Multiscale Structure Research Articles

Construction and 3D Printing of Multiscale Silk Fiber‐Cellulose Composite Networks Based on Ionic Liquids

AbstractMicroscopic defects in the amorphous regions of natural cellulose affect the mechanical properties of their materials. After dissolution and regeneration, cellulose's mechanical properties are still insufficient for application in advanced materials. Conventional compounding and crosslinking techniques with toxic crosslinking agents are not only cumbersome, but also have limited performance enhancement in cellulose materials Therefore, much attention has been paid to designing and developing more straightforward and efficient cellulose reinforcement technologies. Herein, an in situ multiscalarization strategy is used to form multiscale silk fiber‐cellulose composites (MS‐C) by the gradual nanosizing of silk fibers (SF) under heating in a 1‐butyl‐3‐methylimidazolium chloride solution of cellulose (Cel‐BmimCl). MS‐C are constructed from micro and nano SF, fibroin chains, and cellulose molecules into a multidimensional network. Due to its advantage of multiscale structure, MS‐C has excellent mechanical properties, the tensile strength and Young's modulus, which are 123.06% and 91.68% higher than that of pure regenerated cellulose after 5h heating treatment. Besides, MS‐C has good biocompatibility and can be molded or 3D printed with direct ink writing, showing broad application prospects in medical, bioengineering, and other fields. This multiscale enhancement strategy can also be extended to the research and application of biopolymers such as natural polysaccharides or proteins.

Efficient Small Object Detection You Only Look Once: A Small Object Detection Algorithm for Aerial Images

Aerial images have distinct characteristics, such as varying target scales, complex backgrounds, severe occlusion, small targets, and dense distribution. As a result, object detection in aerial images faces challenges like difficulty in extracting small target information and poor integration of spatial and semantic data. Moreover, existing object detection algorithms have a large number of parameters, posing a challenge for deployment on drones with limited hardware resources. We propose an efficient small-object YOLO detection model (ESOD-YOLO) based on YOLOv8n for Unmanned Aerial Vehicle (UAV) object detection. Firstly, we propose that the Reparameterized Multi-scale Inverted Blocks (RepNIBMS) module is implemented to replace the C2f module of the Yolov8n backbone extraction network to enhance the information extraction capability of small objects. Secondly, a cross-level multi-scale feature fusion structure, wave feature pyramid network (WFPN), is designed to enhance the model’s capacity to integrate spatial and semantic information. Meanwhile, a small-object detection head is incorporated to augment the model’s ability to identify small objects. Finally, a tri-focal loss function is proposed to address the issue of imbalanced samples in aerial images in a straightforward and effective manner. In the VisDrone2019 test set, when the input size is uniformly 640 × 640 pixels, the parameters of ESOD-YOLO are 4.46 M, and the average mean accuracy of detection reaches 29.3%, which is 3.6% higher than the baseline method YOLOv8n. Compared with other detection methods, it also achieves higher detection accuracy with lower parameters.

Nanostructure-induced functional combination of vanishing magnetostriction and magnetic softness in ferromagnetic (GaNi)xCoCrFe (x = 0.4–1.6) high-entropy alloys

Searching for high-entropy alloys with functional properties that emerge from their multi-scale structure, we have investigated the (GaNi)xCoCrFe (x = 0.4–1.6) system. We have characterized structure, microstructure, nanostructure and chemical composition of the individual phases in the multi-phase alloys and determined their magnetic, magnetostrictive and electrical properties. We found that the alloys are ferromagnetic and exhibit functional combination of magnetic softness and vanishing magnetostriction, classifying them as energy-efficient “supersilent” materials (inaudible to a human ear) for alternating-current (AC) electromagnetic applications in the audio-frequency range. The alloys develop a two-phase structure, a face-centered cubic (fcc) and a body-centered cubic (bcc), where the fcc phase fraction decreases, while the bcc fraction increases with the increasing (GaNi)x content. Ferromagnetism of the alloys originates from the highly nanostructured bcc phase, with the ferromagnetic Curie temperatures in the range TC = 750–700 K, depending on x. The fcc phase is not nanostructured and is paramagnetic at room temperature, but undergoes a spin glass transition at Tf≈6.4 K. The magnetic softness and vanishing magnetostriction of the alloys are both nanomagnetic phenomena. The magnetic-softness and magnetostriction parameters of the x = 1.3 and 1.6 alloys make them relevant for supersilent AC applications at low frequencies.

