites formed on the surface defects and at the edges of the flat deposit islands. As the overpotential grows, the epitaxiality is gradually lost; the deposit becomes unshaped and the amount of 3D crystallites and their height increase

Thermal Conversion of Ultrathin Nickel Hydroxide for Wide Band Gap 2D Nickel Oxides.

Wide band gap (WBG) semiconductors (E g > 2.0 eV) are integral to the advancement of next-generation electronics, optoelectronics, and power industries owing to their capability for high-temperature operation, high breakdown voltage, and efficient light emission. Enhanced power efficiency and functional performance can be attained through miniaturization, specifically via the integration of device fabrication into a two-dimensional (2D) structure enabled by WBG 2D semiconductors. However, as an essential subgroup of WBG semiconductors, 2D transition metal oxides (TMOs) remain largely underexplored in terms of physical properties and applications in 2D optoelectronic devices, primarily due to the scarcity of sufficiently large 2D crystals. Thus, our goal is to develop synthesis pathways for 2D TMOs possessing large crystal domains (e.g., >10 μm), expanding the 2D TMO family and providing insights for future engineering of 2D TMOs. Here, we demonstrate the synthesis of WBG 2D nickel oxide (NiO) (E g > 2.7 eV) thermally converted from 2D nickel hydroxide (Ni(OH)2) with a lateral domain size larger than 10 μm. Moreover, the conversion process is investigated using various microscopic techniques, such as atomic force microscopy, Raman spectroscopy, transmission electron microscopy, and X-ray photoelectron spectroscopy, providing significant insights into morphology and structural variations under different oxidative conditions. The electronic structure of the converted Ni x O y is further investigated using multiple soft X-ray spectroscopies, such as X-ray absorption and emission spectroscopies.

High-fidelity Image Restoration of Large 3D Electron Microscopy Volume.

Volume electron microscopy (VEM) is an essential tool for studying biological structures. Due to the challenges of sample preparation and continuous volumetric imaging, image artifacts are almost inevitable. Such image artifacts complicate further processing both for automated computer vision methods and human experts. Unfortunately, the widely used contrast limited adaptive histogram equalization (CLAHE) can alter the essential relative contrast information about some biological structures. We developed an image-processing pipeline to remove the artifacts and enhance the images without CLAHE. We apply our method to VEM datasets of a Microwasp head. We demonstrate that our method restores the images with high fidelity while preserving the original relative contrast. This pipeline is adaptable to other VEM datasets.

Utility of diffusion-weighted imaging in differentiating benign and malignant breast lesions.

Breast cancer presents a significant global health burden. An accurate differentiation between benign and malignant lesions is imperative for timely intervention. While dynamic contrast enhanced MRI (DCE-MRI) is highly sensitive, its specificity is limited. This has led to the exploration of diffusion-weighted imaging (DWI) in distinguishing between benign and malignant breast lesions. The study aimed to explore the diagnostic utility of DWI in distinguishing between benign and malignant breast lesions. Assessment of 38 breast lesions using DWI with a b value of 800 s/mm2, performed with 3 Tesla MRI. The diagnostic performance of two different region of Interest (ROI) placement approaches was compared to obtain a feasible cut-off value of apparent diffusion coefficient (ADC) to differentiate between malignant and benign lesions. The histopathological reports were used as the gold standard. ADC values of malignant lesions were significantly lower than those of benign lesions (0.84 × 10-3 mm2/s vs. 1.54 × 10-3 mm2/s). The average ADC measured using a small-sized 2D ROI including the darkest part in the ADC map, performed better than the large 2D ROI covering the entire lesion. Using a cut-off value of 0.98 × 10-3 mm2/s, ADC obtained high sensitivity (90%) and specificity (88.9%) in distinguishing between benign and malignant breast lesions. Utilising quantitative analysis of DWI with ADC value measurement, reliably distinguished between benign and malignant breast lesions in this cohort, especially when employing a higher b value of 800 s/mm2.

