Multi-scale 3D Research Articles

Biomineralized Materials as Model Systems for Structural Composites: 3D Architecture.

Biomineralized materials are sophisticated material systems with hierarchical 3D material architectures, which are broadly used as model systems for fundamental mechanical, materials science, and biomimetic studies. The current knowledge of the structure of biological materials is mainly based on 2D imaging, which often impedes comprehensive and accurate understanding of the materials' intricate 3D microstructure and consequently their mechanics, functions, and bioinspired designs. The development of 3D techniques such as tomography, additive manufacturing, and 4D testing has opened pathways to study biological materials fully in 3D. This review discusses how applying 3D techniques can provide new insights into biomineralized materials that are either well known or possess complex microstructures that are challenging to understand in the 2D framework. The diverse structures of biomineralized materials are characterized based on four universal structural motifs. Nacre is selected as an example to demonstrate how the progression of knowledge from 2D to 3D can bring substantial improvements to understanding the growth mechanism, biomechanics, and bioinspired designs. State-of-the-art multiscale 3D tomographic techniques are discussed with a focus on their integration with 3D geometric quantification, 4D in situ experiments, and multiscale modeling. Outlook is given on the emerging approaches to investigate the synthesis-structure-function-biomimetics relationship.

Multiscale 3D finite element analysis of aluminum matrix composites with nanoµ hybrid inclusions

The present work proposed a multiscale finite element (FE) analytical approach to investigate the mechanical behavior of elasto-plastic aluminum matrix composites reinforced with nano-micro hybrid inclusions. To study the effect of microstructural effects, 3D representative volume elements (RVEs) of nano- and micro-scales are established, where the nano-RVE considers the aluminum matrix with carbon nanotubes (CNTs)-reinforcement whose homogenization is treated as the equivalent matrix (EM) phase for micro-RVE with SiC particles. The FE-based ABAQUS simulation is employed to recover the local stress distributions within RVE of either scales, and the corresponding effective stress–strain relationship, damage and failure behavior are calculated and predicted. Results generated from the present simulations are validated against the experimental measurement of CNTs/SiC/Al composites and good agreement is generally obtained. What’s more, several microstructural parameters are tested on the composites’ effective behavior, including the elaso-plastic constitutive relation and damage response of aluminum matrix, the elastic-brittle failure mechanical behavior of the micro-scale SiC particles. We also consider the traction-separation mechanical behavior at the SiC-Al interface. It is demonstrated that the present numerical method is suitable to study the comprehensive behavior of composites with hybrid inclusions.

Machine learning-enabled feature classification of evaporation-driven multi-scale 3D printing

The freeform generation of active electronics can impart advanced optical, computational, or sensing capabilities to an otherwise passive construct by overcoming the geometrical and mechanical dichotomies between conventional electronics manufacturing technologies and a broad range of three-dimensional (3D) systems. Previous work has demonstrated the capability to entirely 3D print active electronics such as photodetectors and light-emitting diodes by leveraging an evaporation-driven multi-scale 3D printing approach. However, the evaporative patterning process is highly sensitive to print parameters such as concentration and ink composition. The assembly process is governed by the multiphase interactions between solutes, solvents, and the microenvironment. The process is susceptible to environmental perturbations and instability, which can cause unexpected deviation from targeted print patterns. The ability to print consistently is particularly important for the printing of active electronics, which require the integration of multiple functional layers. Here we demonstrate a synergistic integration of a microfluidics-driven multi-scale 3D printer with a machine learning algorithm that can precisely tune colloidal ink composition and classify complex internal features. Specifically, the microfluidic-driven 3D printer can rapidly modulate ink composition, such as concentration and solvent-to-cosolvent ratio, to explore multi-dimensional parameter space. The integration of the printer with an image-processing algorithm and a support vector machine-guided classification model enables automated, in situ pattern classification. We envision that such integration will provide valuable insights in understanding the complex evaporative-driven assembly process and ultimately enable an autonomous optimisation of printing parameters that can robustly adapt to unexpected perturbations.

