Three-dimensional Shape Research Articles

(Keynote) Macroscopic Materials Synthesized from Nanoparticle Superlattices

One of the promises of nanotechnology in its early developmental stages was the ability to make designer materials with precise control over individual building block composition and organization in 3D space. In recent years, material synthesis methods have advanced to be able to create a multitude of nanoparticles of varying sizes, shapes, and compositions, providing a vast array of building blocks to use as materials fabrication components. Additionally, processing methods have been developed to arrange these nanoparticles into ordered arrays, to dry them into thin films, and even to sinter them into more complex bulk solids. However, the fundamental promise of being able to build a material with controlled hierarchical structure across all length scales (atomic crystal structure, molecular composition; nanoscale feature size, shape, and organization; microstructure; macroscopic form) has been challenging to realize. A materials synthesis and processing route that could create free-standing, macroscopic materials or arbitrary three-dimensional shapes with precisely controlled nanoparticle positions across the entirety of the material composition would therefore be a major advancement. Here, we demonstrate a nanoparticle-based building block called a “Nanocomposite Tecton (NCT)” that enables a self-assembly route to fabricating free-standing 3D solids of arbitrary macroscopic shapes that can utilize a multitude of different nanoparticle compositions, and also possess specifically programmed nanoscale particle arrangements and controlled microstructure. This talk will outline the key synthesis and processing steps that enable this method of making materials with programmed material structure across ~7 orders of magnitude in length scale, thereby realizing a long-standing goal of nanomaterials fabrication.

Spatial coding strategy for dual-frequency phase-shifting profilometry

Dual-frequency fringe projection techniques have been broadly utilized for three-dimensional (3D) shape recovery. However, a pervasive challenge across these methodologies lies in the precise implementation of high-speed measurements. This paper explores a spatial coding strategy designed for the dual-frequency phase unwrapping method to tackle this. The strategy mandates the utilization of only four patterns, encapsulating the phase information of both high and low frequencies. The codewords are strategically embedded within the low-frequency phase, facilitating the acquisition of an accurate absolute phase map. A robust decoding algorithm engineered to ensure dependable phase unwrapping, particularly in scenarios characterized by a substantial number of high-frequency cycles. The proposed method exhibits superior noise immunity and enhances accuracy when compared with the previous dual-frequency phase-shifting methodology. Simulations and experiments demonstrate the robustness and efficiency of the proposed strategy.

Enhanced Analysis of Ice Accretion on Rotating Blades of Horizontal-Axis Wind Turbines Using Advanced 3D Scanning Technology

This study investigated the meteorological conditions leading to ice formation on wind turbines in a coastal mountainous area. An enhanced ice formation similarity criterion was developed for the experimental design, utilizing a scaled-down model of a 1.5 MW horizontal-axis wind turbine in icing wind tunnel tests. Three-dimensional ice shapes on the rotating blades were obtained and scanned using advanced 3D laser measurement technology. Post-processing of the scanned data facilitated the construction of solid models of the ice-covered blades. This study analyzed the maximum ice thickness, ice-covered area, and dimensionless parameters such as the maximum dimensionless ice thickness and dimensionless ice-covered area along the blade. Under the experimental conditions, the maximum ice thickness reached 0.5102 m, and the ice-covered area extended up to 0.5549 m2. The dimensionless maximum ice thickness and dimensionless ice-covered area consistently increased along the blade direction. Our analysis of 3D ice shape characteristics and the ice volume under different test conditions demonstrated that wind speed and the liquid water content (LWC) are critical factors affecting ice formation on blade surfaces. For a constant tip speed ratio, higher wind speeds and a greater LWC resulted in increased ice volumes on the blade surfaces. Specifically, increasing the wind speed can augment the ice volume by up to 57.2%, while increasing the LWC can enhance the ice volume by up to 149.2% under the experimental conditions selected in this study.

Three-dimensional shape of natural riblets in the white shark: relationship between the denticle morphology and swimming speed of sharks.

The ridges of the dermal denticles of migratory sharks have inspired riblets to reduce the frictional drag of a fluid. In particular, the dermal denticles of white sharks (Carcharodon carcharias) are characterized by a high middle ridge and low side ridges. The detailed morphology of their denticles and their variation along the body, however, have never been investigated. Moreover, the hydrodynamic function of high-low combinations of ridges is unknown. In this article, the ridge spacings and heights of the white shark denticles were three-dimensionally quantified using microfocus X-ray computed tomography. Then, the swimming speed at which the ridges would reduce drag was hydrodynamically calculated with a flat plate body model and previous riblet data. High ridges with a large spacing were found to effectively reduce drag at a migration speed of 2.3 m s-1, while adjacent high and low ridges with a small spacing reduced drag at a burst hunting speed of 5.1 m s-1. Moreover, the above hydrodynamic calculation method was also applied to the shortfin mako shark and an extinct giant shark (called megalodon) with known ridge spacings, resulting in the estimated hunting speeds of 10.5 m s-1and 5.9 m s-1, respectively.

