Individual 3D Research Articles

Evaluation of pore-fracture microstructure of gypsum rock fragments using micro-CT

This study utilized X-ray micro-computed tomography (micro-CT) to investigate weathered gypsum rocks which can or do serve as a rock substrate for endolithic organisms, focusing on their internal pore-fracture microstructure, estimating porosity, and quantitative comparison between various samples. Examining sections and reconstructed 3D models provides a more detailed insight into the overall structural conditions within rock fragments and the interconnectivity in pore networks, surpassing the limitations of analyzing individual 2D images. Results revealed diverse gypsum forms, cavities, fractures, and secondary features influenced by weathering. Using deep learning segmentation based on the U-Net models within the Dragonfly software enabled to identify and visualize the porous systems and determinate void space which was used to calculate porosity. This approach allowed to describe what type of microstructures and cavities is responsible for the porous spaces in different gypsum samples. A set of quantitative analysis of the detected void and modeled networks provided a needed information about the development of the pore system, connectivity, and pore size distribution. Comparison with mercury intrusion porosimetry showed that both methods consider different populations of pores. In our case, micro-CT typically detects larger pores (> 10 μm) which is related to the effective resolution of the scanned images. Still, micro-CT demonstrated to be an efficient tool in examining the internal microstructures of weathered gypsum rocks, with promising implications particularly in geobiology and microbiology for the characterization of lithic habitats.

Support-Based Transfer and Contacting of Individual Nanomaterials for In Situ Nanoscale Investigations.

Although in situ transmission electron microscopy (TEM) of nanomaterials has been gaining importance in recent years, difficulties in sample preparation have limited the number of studies on electrical properties. Here, a support-based preparation method of individual 1D and 2D materials is presented, which yields a reproducible sample transfer for electrical investigation by in situ TEM. A mechanically rigid support grid facilitates the transfer and contacting to in situ chips by focused ion beam with minimum damage and contamination. The transfer quality is assessed by exemplary specimens of different nanomaterials, including a monolayer of WS2 . Possible studies concern the interplay between structural properties and electrical characteristics on the individual nanomaterial level as well as failure analysis under electrical current or studies of electromigration, Joule heating, and related effects. The TEM measurements can be enriched by additional correlative microscopy and spectroscopy carried out on the identical object with techniques that allow a characterization with a spatial resolution in the range of a few microns. Although developed for in situ TEM, the present transfer method is also applicable to transferring nanomaterials to similar chips for performing further studies or even for using them in potential electrical/optoelectronic/sensingdevices.

Interdisciplinary all-on-four® concept for mandibular jaw in dental education - do students benefit from individual 3d printed models from real patient cases?

PurposeDigitalization is assuming increasing significance in dental education, as dental students are increasingly exposed to digital implant planning and contemporary technologies such as 3D printing. In this study, we present a cohort analysis aimed at assessing the potential benefits derived from the utilization of 3D prints to seamlessly translate planned procedures into real-life applications.Methods21 dental students participated in a virtual planning and hands-on course across two cohorts (C1: n = 10, C2: n = 11). The virtual implant planning phase involved the placement of four implants on an atrophic lower jaw model. Subsequently, Cohort 1 (C1) executed the implantation procedure on a prefabricated hands-on model, while Cohort 2 (C2) engaged with 3D prints representing their individual implant planning during the hands-on session. Subjective assessments of knowledge, skills, and the perceived utility of 3D prints were conducted through pre- and post-course questionnaires, utilizing a 5-point scale.ResultsIn the subjective evaluation, 17 out of 21 participants expressed a positive appraisal of the use of personalized models. Notably, there was no statistically significant improvement in overall knowledge scores; however, there was a discernible increase of 0.5 points in the ratings related to perceived expertise and procedural abilities.ConclusionWhile there was a notable increase in the subjective ratings of knowledge and abilities, no statistically significant difference was observed between the two groups. The consensus among dental students is that individually planned and printed implant models serve as a valuable and effective tool in hands-on courses.