Structural, properties and digestion in vitro changes of starch subjected to high pressure homogenization: An update review.

High pressure homogenization (HPH) is considered as a promising method for improving the ideal metabolic reaction of starch-based foods in the body, but there is still no comprehensive understanding of the structure-property relationship of starch treated with HPH. This study reviews the advantages and limitations of HPH in starch-based foods processing in recent years. It also elaborates the bidirectional regulation of HPH on starch structure-property and its potential in improving nutritional quality, which includes the regular modification effects of HPH on the multi-scale structure, physicochemical properties, and digestion characteristics of starch. It was found that HPH could lead to the degradation of amylopectin, destruction of amorphous structure, and homogenization of fine particles, promoting gelatinization and ultimately endowing starch with good solubility and digestibility. Moreover, it could reorganize and reorder the internal starch chains, or cause the particles to disintegrate into an amorphous state, thereby enhancing the anti-digestibility of starch. The interaction of starch with different nutrients during the HPH process could be further investigated in future studies and explored with other techniques for structure-property modifications, which would help expand the development of personalized starch foods to meet growing consumer demands.

A novel connectivity index for microstructures imperfection detection and rectification in multiscale structure design

A novel connectivity index for microstructures imperfection detection and rectification in multiscale structure design

Correlative X-ray micro-nanotomography with scanning electron microscopy at the Advanced Light Source.

Geological samples are inherently multi-scale. Understanding their bulk physical and chemical properties requires characterization down to the nano-scale. A powerful technique to study the three-dimensional microstructure is X-ray tomography, but it lacks information about the chemistry of samples. To develop a methodology for measuring the multi-scale 3D microstructure of geological samples, correlative X-ray micro- and nanotomography were performed on two rocks followed by scanning electron microscopy with energy-dispersive spectroscopy (SEM-EDS) analysis. The study was performed in five steps: (i) micro X-ray tomography was performed on rock sample cores, (ii) samples for nanotomography were prepared using laser milling, (iii) nanotomography was performed on the milled sub-samples, (iv) samples were mounted and polished for SEM analysis and (v) SEM imaging and compositional mapping was performed on micro and nanotomography samples for complimentary information. Correlative study performed on samples of serpentine and basalt revealed multiscale 3D structures involving both solid mineral phases and pore networks. Significant differences in the volume fraction of pores and mineral phases were also observed dependent on the imaging spatial resolution employed. This highlights the necessity for the application of such a multiscale approach for the characterization of complex aggregates such as rocks. Information acquired from the chemical mapping of different phases was also helpful in segmentation of phases that did not exhibit significant contrast in X-ray imaging. Adoption of the protocol used in this study can be broadly applied to 3D imaging studies being performed at the Advanced Light Source and other user facilities.

Multi-scale and time-resolved structure analysis of relaxor ferroelectric crystals under an electric field

Lead-based relaxor ferroelectrics exhibit giant piezoelectric properties owing to their heterogeneous structures. The average and local structures measured by single-crystal X-ray diffraction under DC and AC electric fields are reviewed in this article. The position-dependent local lattice strain and the distribution of polar nanodomains and nanoregions show strong electric field dependence, which contributes to the giant piezoelectric properties.

Visual detection of drilling robot position for rockburst prevention in mining processing by a new image dehazing method

AbstractThe positioning of drill pipe based on visual detection is a crucial aspect in achieving unmanned operation of drilling robot for rockburst prevention. However, the images directly collected from mechanized mining face are always polluted. In order to eliminate the interference of dust and haze in the image, an image dehazing method based on improved CycleGAN model is proposed in this paper. Firstly, a pipe image dehazing dataset for the rockburst prevention drilling robot is collected and established. Moreover, a generator architecture with a multi-scale U-shaped network structure is designed to improve the quality and accuracy of image recovery. A new reconstruction block is designed and an SK fusion layer is introduced to improve the feature extraction capability of the model, and a MU-CycleGAN network structure is constructed. Finally, an experimental platform for drill pipe image dehazing of the drilling robot for rockburst prevention is set up in the intelligent mining equipment laboratory. Experimental results showed that the PSNR and SSIM of the image dehazing model achieved 27.04 and 0.946, and the success rate of drill pipe grabbing has increased by 13.75%. Experimental results reveal that the proposed framework achieves superior image enhancement performance than the comparison algorithms.