Decimeter-depth and polarization addressable color 3D meta-holography

Fueled by the rapid advancement of nanofabrication, metasurface has provided unprecedented opportunities for 3D holography. Large depth 3D meta-holography not only greatly increases information storage capacity, but also enables distinguishing of the relative spatial relationship of 3D objects, which has important applications in fields like optical information storage and medical diagnosis. Although the methods based on Fresnel diffraction theory can reconstruct the real depth information of 3D objects, the maximum depth is only 2 mm. Here, we develop a 3D meta-holography based on angular spectrum diffraction theory to break through the depth limit. By developing the angular spectrum diffraction theory into meta-holography, the metasurface structure with independent polarization control is used to create a polarization multiplexing 3D meta-hologram. The fabricated amorphous silicon metasurface increases the depth range by 47.5 times and realizes 0.95 dm depth reconstruction for polarization independent and different color 3D meta-hologram in visible. Such polarization controlled large-depth color meta-holography is expected to open avenue for data storage, display, information security and virtual reality.

Rapid hologram generation through backward ray tracing and adaptive-resolution wavefront recording plane

Abstract An advanced method for rapidly computing holograms of large three-dimensional (3D) objects combines backward ray tracing with adaptive resolution wavefront recording plane (WRP) and adaptive angular spectrum propagation. In the initial phase, a WRP with adjustable resolution and sampling interval based on the object’s size is defined to capture detailed information from large 3D objects. The second phase employs an adaptive angular spectrum method (ASM) to efficiently compute the propagation from the large-sized WRP to the small-sized computer-generated hologram (CGH). The computation process is accelerated using CUDA and OptiX. Optical experiments confirm that the algorithm can generate high-quality holograms with shadow and occlusion effects at a resolution of 1024 × 1024 in 29 ms.

Effective and efficient coded aperture cone-beam computed tomography via generative adversarial U-Net

Coded aperture cone-beam computed tomography (CBCT) represents a crucial method for acquiring high-fidelity three-dimensional (3D) tomographic images while reducing radiation exposure. However, projections are non-uniformly and discontinuously sampled with the coded apertures placed in front of the x-ray source, leading to very small reconstruction scale and time-intensive iterations. In this study, an alternative approach to reconstruct coded aperture CBCT based on generative adversarial U-net is proposed to effectively and efficiently reconstruct large scale 3D CBCT images. Our method entails predicting complete and uniform projections from incomplete and non-uniform coded projections, enabling the requirement of continuity for the use of analytical algorithms in 3D image reconstruction. This novel technique effectively mitigates the traditional trade-off between image fidelity and computational complexity inherent in conventional coded aperture CBCT reconstruction methods. Our experimental results, conducted using clinical datasets comprising CBCT images from 102 patients at Nanjing Medical University, demonstrate that high-quality CBCT images with voxel dimensions of 400 × 400 × 400 can be reconstructed within 35 s, even when 95% of projections are blocked, yielding images with PSNR values exceeding 25dB and SSIM values surpassing 0.85.

Researching the electrochemical performance by Mn2+ substituted Na3+xV2-xMnx(PO4)3/rGO cathode materials for aqueous zinc-ion batteries

Aqueous zinc-ion batteries (AZIBs) are gaining rising popularity as potential energy storage solutions for large-scale renewable energy, attributed to their affordable pricing and inherent safety features. The reversible capacity of AZIBs, which is crucial for their cycle performance, is significantly influenced by the choice of cathode material, with Na3V2(PO4)3 standing out as promising candidates for their large 3D transport channels and rapid kinetics. However, they suffer from rapid degradation caused by low structural stability during the charge-discharge process. In this work, we researched the electrochemical performance of cathode materials by employing a sol-gel preparation for Mn-doped Na3-xV2-xMnx(PO4)3/rGO (x = 0, 0.05, 0.1), in which graphene oxides (rGO) were introduced as carbon sources. It is identified that the Mn doping exerts a beneficial influence to enhance stability of the structure. The Mn0.05-NVP/rGO material, optimized for performance, exhibits a specific capacity of 106.3 mAh·g−1 with a discharge plateau at 1.3 V at a current density of 100 mA·g−1, which corresponds to an energy density of 134.7 Wh·kg−1. Particularly, the addition of Mn enhances cycling performance, leading to a remarkable capacity retention rate of 75.3 % even after 100 cycles. This work confirms the feasibility using NASICON-type cathodes and offers valuable perceptions into the advancement of cathode materials in AZIBs.