Integrated Water and Thermal Managements in Bioinspired Hierarchical MXene Aerogels for Highly Efficient Solar‐Powered Water Evaporation

AbstractSolar‐powered water evaporation is a straightforward, practical approach to use solar energy for water desalination. Solar absorbers made from photothermal materials capable of effectively confining heat and pumping water to the evaporation surface are essential for a high energy efficiency. However, separate designs of water transport routes and thermal insulating layers are required to simultaneously achieve desired water and thermal managements. This work reports an integrated design for efficient multifunctional capabilities through rational assembly of spectrally modified Ti3C2Tx (SM‐Ti3C2Tx) nanosheets and polyvinyl alcohol (PVA) into a multiscale 3D aerogel with a feather‐like microstructure. The aerogel contains longitudinal struts with transversely parallel ligaments developed at an angle of ≈60° from the struts, resembling the microstructure of down feathers in penguins and thus leading to excellent thermal insulation. The hydrophilic porous ligaments serve as upward water transport channels, pumping the water to the evaporation surface while confining it within the ligaments to avoid oversaturation. These functional features endow the composite aerogel with a high energy efficiency of 88.52% and an evaporation rate of 0.92 kg m−2 h−1 at a weak solar irradiance of 0.5‐sun, indicating its great potential for practical solar‐powered water desalination under natural sunlight.

Multiscale concurrent design and 3D printing of continuous fiber reinforced thermoplastic composites with optimized fiber trajectory and topological structure

3D printing of continuous fiber reinforced thermoplastic composites (CFRTPCs) enables the fabrication of multiscale structures, whose features can simultaneously span the microscale fiber trajectory and macroscale topological structure. In this study, a multiscale design and manufacturing strategy integrating concurrent optimization of micro fiber orientation and macro structural topology was developed for CFRTPCs and realized by ingenious path planning for 3D printing process. Typical structures, such as Messerschmitt-Bölkow-Blohm (MBB) beam and cantilever beam, were verified experimentally in comparison with the monoscale structures. Structural stiffness and peak load could be improved by 36.27% and 64.43% respectively for MBB beam, 123.07% and 52.16% respectively for cantilever beam, showing the significant influence on concurrent material and structure design for CFRTPCs. Multiscale concurrent design and 3D printing could promote the potential of CFRTPCs, and even challenge traditional design and manufacturing mechanism relating material and structure scale.

Double-ring-modulated light sheet fluorescence microscopic technique for multi-scale high-resolution 3D imaging

The predecessor of China Optics was China Optics and Applied Optics Digest, which was founded in 1985. At that time, it was the only retrieval journal in the field of optics in China. At the end of 2008, China Optics and Applied Optics Digest was renamed

Multi-Scale 3d Cellular Automata Modeling: Application to Wildland Fire Spread

Multi-Scale 3d Cellular Automata Modeling: Application to Wildland Fire Spread

DFFIR-net: Infrared Dim Small Object Detection Network Constrained by Gray-level Distribution Model

A new deep learning method based on the target gray-level distribution constraint mechanism model is proposed to solve the infrared dim small target detection problem in the complex environment. First, to solve the uneven distribution of positive and negative samples, the designed smoothness operator is used to suppress the background and enhancement target by measuring the difference in their features in 1D and 2D gradient. Second, an infrared dim small target detection network based on dense feature fusion, namely the DFFIR-net network, is proposed. The DFFIR-net enhances the feature expression of dim small targets by integrating the original features and the smoothness features of gray-level gradient. Also, the DFFIR-net alleviates the problem of sparse feature extraction. Finally, a multiscale 2D Gaussian label generation strategy is proposed. This strategy is critical in supervising the training of DFFIR-net in multi-dimensional Gaussian space, improving the feature exploration ability of the network and detection performance under small training samples. The experimental results show that compared with the existing advanced detection methods, the proposed method has higher accuracy and lower false alarm rates in various complex scenes.

A multi-scale E-jet 3D printing regulated by structured multi-physics field

Micro/nano scale structure as important functional part have been widely used in wearable flexible sensors, gas sensors, biological tissue engineering, microfluidic chips super capacitors and so on. Here a multi-scale electrohydrodynamic jet (E-jet) 3D printing approach regulated by structured multi-physics fields was demonstrated to generate 800 nm scale 2D geometries and high aspect ratio 3D structures. The simulation model of jetting process under resultant effect of top fluid field, middle electric field and bottom thermal field was established. And the physical mechanism and scale law of jet formation were studied. The effects of thermal field temperature, applied voltage and flow rate on the jet behaviors were studied; and the range of process parameters of stable jet was obtained. The regulation of printing parameters was used to manufacture the high resolution gradient graphics and the high aspect ratio structure with tight interlayer bonding. The structural features could be flexibly adjusted by reasonably matching the process parameters. Finally, polycaprolactone/polyvinylpyrrolidone (PCL/PVP) composite scaffolds with cell-scale fiber and ordered fiber spacing were printed. The proposed E-jet printing method provides an alternative approach for the application of biopolymer materials in tissue engineering.