Quantification of Airway Structures by Persistent Homology.

We propose two types of novel morphological metrics for quantifying the geometry of tubular structures on computed tomography (CT) images. We apply our metrics to identify irregularities in the airway of patients with chronic obstructive pulmonary disease (COPD) and demonstrate that they provide complementary information to the conventional metrics used to assess COPD, such as the tissue density distribution in lung parenchyma and the wall area ratio of the segmented airway. The three-dimensional shape of the airway and its abstraction as a rooted tree with the root at the trachea carina are automatically extracted from a lung CT volume, and the two metrics are computed based on a mathematical tool called persistent homology; treeH0 quantifies the distribution of branch lengths to assess the complexity of the tree-like structure and radialH0 quantifies the irregularities in the luminal radius along the airway. We show our metrics are associated with clinical outcomes.

3D printing of reprogrammable liquid crystal elastomers with exchangeable boronic ester bonds

Liquid crystal elastomers (LCEs) are a kind of soft actuating materials with large reversible deformation ability, which can work as the “motor” to exhibit complex deformations and drive the locomotion of soft robots. The deformation of LCEs depends on the three-dimensional (3D) shape of whole structure and alignment patterns of mesogens. Various methods have been employed to fabricate the LCE structure with desired shapes and mesogen alignments. However, conventional 3D printed LCEs require continuous thermal energy input to maintain their actuated shapes. The LCEs cannot be reprocessed and reprogrammed once cured. Herein, we introduce dynamic boronic ester bonds into the ink, with which the printed LCE structures are capable of being reprogrammed from polydomain into monodomain state and vice versa. We further explore the effects of printing parameters and content of dynamic covalent bonds on the actuation performance and reprogramming ability. The actuated shape could be well predicted with finite element method. The dynamic printable LCEs developed here offer new strategy and large design space for LCE structures.

Fast and high-accuracy three-dimensional shape measurement using intermediate-bit projection

Fast and high-accuracy three-dimensional shape measurement using intermediate-bit projection

Bending Forming Characteristics of CoCrFeMnNi High-Entropy Alloy Sheets Induced by Nanosecond Pulse Laser Irradiation.

Laser bending forming, as a flexible and die-less forming approach, facilitates the three-dimensional shaping of sheets through the generation of thermal stress via laser-material interaction. In this study, the bending forming characteristics of CoCrFeMnNi high-entropy alloy sheets induced by nanosecond pulse laser irradiation were systematically investigated. The effects of parameters including laser power, scanning speed, number of scans, scanning interval, and sheet size on the bending angle, cross-sectional morphology, and hardness were studied in detail under both the laser single-line and multi-line scanning modes. The experimental results confirmed the effectiveness of nanosecond pulse laser irradiation for achieving accurate formation of CoCrFeMnNi sheets, with the successful fabrication of J, L, and U-shaped metal components. Apart from the forming ability, the cross-sectional hardness was significantly increased due to the grain refinement effect of nanosecond pulse laser irradiation. Furthermore, employing the laser single-line scanning mode enabled the effective rectification of overbending parts, showcasing complete recovery for small-angle overbending, and a remarkable 91% recovery for larger-angle overbending. This study provides an important basis for the bendability of CoCrFeMnNi sheets by laser forming and elucidates the evolution of the microstructure and mechanical properties in the bending region.

Multi-factor quality assessment of digital speckle pattern for speckle projection profilometry