Fast generation of spectrally shaped disorder.

Media with correlated disorder display unexpected transport properties, but it is still a challenge to design structures with desired spectral features at scale. In this work, we introduce an optimal formulation of this inverse problem by means of the nonuniform fast Fourier transform, thus arriving at an algorithm capable of generating systems with arbitrary spectral properties, with a computational cost that scales O(NlogN) with system size. The method is extended to accommodate arbitrary real-space interactions, such as short-range repulsion, to simultaneously control short- and long-range correlations. We thus generate the largest-ever stealthy hyperuniform configurations in 2d (N=10^{9}) and 3d (N>10^{7}) and demonstrate the flexibility of the approach by generating structures with designed spectral features at scale. By an Ewald sphere construction we link the spectral and optical properties at the single-scattering level and show that stealthy hyperuniform structures generically display transmission gaps, providing a concrete example of fine-tuning of a physical property. We also show that large 3d power-law hyperuniformity in particle packings leads to single-scattering properties nearly identical to those of simple hard spheres. Finally, we demonstrate generalizations of the approach to impose features in either continuous or discrete real space, using constraints in either continuous or discrete reciprocal space. In particular, enforcing large spectral power at peaks with the right symmetry leads to the nondeterministic generation of quasicrystalline structures in 2d and 3d. This technique should become an essential tool to embed, and understand the role of, long-range correlations in disordered metamaterials.

Transformer neural network based real-time process monitoring and direct visualization of top-down vat photopolymerization

Top-down vat photopolymerization (TVPP) technology is rapidly developing to all of the industries for new products development and manufacturing due to its low cost, fast speed and high precision. The powerful capability of TVPP for large size, highly customized and medium batch production renders it one of the most popular additive manufacturing techniques today. An effective real-time process monitoring method providing timely feedback for part defects, especially in case of print failure, is highly desirable but still rarely reported for TVPP 3D printing. Large 3D objects are normally segmented into smaller parts to reduce the risk of failure and materials waste, resulting in the complexity of building while sacrificing component integrity. Herein, a transformer neural network based real-time and visualized process monitoring (TransRV) was constructed as an effective method to enhance the manufacturing performance and quality. Upon the challenge of visualizing and capturing the real-time fabricated layer from the around liquid photoresin, a real-time dataset including in-situ standard reference images and real-time mask fabricated layers was initially constructed. Based on the dataset foundation, we then developed a novel neural network model for effective segmentation of captured images by introducing multiple attention mechanisms and adopting the architecture of Swin Transformer. The experimental results showed that the real-time taken images during the printing process could be accurately segmented through our designed neural network model. The mIoU, which is the ratio of mean intersection over union, was considered as the main evaluation index in the test set. And the value of mIoU could achieve as high as 96.14 %. On the basis of this result, we further constructed a multiple quality monitoring indicator for quality assessment and defect detection of TVPP process. It was proved that this indicator enabled real-time accurate recognition and in time feedback. The typical defects such as overall collapse and partial missing of the printed parts that usually occur during TVPP process would be timely detected and subsequently stopped printing. Apparently, the methods developed in this work provide a promising strategy to effectively eliminate the material waste and highly improve the productivity. Most importantly, the presented real-time process monitor holds the great potential for quality control and defect detection of widespread TVPP manufacturing.

Quality and Efficiency of Coupled Iterative Coverage Path Planning for the Inspection of Large Complex 3D Structures

To enable unmanned aerial vehicles to generate coverage paths that balance inspection quality and efficiency when performing three-dimensional inspection tasks, we propose a quality and efficiency coupled iterative coverage path planning (QECI-CPP) method. First, starting from a cleaned and refined mesh model, this was segmented into narrow and normal spaces, each with distinct constraint settings. During the initialization phase of viewpoint generation, factors such as image resolution and orthogonality degree were considered to enhance the inspection quality along the path. Then, the optimization objective was designed to simultaneously consider inspection quality and efficiency, with the relative importance of these factors adjustable according to specific task requirements. Through iterative adjustments and optimizations, the coverage path was continuously refined. In numerical simulations, the proposed method was compared with three other classic methods, evaluated across five aspects: image resolution, orthogonality degree, path distance, computation time, and total path cost. The comparative simulation results show that the QECI-CPP achieves maximum image resolution and orthogonality degree while maintaining inspection efficiency within a moderate computation time, demonstrating the effectiveness of the proposed method. Additionally, the flexibility of the planned path is validated by adjusting the weight coefficient in the optimized objective function.