WTDUN: Wavelet Tree-Structured Sampling and Deep Unfolding Network for Image Compressed Sensing

Deep unfolding networks have gained increasing attention in the field of compressed sensing (CS) owing to their theoretical interpretability and superior reconstruction performance. However, most existing deep unfolding methods often face the following issues: 1) they learn directly from single-channel images, leading to a simple feature representation that does not fully capture complex features; and 2) they treat various image components uniformly, ignoring the characteristics of different components. To address these issues, we propose a novel wavelet-domain deep unfolding framework named WTDUN, which operates directly on the multi-scale wavelet subbands. Our method utilizes the intrinsic sparsity and multi-scale structure of wavelet coefficients to achieve a tree-structured sampling and reconstruction, effectively capturing and highlighting the most important features within images. Specifically, the design of tree-structured reconstruction aims to capture the inter-dependencies among the multi-scale subbands, enabling the identification of both fine and coarse features, which can lead to a marked improvement in reconstruction quality. Furthermore, a wavelet domain adaptive sampling method is proposed to greatly improve the sampling capability, which is realized by assigning measurements to each wavelet subband based on its importance. Unlike pure deep learning methods that treat all components uniformly, our method introduces a targeted focus on important subbands, considering their energy and sparsity. This targeted strategy lets us capture key information more efficiently while discarding less important information, resulting in a more effective and detailed reconstruction. Extensive experimental results on various datasets validate the superior performance of our proposed method.

Digital rock modeling of deformed multi-scale media in deep hydrocarbon reservoirs based on in-situ stress-loading CT imaging and U-Net deep learning

Deep and ultra-deep hydrocarbon reservoirs are regarded as an attractive target for onshore oil and gas exploration and development in China. The multi-scale fractures are widely distributed in this type of reservoir. The fluid-solid coupling effect is strong, which can greatly affect the fluid flow, resulting in a low hydrocarbon recovery with the average value less than 15%. To explore the underlying flow dynamics during efficient development of deep and ultra-deep hydrocarbon reservoirs, it is of great importance to characterize the multi-scale fracture-pore structure evolution of rock affected by strong geo-stress field variation. To tackle the above issues, an actual rock drilled from a typical ultra-deep reservoir in Tarim Basin are selected to conduct in-situ stress-loading computed tomography (CT) scanning experiments, and the CT gray images of rock microstructure under different dynamic loading conditions are obtained. A fully convolutional neural network (U-Net) deep learning segmentation algorithm is introduced to accurately distinguish the rock skeleton, pore space and fractures in deep rock. The three-dimensional (3-D) digital rock model of deformed multi-scale fracture-porous media under different dynamic loading conditions is ultimately established to investigate the evolution of fracture morphology and deep rock microstructure as the in-situ stress is gradually loaded. It indicates that, the U-Net deep learning semantic segmentation algorithm can accurately segment CT images of deep rocks, and the established 3D digital rock model of fractured porous media can accurately represent the pore-throat distribution, fracture morphology, and pore-scale topological connectivity. At the initial stage of stress loading, there are few micro-fractures inside the rock, and the fracture connectivity is poor. Due to effect of rock compaction, the average pore throat radius increases while pore throat ratio, coordination number and tortuosity greatly decrease. As the effective stress is gradually loaded, the micro-fractures begin to propagate, leading to stronger heterogeneity of micro-fractures’ distribution and better topological connectivity, and both the coordination number and tortuosity gradually increase. After the deep rock is fractured, micro-fractures run through a fracture network and all the above topological parameters increase greatly.