Abstract The digital speckle pattern (DSP) is an essential component in the speckle projection profilometry (SPP) task, its quality directly affects the results of three-dimensional (3D) shape reconstruction. However, the SPP field lacks specialized numerical metrics for evaluating speckle quality. To address this issue, this study introduces a multi-factor metric (MFM) for comprehensive DSP assessment. Through comparing the metric, optimal parameter ranges for DSP design and the advisable matching subset size can be determined for SPP algorithm. A global indicator named valid feature distribution (VFD) based on scale-invariant feature transform (SIFT) and Delaunay triangulation, is defined to analyze the overall information distribution in DSPs. In addition, MFM incorporates a local metric called mean subset intensity gradient (MSIG), which aids in selecting the suitable radius for different DSPs to balance the accuracy and efficiency. The quality assessment targets the speckle scene images, allowing for the reverse adjustment of the most suitable DSP according to different scenes. The performance of DSPs can be evaluated based on the accuracy and completeness of 3D reconstruction results. By conducting simulation experiments on the 3ds Max platform, the recommended parameter range for DSP can be inferred, including speckle density ratio, speckle diameter, and random variation rate. Appropriate subset sizes for different scenes are also investigated. Furthermore, the MFM is verified on a real binocular speckle device, demonstrating that the measurement standard deviation of a complex workpiece can be reduced to 0.078 mm using the recommended DSP.

Deep learning-based prediction of 3-dimensional silver contact shapes enabling improved quality control in solar cell metallization

The industrial metallization of Si solar cells predominantly relies on screen printing, with silver as the preferred electrode material. However, the design of commercial screens often leads to suboptimal silver usage and increased electrical resistance due to print-related inhomogeneities like mesh marks, constrictions and spreading. Real-time monitoring of quality parameters during production has thus become increasingly critical. Current inline optical quality control systems usually only include 2D visualizations of the printed layout, which limits their effectiveness in quality control. Options that allow 3D measurements are usually slow, expensive, and therefore not worth considering in most cases. This research focuses on the development of a model that can estimate the three-dimensional shape of printed contact fingers from a single 2D image without the need of additional hardware using deep learning. Furthermore, a workflow for the generation of training data, which involves the creation of image pairs from a 2D microscope and a 3D confocal laser scanning microscope (CLSM) to accurately represent solar cell fingers, is presented. After model training, the predicted height maps are compared with the ground truth height maps, and the robustness of the model with respect to a paste variation and screen parameter variation is examined. The results confirm the feasibility and reliability of deep learning-based 3D shape estimation, extending its applicability to new, previously unseen data from screen-printed contact fingers. With a structural similarity index (SSIM) score of 0.76, a strong correlation between the estimated and ground truth height maps is established. In summary, our deep learning-based approach for height map estimation offers an effective and reliable solution for fast inline detection and analysis of the cross-sectional area of the printed contact fingers.

A study of centaur (54598) Bienor from multiple stellar occultations and rotational light curves

Centaurs, distinguished by their volatile-rich compositions, play a pivotal role in understanding the formation and evolution of the early Solar System, as they represent remnants of the primordial material that populated the outer regions. Stellar occultations offer a means to investigate their physical properties, including shape and rotational state, and the potential presence of satellites and rings. This work aims to conduct a detailed study of the centaur (54598) Bienor through stellar occultations and rotational light curves from photometric data collected during recent years. We successfully predicted three stellar occultations by Bienor that were observed from Japan, Western Europe, and the USA. In addition, we organized observational campaigns from Spain to obtain rotational light curves. At the same time, we developed software to generate synthetic light curves from three-dimensional shape models, enabling us to validate the outcomes through computer simulations. We resolved Bienor's projected ellipse for December 26, 2022; determined a prograde sense of rotation; and confirmed an asymmetric rotational light curve. We also retrieved the axes of its triaxial ellipsoid shape as a = (127 pm 5) km, b = (55 pm 4) km, and c = (45 pm 4) km. Moreover, we refined the rotation period to 9.1736 pm 0.0002 hours and determined a geometric albedo of (6.5 pm 0.5) <!PCT!>, which is higher than previously determined by other methods. Finally, by comparing our findings with previous results and simulated rotational light curves, we analyzed whether an irregular or contact-binary shape, an additional element such as a satellite, or significant albedo variations on Bienor's surface may be present.

Three-dimensional shape and connectivity of physical networks

Data describing the three-dimensional structure of physical networks is increasingly available, leading to a surge of interest in network science to explore the relationship between the shape and connectivity of physical networks. We contribute to this effort by standardizing and analyzing 15 data sets from different domains. Each network is made of tube-like objects bound together at junction points, which we treat as nodes, with the connections between them considered as links. We divide these networks into three categories: lattice-like networks, trees, and linked trees. The degree distribution of these physical networks is bounded, with most nodes having degrees one or three. Characterizing the physical properties of links, we show that links have an elongated shape and tend to follow a nearly straight trajectory, while a small fraction of links follow a winding path. These typical node and link properties must be reflected by physical network models. We also measure how confined a link is in space by comparing its trajectory to a randomized null model, showing that links that are central in the abstract network tend to be physically confined by their neighbors. The fact that the shape and connectivity of the physical networks are intertwined highlights that their three-dimensional layout must be taken into account to understand the evolution and function of physical networks.