Machine learning enables the discovery of 2D Invar and anti-Invar monolayers

Materials demonstrating positive thermal expansion (PTE) or negative thermal expansion (NTE) are quite common, whereas those exhibiting zero thermal expansion (ZTE) are notably scarce. In this work, we identify the mechanical descriptors, namely in-plane tensile stiffness and out-of-plane bending stiffness, that can effectively classify PTE and NTE 2D crystals. By utilizing high throughput calculations and the state-of-the-art symbolic regression method, these descriptors aid in the discovery of ZTE or 2D Invar monolayers with the linear thermal expansion coefficient (LTEC) within ±2 × 10−6 K−1 in the middle range of temperatures. Additionally, the descriptors assist the discovery of large PTE and NTE 2D monolayers with the LTEC larger than ±15 × 10−6 K−1, which are so-called 2D anti-Invar monolayers. Advancing our understanding of materials with exceptionally low or high thermal expansion is of substantial scientific and technological interest, particularly in the development of next-generation electronics at the nanometer or even Ångstrom scale.

Ionic 2D Materials

2D materials are an exciting topic with a wide variety of applications in many research fields, from optoelectronics, photonics or qubit devices to catalysis. So far, these layered nanoscopic materials have been obtained by different procedures. In top-down approaches, from simple mechanical "Scotch tape" exfoliation to liquid exfoliation to break the weak non-covalent interactions between layers. In this project, the challenge was to obtain 2D flakes of a 3D ionic starting material with cubic rock salt structure (e.g. alkali halides NaCl, NaF, KCl, KF or CsF). They will have physical properties with substantial differences from those of known 2D materials. For example, there are few large bandgap 2D materials, almost exclusively BN layers. The BN bulk system has a band gap around 4 eV and its monolayer 6 eV, while the employed alkali halides in this project, the bulk vales are 10.0 eV (CsF), 10.9 eV (KF), 11.7 eV (NaF), KCl eV (8.5) and eV NaCl (8.6). This will open many applications that are now only restricted to BN layers. The crucial point is how it is possible to exfoliate an isotropic 3D material with ionic interactions in three spatial directions.(1,2) The intercalation of bromine molecules in CsF was corroborated, many years ago, by X-ray single-crystal diffraction.(1,3, see Figure) The intercalated structure is analogous to that of other conventional 2D materials; for example, graphene also with bromine molecules. This intercalation into the 3D structure of the alkali halide has two effects, one is to generate an anisotropic 2D structure with alternated monoatomic CsF layers and layers of Br2 molecules. The second important effect is that now for the exfoliation of the CsF-Br2 compound, only the non-ionic F···Br-Br interactions must be broken. Preliminary DFT calculations indicate that such contact energies are of the same order of magnitude as other non-covalent interactions of common 2D materials.The intercalation of such 2D materials has been performed by the intercalation of Br2 and IBr molecules combined with different exfoliation techniques (sonication-centrifugation, ball milling, microwaves..). The characterization of the 2D flakes has been performed using Atomic Force Microscopy. The final goal of this project is to make a profit from the large gap of such 2D systems for optoelectronic properties, such as single-photon emission.(1) Desmarteau, D. D.; Grelbig, T.; Hwang, S. H.; Seppelt, K., Angewandte Chemie-International Edition in English 1990, 29 (12), 1448-1449(2) Ruiz, E.; Alvarez, S., Journal of the American Chemical Society 1995, 117 (10), 2877-2883.(3) Drews, T.; Marx, R.; Seppelt, K., Chemistry-a European Journal 1996, 2 (10), 1303-1307. Figure 1

On-Surface Synthesis of 1D and 2D π-Conjugated Polymers from Heterotetrabromo-P-quinodimethanes (HTBQs)