MPFEM investigation on densification and mechanical structures during ferrous powder compaction

Metal powder compaction is a crucial process in powder metallurgy, significantly affecting the final properties of compacts. However, the quantitative characteristics of multi-scale mechanical structures and the microscopic densification behavior, taking into account the influence of friction conditions, remain unclear. This study utilises a two-dimensional multi-particle finite element method to analyse the ferrous powder compaction. The evolution of powder densification, powder deformation and multi-scale mechanical behaviour under different friction coefficient conditions are analyzed quantitatively and qualitatively. Results reveal that powder densification occurs in distinct stages, with lower friction coefficients promoting greater powder densification, as observed from relative density and coordination number. Additionally, as the axial strain increases, plastic strain gradually rises whilst roundness decreases. Higher friction coefficients are associated with higher equivalent plastic strain but lower powder roundness. The Von Mises stress exhibits different stages of increase with the increment of axial strain. Powders with lower friction coefficients exhibit lower levels of Von Mises stress. As axial strain increases, the number, length, strength and direction coefficient of force chains undergo different evolution processes. Force chains exhibit longer length, fewer numbers, lower strength and lower direction coefficients at lower friction coefficients.

Self-Assembly Hybrid Manufacture of Nanoarrays for Metasurfaces.

The development of metasurfaces necessitates the rapid fabrication of nanoarrays on diverse substrates at large scales, the preparation of patterned nanoarrays on both planar and curved surfaces, and even the creation of nanoarrays on prefabricated structures to form multiscale metastructures. However, conventional fabrication methods fall short of these rigorous requirements. In this work, a novel self-assembly hybrid manufacturing (SAHM) method is introduced for the rapid and scalable fabrication of shape-controllable nanoarrays on various rigid and flexible substrates. This method can be easily integrated with other fabrication techniques, such as lithography and screen printing, to produce patterned nanoarrays on both planar and non-developable surfaces. Utilizing the SAHM method, nanoarrays are fabricated on prefabricated micropillars to create multiscale pillar-nanoarray metastructures. Measurements indicate that these multiscale metastructures can manipulate electromagnetic waves across a range of wavelengths. Therefore, the SAHM method demonstrates the potential of multiscale structures as a new paradigm for the design and fabrication of metasurfaces.

Rational MOF Membrane Design for Gas Detection in Complex Environments.

Metal-organic frameworks (MOFs) hold significant promise in the realm of gas sensing. However, current understanding of their sensing mechanisms remains limited. Furthermore, the large-scale fabrication of MOFs is hampered by their inadequate mechanical properties. These two challenges contribute to the sluggish development of MOF-based gas-sensing materials. In this review, the selection of metal ions and organic ligands for designing MOFs is first presented, deepening the understanding of the interactions between different metal ions/organic ligands and target gases. Subsequently, the typical interfacial synthesis strategies (gas-solid, gas-liquid, solid-liquid interfaces) are provided, highlighting the potential for constructing MOF membranes on superhydrophobic and/or superhydrophilic substrates. Then, a multi-scale structure design strategies is proposed, including multi-dimensional membrane design and heterogeneous membrane design, to improve sensing performance through enhanced interfacial mass transfer and specific gas sieving. This strategy is anticipated to augment the task-specific capabilities of MOF-based materials in complex environments. Finally, several key future research directions are outlined with the aim not only to further investigate the underlying sensing principles of MOF membranes but also to achieve efficient detection of target gases amidst interfering gases and elevated moisture levels.

Supramolecular structure and in vitro digestive properties of plasma-treated corn starches varying in amylose content

The effects of plasma treatment on the multi-scale structure and in vitro digestibility of maize starch with different amylose contents were systematically evaluated. The results demonstrated that all maize starches' molecular weights (MW) decreased when treated with plasma, among which the MW of waxy maize starch displayed the largest reduction. Plasma treatment led to an increase in the thickness of the semi-crystalline lamellae and double helix proportions of waxy and normal maize starches. However, high-amylose maize starch presented a less ordered structure by plasma treatment. Additionally, larger pores and channels were observed on the surface of plasma-treated waxy and normal maize starch granules. Moreover, deposits were displayed on the surface of high-amylose maize starch granules. These changes increased the in vitro digestibility and hydrolysis rate of three starches after plasma treatment. Notably, plasma treatment caused diverse alterations in the structure and functionality of maize starch varying in amylose content, leading to maize starch with better digestibility, therefore being used as an ingredient for functional foods.