Обучение математике в цифровой образовательной среде

Currently, ICT technologies are widely used in the learning process. In practice, it turned out that it is not so easy to introduce these innovations into the educational process. This article provides an analysis of the use of modern technologies in mathematics lessons. The main areas of application of ICT technologies in education are the construction of individual learning routes, automation of control, the construction of various graphs and three-dimensional geometric shapes. Based on the above, the question arises about the possibility of improving the effectiveness and accessibility of education. Is it possible to increase students' interest in the subject, as well as the quality of education, with the help of a digital educational environment?

FUSION3D: Multimodal data fusion for 3D shape reconstruction: A soft-sensing approach

Traditional high-fidelity imaging techniques, such as X-ray computer tomography (CT), excel in capturing intricate shape details through high-resolution two-dimensional (2D) images. However, the extensive cost or impracticality of obtaining X-ray images poses a significant challenge in various applications (e.g., dense metal components in manufacturing). To overcome this limitation, we propose a soft sensing approach, named FUSION3D, aimed at reconstructing three-dimensional (3D) shapes from 2D sliced images and heterogenous process inputs using a novel model based on multi-modal process knowledge. The developed FUSION3D method is built upon a deep learning framework that utilizes continuous normalizing flow to model the complex shape distribution of 3D objects. We propose a principled probabilistic framework to regularize 3D point clouds by modeling them as a distribution of distributions. Specifically, we learn a two-level hierarchy of distributions where the first level is the distribution of shapes, and the second level is the distribution of points given a shape. Our generative regularization model learns each level of the distribution with a continuous normalizing flow. The invertibility of normalizing flows enables the computation of the likelihood during training and allows us to train our model in the variational inference framework. Our methodology is validated through a case study in additive manufacturing, demonstrating its effectiveness as a soft sensing approach for accurate 3D shape reconstruction without relying on high-fidelity imaging techniques like CT during model deployment.

Preferential Secretion of Oxidation-Sensitive Proteins by Unconventional Pathways: Why is This Important for Inflammation?

Significance: Fidelity of intercellular communication depends on unambiguous interactions between protein ligands and membrane receptors. Most proteins destined to the extracellular space adopt the required three-dimensional shape as they travel through the endoplasmic reticulum (ER), Golgi complex, and other organelles of the exocytic pathway. However, some proteins, many of which are involved in inflammation, avoid this classical secretory route and follow unconventional pathways to leave the cell. Recent Advances: Stringent quality control systems operate in the ER and cis-Golgi, restricting transport to native conformers, devoid of non-native disulfides and/or reactive thiols. However, some proteins released by living cells require reduced cysteines to exert their extracellular function(s). Remarkably, these proteins lack the secretory signal sequence normally required by secretory proteins for translocation into the ER lumen. Critical Issues: Why do interleukin-1β, high mobility group box 1, and other proinflammatory proteins avoid the ER-Golgi route to reach the intercellular space? These proteins require reactive cysteines for exerting their function. Therefore, eluding thiol-mediated quality control along the exocytic pathway is likely one of the main reasons why extracellular proteins that need to be reduced utilize unconventional pathways of secretion, where a quality control aimed at oxidating native cysteines is not present. Future Directions: Particularly under stress conditions, cells release redox-active enzymes and nonprotein thiol compounds that exert an extracellular control of redox-sensitive protein activity, shaping inflammatory responses. This post-secretion, redox-dependent editing of protein messages is still largely undefined. Understanding the underlying mechanistic events will hopefully provide new tools to control inflammation.

Evolution of the Italian pasta ripiena: the first steps toward a scientific classification

In this study, phylogenetic and biogeographic methods are used to investigate the evolutionary relationships between various types of Italian pasta ripiena (filled pasta) and related representatives from across Eurasia, using information from their geography, shape, content and cooking methods. Our results showed that, with the exception of the Sardinian Culurgiones, all the other pasta ripiena from Italy likely had a single origin in the northern parts of the country. Based on the proposed evolutionary hypothesis, the Italian pasta are divided into two main clades: a ravioli clade mainly characterized by a more or less flat shape, and a tortellini clade mainly characterized by a three-dimensional shape. The implications of these findings are further discussed.

Structured electrons with chiral mass and charge.