Three-dimensional dispersion entropy for uterine fibroid texture quantification and post-embolization evaluation

Background and Objective: Uterine fibroids are benign tumors that could lead to symptoms complicating a patient’s daily life. Those fibroids can be treated using uterine fibroid embolization (UFE), an effective non-surgical procedure. However, objectively quantifying the benefits of such a procedure, and the patient’s quality of life, is rather challenging. Methods: With a novel multiscale three-dimensional (3D) entropy-based texture analysis, the multiscale 3D dispersion entropy (MDispEn3D), this work aims to objectively quantify the evolution – after UFE – of patients’ health in terms of quality of life, symptoms severity, and sexual function. For this purpose, clinical data and magnetic resonance imaging (MRI) scans of fibroids are analyzed before UFE (D0), ten days after (D10), and six months after (M6). Results: An inverse correlation is observed between MDispEn3D entropy values and both size and volume of fibroids. An inverse correlation is also observed between MDispEn3D at M6 and the scores of symptoms severity. Moreover, the patient age is found to be related to the relative difference of DispEn3D and MDispEn3D values, between D0 and M6, translating into an increasing entropy value. Furthermore, we show that history of fibroma plays a role in determining the obtained DispEn3D values at D0. Finally, we observe that the lower MDispEn3D values at D0, the larger the size of the fibroid at M6. Conclusions: The proposed MDispEn3D method - by quantifying fibroid texture - could assist the medical doctors in the prognosis of uterine fibroids and the patients’ quality of life assessment post-UFE. It could therefore favor the choice of this treatment compared to other more invasive surgical treatments.

Physics-based large-deformation analysis of coseismic landslides: A multiscale 3D SEM-MPM framework with application to the Hongshiyan landslide

A multiscale framework is developed based on the spectral element method (SEM) and material point method (MPM) for the large-deformation analysis of coseismic landslides. At a regional scale, SEM is used to model elastic wave propagation from a seismic source to a local site with complex topography. At a local scale, the coseismic landslide process and large deformation behavior are simulated by a nonlinear MPM model. Therefore, the proposed SEM-MPM model is able to reproduce the entire coseismic landslide process, including triggering, sliding and deposition. In this study, the coupled SEM-MPM method is used to simulate the massive Hongshiyan landslide triggered by the 2014 Ms6.5 Ludian earthquake in China. The study shows that the domain reduction method is able to reproduce the complex wave field across the SEM and MPM platforms, especially the strong topographic amplification of ground motion on the mountain ridge, which triggered the catastropic failure of the Hongshiyan landslide. The simulated volume of the sliding mass, sliding velocity, and final deposition profile are comparable with the field data and previous numerical studies, showing that the SEM-MPM method is a promising physics-based numerical tool for an integrated study of coseismic landslides.

Hydroprinting Technology to Transfer Ultrathin, Transparent, and Double-Sided Conductive Nanomembranes for Multiscale 3D Conformal Electronics.

Transparent multiscale 3D conformal electronics using hydroprinting with polyvinyl alcohol (PVA) as a sacrificial layer to transfer networks of silver nanowires (AgNWs) without a carrier layer is developed. However, AgNWs are known to disperse on water surfaces during the transfer process. Therefore, a functional film is developed by simultaneously welding and embedding AgNWs in the PVA through a simple one-step thermal pressing, demonstrating that ultrathin, transparent, and double-sided conductive/patterned nanomembranes with welded AgNWs can float on water without dispersion. The nanomembrane with an excellent figure of merit of 1200, a low sheet resistance of 16.2 Ωsq-1 , and a high transmittance of 98.17% achieves conformal contact with excellent step surface coverage of complex macro- and microstructures because of its nanoscale thickness (54.39nm) and numerous deformable micro- and nanopores. Furthermore, the double-sided conductive nanomembranes facilitate wiring and layer-by-layer assembly, regardless of the transfer direction of the surface. As a proof-of-concept demonstration, a nanomembrane-based aneurysm sensor is developed. Its high transparency enables coil embolization, and the sensor can measure the pushing force of the coil within an aneurysm in an endovascular simulator. Moreover, this newly developed hydroprinting technology provides a new method for the fabrication of transparent multiscale 3D conformal electronics.