Molecules bearing gem-dibromoolefines functionalities have been employed in wet chemistry as intermediates in the well-known Corey-Fuchs homologation of carbonyls to alkynes. This gem-dibromoolefines can be embedded into tetrabromo-p-quinodimethanes (TBQs), which efficiently undergo dehalogenative homocoupling on coinage metal surfaces, namely Au(111) and Ag(111), forming one-dimensional (1D) acene and periacene π-conjugated polymers able to exhibit non-trivial topological properties.[1,2]However, the synthesis of more complex π-conjugated polymers to determine the scope of this new reaction, as well as the synthesis of two-dimensional (2D) polymers by following this chemical methodology are still important challenges. In this context, new dimeric TBQs have been synthesized by wet chemical protocols for their further use in the synthesis of double-strand oligomers. Interestingly, the new systems exhibit many defects, due to the intrinsic nature of the synthesized precursors.[3] In this work, we firstly take advantage of the introduction of heteroatoms at the edges of the monomers in order to grow 1D new π-conjugated polymers which, eventually, collapse into amazing large 2D networks by non-covalent interactions. Moreover, when suitable designed TBQs with both the presence of heteroatoms and steric hindrance, unprecedented 1D and covalently linked 2D π-conjugated polymers are formed. These results have allowed the study of new topologically non-trivial polymers which will be presented and discussed in this communication.[1] Cirera, B., Sánchez-Grande, A., de la Torre, B., Santos J. et al. Nat. Nanotechnol. 2020, 15, 437–443.[2] González-Herrero, H., Mendieta-Moreno, J. I., Edalatmanesh, S., Santos, J. et al. Advanced Materials 2021, 33, 2104495.[3] Vicent, D. J., Perez-Escribano, M. et al. Chem. Sci., 2023, 14, 10112-10120.

Progress in organ 3D bioprinting.

Three dimensional (3D) printing is a hot topic in today’s scientific, technological and commercial areas. It is recognized as the main field which promotes “the Third Industrial Revolution”. Recently, human organ 3D bioprinting has been put forward into equity market as a concept stock and attracted a lot of attention. A large number of outstanding scientists have flung themselves into this field and made some remarkable headways. Nevertheless, organ 3D bioprinting is a sophisticated manufacture procedure which needs profound scientific/technological backgrounds/knowledges to accomplish. Especially, large organ 3D bioprinting encounters enormous difficulties and challenges. One of them is to build implantable branched vascular networks in a predefined 3D construct. At present, organ 3D bioprinting still in its infancy and a great deal of work needs to be done. Here we briefly overview some of the achievements of 3D bioprinting technologies in large organ, such as the bone, liver, heart, cartilage and skin, manufacturing.

Initial Experience With Ultra-High-Definition 3D Exoscope in Thyroid and Parathyroid Surgery.

Operation with a 3D exoscope has recently been introduced in clinical practice. The exoscope consists of two cameras placed in front of the operative field. Images are shown on a large 3D screen with high resolution. The system can be used to enhance precise dissection and provides new possibilities for improved ergonomics, fluorescence, and other optical-guided modalities. Initial experience with the ultra-high-definition (4K) 3D exoscope in thyroid and parathyroid operations. The exoscope (OrbEyeTM) was mounted on a holding system (Olympus). We used the exoscope in parathyroidectomy (N = 6) and thyroidectomy (N = 6). Immediate advantages and disadvantages were discussed and recorded. The learning curve for use of the exoscope may be shorter for surgeons with training in endoscopic or robotic procedures. There may be improved ergonomics compared with normal open-neck operations. Further, the optical guided operations can be used with fluorescence and have potential for different on-lay techniques in the future. The 4K 3D image quality is state-of-art and is highly appreciated during fine surgical dissection and eliminates the need for loupes. In several ways, using the ORBEYE™ in thyroid and parathyroid surgery provides the surgical team with a new and enhanced experience. This includes improved possibility for teaching, surgical ergonomics, and a 4K 3D camera with a powerful magnification system. However, it is not clear if utilization of these features would improve surgical outcomes. Furthermore, the ORBEYE™ lacks incorporation of parathyroid autofluorescence, and the current costs for the system do not facilitate general access to exoscope assisted operations.