Chirality is a phenomenon with widespread relevance in fundamental physics, material science, chemistry, optics, and spectroscopy. In this work, we show that a free electron can be converted by the field cycles of laser light into a right-handed or left-handed coil of mass and charge. In contrast to phase-vortex beams, our electrons maintained a flat de Broglie wave but obtained their chirality from the shape of their expectation value in space and time. Measurements of wave function densities by attosecond gating revealed the three-dimensional shape of coils and double coils with left-handed or right-handed pitch. Engineered elementary particles with such or related chiral geometries should be useful for applications in chiral sensing, free-electron quantum optics, particle physics or electron microscopy.

Bionic nanopore recognition receptors for single-molecule enantioselectivity studies of chiral drugs

BackgroundEnantiodiscrimination of chiral drugs is critical for understanding physiological phenomena and ensuring medical safety. Although enantiomers of these drugs share identical physicochemical properties, they exhibit significant differences in pharmacodynamic, pharmacokinetic, and toxicological properties due to the differences in their three-dimensional shapes. Therefore, the development of effective methods for chiral recognition is of great significance and has been a hot topic in chemo/biological studies. ResultsIn this study, we designed a recognition receptor comprising a α-hemolysin (α-HL) nanopore and sulfobutyl ether-β-cyclodextrin (SBEβCD) for identifying the enantiomers of the antidepressant duloxetine at the single-molecule level. Chiral molecules were discriminated based on the different current blockages within the recognition receptor. The results indicated a strong interaction between R-duloxetine and the recognition receptor. By combining the experimental data and molecular docking results, we explored the recognition mechanism of the designed nanopore recognition receptor for chiral drug molecules. It was found that hydrophobic and electrostatic interactions play key roles in chiral recognition. Additionally, by comparing the binding kinetics of enantiomers to cyclodextrins in confined nanospace and bulk solution, we found that enantiomeric identification was better facilitated in the confined nanospace. Finally, the enantiomeric excess (ee) of the enantiomeric duloxetine mixture was measured using this recognized receptor. SignificanceThis strategy has the advantages of low cost, high sensitivity, and no need for additional derivative modifications, providing a new perspective on the development of chiral recognition sensors with excellent enantioselectivity in drug design, pharmaceuticals, and biological applications.

Adherent Peanut Image Segmentation Based on Multi-Modal Fusion.

Aiming at the problem of the difficult segmentation of adherent images due to the not fully convex shape of peanut pods, their complex surface texture, and their diverse structures, a multimodal fusion algorithm is proposed to achieve a 2D segmentation of adherent peanut images with the assistance of 3D point clouds. Firstly, the point cloud of a running peanut is captured line by line using a line structured light imaging system, and its three-dimensional shape is obtained through splicing and combining it with a local surface-fitting algorithm to calculate a normal vector and curvature. Seed points are selected based on the principle of minimum curvature, and neighboring points are searched using the KD-Tree algorithm. The point cloud is filtered and segmented according to the normal angle and the curvature threshold until achieving the completion of the point cloud segmentation of the individual peanut, and then the two-dimensional contour of the individual peanut model is extracted by using the rolling method. The search template is established, multiscale feature matching is implemented on the adherent image to achieve the region localization, and finally, the segmentation region is optimized by an opening operation. The experimental results show that the algorithm improves the segmentation accuracy, and the segmentation accuracy reaches 96.8%.

Geometric relationship between the projected surface area and mass of a plastic particle

The quantification of the mass of meso/microplastic (MMP) particles is crucial for assessing the global inventory of ocean plastics and assessing environmental and human health risks. Herein, linear regression models between mass and projected surface area on a log scale were established by directly measuring the masses of 4390 MMP particles collected at 35 sites in 17 Japanese rivers with an ultramicrobalance. The linear regression models estimated mass concentrations more accurately than any previous method based on geometric volume assuming several three-dimensional shapes. Additionally, linear regression models were quite reasonable for determining the geometric relationships of idealized cuboid particles. The slope of the linear regression models was dependent on the three-dimensional shapes of the particles, and their intercept was determined according to their third dimension. Moreover, the third dimension led to uncertainty in the mass estimation of particles; thus, the accuracies of the previous methods were relatively poor. Nevertheless, two limitations for mass measurement by linear regression models were identified, which determined the size range of the MMP particles on the projected surface area (ranging from 10−4 mm2 to 102 mm2) that is applicable for mass estimation of the particles collected from riverine and marine environments. Our results could be used to accurately estimate the mass concentrations in aquatic environments and provide insights into the geometric relationships between the mass and size of MMP particles.