An Investigation of Integrated Multiscale Three-Dimensional Printing for Hierarchical Structures Fabrication

Abstract Nature provides us with a large number of functional material systems consisting of hierarchical structures, where significant variations in dimensions are present. Such hierarchical structures are difficult to build by traditional manufacturing processes due to manufacturing limitations. Nowadays, three-dimensional (3D) objects with complex structures can be built by gradually accumulating in a layer-based additive manufacturing (AM); however, the hierarchical structure measured from macroscale to nanoscale sizes still raises significant challenges to the AM processes, whose manufacturing capability is intrinsically specified within a certain scope. It is desired to develop a multiscale AM process to narrow this gap between scales of feature in hierarchical structures. This research aims to investigate an integration approach to fabricating hierarchical objects that have macro-, micro-, and nano-scales features in an object. Firstly, the process setup and the integrated process of two-photon polymerization (TPP), immersed surface accumulation (ISA), and mask image projection-based stereolithography (MIP-SL) were introduced to address the multiscale fabrication challenge. Then, special hierarchical design and process planning toward integrating multiple printing processes are demonstrated. Lastly, we present two test cases built by our hierarchical printing method to validate the feasibility and efficiency of the proposed multiscale hierarchical printing approach. The results demonstrated the capability of the developed multiscale 3D printing process and showed its future potential in various novel applications, such as optics, microfluidics, cell culture, as well as interface technology.

Multiscale 3D Analysis of Defects and Temporal Development of Electrode Morphology in Lithium-Ion Batteries By X-Ray Computed Tomography

X-ray computed tomography is a 3D imaging technique that is able to provide information about both inner and outer parts of battery cells without their destruction. We will present this technique allowing us access to the batteries data in the whole range of scales – from the analysis of entire cells down to microstructure of electrodes. We will focus on the changes in the structure during the battery's aging process and the manufacturing defects.

Spectral-spatial feature fusion via dual-stream deep architecture for hyperspectral image classification

Recently, deep learning has been widely used in hyperspectral images (HSIs) classification to extract spectral-spatial information. However, how to fuse and utilize spectral-spatial features more efficiently is a challenging task. To address this issue, we propose a spectral-spatial feature fusion via dual-stream deep architecture (SSDS) for HSIs classification in this letter that integrates two core modules of dual-stream and feature fusion. In detail, one stream applies a bi-direction gated recurrent unit (Bi-GRU) to extract spectral features at the pixel level, and another takes the corresponding image patch as an input for a shallow multi-scale parallel 2D convolutional neural network (CNN) to extract multi-scale spatial features. Subsequently, the spectral and spatial features of HSIs are fused by feature fusion module that consists of fusion-wise pooling and fully connected layers to learn discriminative and useful features adaptively. Moreover, a spectral attention module is designed to further extract correlation among spectral bands and reduce interference from irrelevant features. Experimental results on three HSIs datasets, i.e., Pavia University, Salinas, and Kennedy Space Center, demonstrate the effectiveness of the proposed SSDS method. The overall accuracy of SSDS on such three HSIs datasets is 99.22%, 97.99%, and 94.35%, respectively, which increases at least 0.4%, 0.33%, and 0.36% compared with other state-of-the-art methods. In addition, SSDS is less time-consuming than most baselines. For instance, the training time on Salinas data decreases from 1226.7 min (3DCNN) to 31.6 min.

Multiscale Characterization of an Oxide Scale Formed on the Creep-Resistant ATI 718Plus Superalloy during High-Temperature Oxidation.

The ATI 718Plus® is a creep-resistant nickel-based superalloy exhibiting high strength and excellent oxidation resistance in high temperatures. The present study is focused on multiscale 2D and 3D characterization (morphological and chemical) of the scale and the layer beneath formed on the ATI 718Plus superalloy during oxidation at 850 °C up to 4000 h in dry and wet air. The oxidized samples were characterized using various microscopic methods (SEM, TEM and STEM), energy-dispersive X-ray spectroscopy and electron diffraction. The 3D visualization of the microstructural features was achieved by means of FIB-SEM tomography. When oxidized in dry air, the ATI 718Plus develops a protective, dense Cr2O3 scale with a dual-layered structure. The outer Cr2O3 layer is composed of coarser grains with a columnar shape, while the inner one features fine, equiaxed grains. The Cr2O3 scale formed in wet air is single-layered and features very fine grains. The article discusses the difference between the structure, chemistry and three-dimensional phase distribution of the oxide scales and near-surface areas developed in the two environments. Electron microscopy/spectroscopy findings combined with the three-dimensional reconstruction of the microstructure provide original insight into the role of the oxidation environment on the structure of the ATI 718Plus at the nanoscale.

Customizable, conformal, and stretchable 3D electronics via predistorted pattern generation and thermoforming.