Achieving strength-ductility balance in AZ31-WE43 Mg alloys by symmetric extrusion-shear process at various temperatures

The development of composites that simultaneously improve strength and plasticity is a challenging task, especially in Mg alloys. The focus of this study is to create a novel multiscale gradient structure (GS) in Mg laminates to exhibit optimal strength-plasticity synergies through a temperature-controlled extrusion shear (TCES) and extrusion composite process with preset grain sizes. Initially, the gradient structure is formed by a short extrusion process, leading to an increase in the average grain size of the elongated coarse grains from the middle layer of the equiaxed fine grain zone to the elongated coarse grain surface layer. The resulting gradient structure improves the material synergy. The microstructure evolution mechanism of the alloy during symmetric plastic deformation was analyzed. The initial grains underwent DDRX, CDRX, and shear deformation-induced dynamic recrystallization (SDDRX) during the RZ extrusion stage, the grains were significantly refined, and the basal texture intensity was significantly weakened during the SD extrusion stage. The effect of extrusion temperature (400 °C, 450 °C, 500 °C) on the alloys in the forming zone after ES extrusion was further investigated. Tensile tests on Mg laminates prepared by the extrusion method showed that the ultimate tensile strength (UTS) of the samples prepared at 450 °C was 265 MPa and the elongation (EL) was 23.8 %. The significant improvement in the yield tensile strength (YTS) of the composite Mg alloys post-ES processing is primarily attributed to the fine-grained (FG) interior, while the increase in elongation is due to the coarse-grained (CG) exterior. This study presents a novel approach to enhancing the mechanical properties of Mg alloys, offering a promising direction for the development of advanced materials with superior strength and ductility.

Rolling bearing fault diagnosis method based on MSDCNN in strong noise environment

Aiming at the problems of poor noise resistance, high computational complexity and insufficient generalization performance of traditional bearing fault diagnosis methods based on deep learning, a rolling bearing fault diagnosis method based on multi-scale dynamic convolutional neural network (MSDCNN) is proposed. Firstly, the one-dimensional vibration signal of the rolling bearing is converted to the frequency domain by Fourier transform, and the features are further extracted by wide convolution kernel. Secondly, a multi-scale dynamic convolution structure is proposed, and the feature information extracted by convolution kernels of different sizes is given different weights by using an improved channel attention mechanism. Then, a self-calibrating spatial attention mechanism (SCSAM) is designed, and the extracted feature information is input into the spatial attention mechanism to capture the importance of different regions. Finally, the features are further extracted by small convolution kernels, and the fault category is classified by using Softmax classifier. The fault diagnosis performance of the proposed model is verified by three different data sets. The experimental results show that compared with other intelligent models, the proposed model has higher classification accuracy, better generalization ability and stronger robustness under strong noise background.

Revisiting the Evolution of Multi-Scale Structures of Starches with Different Crystalline Structures During Enzymatic Digestion.

Porous starch has been created through hydrolysis by amyloglucosidase and α-amylase. However, little information is known about the precise evolution of multi-scale structures of starch during digestion. In this study, rice starch and potato starch, containing different crystalline structures, were hydrolyzed by amyloglucosidase and α-amylase for 20 and 60 min, respectively, and their resulting structural changes were examined. The digestion process caused significant degradation of the molecular structures of rice and potato starches. In addition, the alterations in the ordered structures varied between the two starches. Rice starch exhibited porous structures, thicker crystalline lamellae as determined by small-angle X-ray scattering, and enhanced thermostability after digestion using differential scanning calorimetry. For rice starch, the extent of crystalline structures was analyzed with an X-ray diffractometer; it was found to first increase after 20 min of digestion and then decrease after 60 min of digestion. In contrast, potato starch did not display porous structures but exhibited thicker crystalline lamellae and a reduction in ordered structures after digestion. These findings suggest that it is possible to intentionally modulate the multi-scale structures of starch by controlling the digestion time, thereby providing valuable insights for the manipulation of starch functionalities.

MSMP-Net: A Multi-Scale Neural Network for End-to-End Monkeypox Virus Skin Lesion Classification

Monkeypox is a zoonotic disease caused by monkeypox virus infection. It is easily transmitted among people and poses a major threat to human health, making it of great significance in public health. Therefore, this paper proposes MSMP-Net, a multi-scale neural network for end-to-end monkeypox virus skin lesion classification ConvNeXt is used as the backbone network, and designs such as inverse bottleneck layers and large convolution kernels are used to enhance the network’s feature extraction capabilities. In order to effectively utilize the multi-level feature maps generated by the backbone network, a multi-scale feature fusion structure was designed. By fusing the deepest feature maps of multi-scale features, the model’s ability to represent monkeypox image features is enhanced. Experimental results show that the accuracy, precision, recall, and F1-score of this method on the MSLD v2.0 dataset are 87.03 ± 3.43%, 87.59 ± 3.37%, 87.03 ± 3.43%, and 86.58 ± 3.66%, respectively.

Unique framework effect induced by uniform silk fibroin dynamic nanospheres enables multiscale hydrogel with outstanding elastic resilience and strain sensing performance

It is a significant challenge to obtain hydrogels simultaneously with low tensile energy dissipation, high compressive resilience and long durability. Herein, the uniform dynamic nanospheres (Sil-4H0.75) derived from 4-Hydroxybutyl acrylate glycidyl ether grafted silk fibroin is designed to overcome this issue. Due to its uniform and dynamic characteristic, Sil-4H0.75 could endow hydrogel with homogeneous multiscale structure and produce unique framework effect. Thus, transparent Sil-4H0.75 crosslinked acrylamide hydrogel doped with Ag nanowires APS3.75%/AgNW0.1 exhibits a high stretchability (1260 %) and outstanding elastic resilience. The tensile energy dissipation ratio maintains a low value of 9 % across a wide 800 % strain range. A high compression resilience ratio of 92.2 % is kept after ten compression cycles under 90 % compressive strain. The orderly AgNWs motion guided by framework effect also make it be used as both tensile and compressive sensors and exhibits high gauge factor of 7.35, outstanding compression sensitivity of 30.379 kPa−1 and excellent durability (up to 2000 cycles). The detection or other applications based on both two sensing modes are also demonstrated. In a word, this work affords a general strategy to achieve high-performance hydrogel based on uniform dynamic nanospheres which exhibits great potential in the applications of flexible wearable strain sensors.

Biomimetic multiscale structure with hierarchically entangled topologies of cellulose-based hydrogel sensors for human-computer interaction

The development of high-performance cellulose-based sensors with superior interfacial compatibility, flexibility, and strength has always been challenging. Drawing inspiration from the intricate multiscale hierarchy found in resilient natural materials, the incorporation of this structure into cellulose-based hydrogels using biomimetic strategies is anticipated to enhance their properties. Therefore, the cellulose/polyacrylamide (PAM) hybrid hydrogels are fabricated using the aqueous AlCl3/ZnCl2 system through an all-green one-pot method at room temperature, achieving efficient dissolution of cellulose at multiple scales and in-situ polymerization of polyacrylamide. The multiscale cellulose/PAM hydrogels achieve good interfacial compatibility, with the reinforcing mechanism being confirmed through a combination of experimental validation and atomic simulations. The findings underscore the key elements that drive the multiscale behavior of hydrogels, encompassing a range of factors such as multiple interfacial interactions, hierarchical molecular entanglements, and microskeleton reinforcement. The hydrogels exhibit satisfactory stretchability (412 %), compressive strength (1.2 MPa), conductivity (1.65 S m−1), and ultralong circulability (> 12,000 cycles), rendering them suitable for various scenarios of human-computer interaction. This work establishes a comprehensive interpretation of the strengthening mechanism in cellulose-based multiscale hierarchical structures, thereby providing novel insights for the advanced design strategies of other promising hierarchical materials.