Recently, three-dimensional electronics (3DE) is attracting huge interest owing to the increasing demands for seamless integration of electronic systems on 3D curvilinear surfaces. However, it is still challenging to fabricate 3DE with high customizability, conformability, and stretchability. Here, we present a fabrication method of 3DE based on predistorted pattern generation and thermoforming. Through this method, custom-designed 3DE is fabricated through the thermoforming process. The fabricated 3DE has high 3D conformability because the thermoforming process enables the complete replication of both the overall shape and the surface texture of the 3D mold. Furthermore, the usage of thermoplastic elastomer and a liquid metal–based conductive electrode allows for high thermoformability during the device fabrication as well as high stretchability during the device operation. We believe that this technology can enable a wide range of new functionalities and multiscale 3D morphologies in wearable electronics.

Multiscale and integrative single-cell Hi-C analysis with Higashi

Single-cell Hi-C (scHi-C) can identify cell-to-cell variability of three-dimensional (3D) chromatin organization, but the sparseness of measured interactions poses an analysis challenge. Here we report Higashi, an algorithm based on hypergraph representation learning that can incorporate the latent correlations among single cells to enhance overall imputation of contact maps. Higashi outperforms existing methods for embedding and imputation of scHi-C data and is able to identify multiscale 3D genome features in single cells, such as compartmentalization and TAD-like domain boundaries, allowing refined delineation of their cell-to-cell variability. Moreover, Higashi can incorporate epigenomic signals jointly profiled in the same cell into the hypergraph representation learning framework, as compared to separate analysis of two modalities, leading to improved embeddings for single-nucleus methyl-3C data. In an scHi-C dataset from human prefrontal cortex, Higashi identifies connections between 3D genome features and cell-type-specific gene regulation. Higashi can also potentially be extended to analyze single-cell multiway chromatin interactions and other multimodal single-cell omics data.

Construction of 3D geological model and multi-scale numerical analysis of in-situ stress field of long tunnel in fault development area

Affected by faults of different scales, there is a big difference between the local stress and regional stress in the crust. The Xianglushan tunnel of Central Yunnan Water Diversion Project is located in the Sichuan-Yunnan block with violent tectonic activity, and the tunnel passes through complex strata and fault development areas. Due to the limitations of testing methods, the measured stress data in Xianglushan tunnel project are mainly distributed in the relatively intact rock mass, so it is difficult to reflect the stress characteristics in the fault-affected area. Therefore, based on the tectonic geological data of Xianglushan tunnel, this article explores the construction method of multi-scale 3D geological model of fault development area. The corresponding information processing mechanism is established to realize the interactive interpretation, supplement and correction of fault data, and then gradually approach the actual situation of the geological body in the modeling process. According to the modeling method, a generalized large-scale model and a refined sub-model were established separately for the whole Xianglushan tunnel project area and the Longpan-Qiaohou fault affected area. At the same time, combined with the stress test data of Xianglushan tunnel, the first-stage stress field inversion calculation of large scale model is carried out based on multiple regression analysis method. The macroscopic stress field distribution characteristics of the strata and fault zones along the tunnel are preliminarily explored. From the first-stage inversion result, the displacement boundary conditions of the sub-model can be obtained by interpolation in the displacement field, which is applied to sub-model for the secondary elastic-plastic iterative calculation. Finally, the construction of the refined sub-model and the secondary elastic-plastic iterative calculation not only optimize the detail features of the initial stress field obtained by the first-stage inversion analysis, but also reveal the disturbance law of the complex strata and the Longpan-Jiaohou fault on the initial stress state, which has a high guiding significance for the construction and support of tunnel engineering.

X-ray microscopy enables multiscale high-resolution 3D imaging of plant cells, tissues, and organs.

Capturing complete internal anatomies of plant organs and tissues within their relevant morphological context remains a key challenge in plant science. While plant growth and development are inherently multiscale, conventional light, fluorescence, and electron microscopy platforms are typically limited to imaging of plant microstructure from small flat samples that lack a direct spatial context to, and represent only a small portion of, the relevant plant macrostructures. We demonstrate technical advances with a lab-based X-ray microscope (XRM) that bridge the imaging gap by providing multiscale high-resolution three-dimensional (3D) volumes of intact plant samples from the cell to the whole plant level. Serial imaging of a single sample is shown to provide sub-micron 3D volumes co-registered with lower magnification scans for explicit contextual reference. High-quality 3D volume data from our enhanced methods facilitate sophisticated and effective computational segmentation. Advances in sample preparation make multimodal correlative imaging workflows possible, where a single resin-embedded plant sample is scanned via XRM to generate a 3D cell-level map, and then used to identify and zoom in on sub-cellular regions of interest for high-resolution scanning electron microscopy. In total, we present the methodologies for use of XRM in the multiscale and multimodal analysis of 3D plant features using numerous economically and scientifically important plant systems.