Evaluating the Point Cloud of Individual Trees Generated from Images Based on Neural Radiance Fields (NeRF) Method

Three-dimensional (3D) reconstruction of trees has always been a key task in precision forestry management and research. Due to the complex branch morphological structure of trees themselves and the occlusions from tree stems, branches and foliage, it is difficult to recreate a complete three-dimensional tree model from a two-dimensional image by conventional photogrammetric methods. In this study, based on tree images collected by various cameras in different ways, the Neural Radiance Fields (NeRF) method was used for individual tree dense reconstruction and the exported point cloud models are compared with point clouds derived from photogrammetric reconstruction and laser scanning methods. The results show that the NeRF method performs well in individual tree 3D reconstruction, as it has a higher successful reconstruction rate, better reconstruction in the canopy area and requires less images as input. Compared with the photogrammetric dense reconstruction method, NeRF has significant advantages in reconstruction efficiency and is adaptable to complex scenes, but the generated point cloud tend to be noisy and of low resolution. The accuracy of tree structural parameters (tree height and diameter at breast height) extracted from the photogrammetric point cloud is still higher than those derived from the NeRF point cloud. The results of this study illustrate the great potential of the NeRF method for individual tree reconstruction, and it provides new ideas and research directions for 3D reconstruction and visualization of complex forest scenes.

Mobile phone applications for 3-dimensional scanning and digital anthropometry: a precision comparison with traditional scanners.

The precision of digital anthropometry through 3-dimensional (3D) scanning has been established for relatively large, expensive, non-portable systems. The comparative performance of modern mobile applications is unclear. Forty-six adults (age: 23.3 ± 5.3 y; BMI: 24.4 ± 4.1 kg/m2) were assessed in duplicate using: (1) a mobile phone application capturing two individual 2D images, (2) a mobile phone application capturing serial images collected during a subject's complete rotation, (3) a traditional scanner with a time of flight infrared sensor collecting visual data from a subject being rotated on a mechanical turntable, and (4) a commercial measuring booth with structured light technology using 20 infrared depth sensors positioned in the booth. The absolute and relative technical error of measurement (TEM) and intraclass correlation coefficient (ICC) for each method were established. Averaged across circumferences, the absolute TEM, relative TEM, and ICC were (1) 0.9 cm, 1.5%, and 0.975; (2) 0.5 cm, 0.9%, and 0.986; (3) 0.8 cm, 1.5%, and 0.974; and (4) 0.6 cm, 1.1%, and 0.985. For total body volume, these values were (1) 2.2 L, 3.0%, and 0.978; (2) 0.8 L, 1.1%, and 0.997; (3) 0.7 L, 0.9%, and 0.998; and (4) 0.8 L, 1.1%, and 0.996, with segmental volumes demonstrating higher relative errors. A 3D scanning mobile phone application involving full rotation of subjects in front of a smartphone camera exhibited similar reliability to larger, less portable, more expensive 3D scanners. In contrast, larger errors were observed for a mobile scanning application utilizing two 2D images, although the technical errors were acceptable for some applications.

PETS-Nets: Joint Pose Estimation and Tissue Segmentation of Fetal Brains Using Anatomy-Guided Networks.

Fetal Magnetic Resonance Imaging (MRI) is challenged by fetal movements and maternal breathing. Although fast MRI sequences allow artifact free acquisition of individual 2D slices, motion frequently occurs in the acquisition of spatially adjacent slices. Motion correction for each slice is thus critical for the reconstruction of 3D fetal brain MRI. In this paper, we propose a novel multi-task learning framework that adopts a coarse-to-fine strategy to jointly learn the pose estimation parameters for motion correction and tissue segmentation map of each slice in fetal MRI. Particularly, we design a regression-based segmentation loss as a deep supervision to learn anatomically more meaningful features for pose estimation and segmentation. In the coarse stage, a U-Net-like network learns the features shared for both tasks. In the refinement stage, to fully utilize the anatomical information, signed distance maps constructed from the coarse segmentation are introduced to guide the feature learning for both tasks. Finally, iterative incorporation of the signed distance maps further improves the performance of both regression and segmentation progressively. Experimental results of cross-validation across two different fetal datasets acquired with different scanners and imaging protocols demonstrate the effectiveness of the proposed method in reducing the pose estimation error and obtaining superior tissue segmentation results simultaneously, compared with state-of-the-art methods.

An AI-based approach to create spatial inventory of safety-related architectural features for school buildings

An understanding of the spatial information for safety-related architectural features inside a school building is crucial for effective responses to emergency situations such as active shooter or fire incidences. However, efficiently creating high-quality spatial inventory for a wide variety of safety-related assets inside a large school building is a challenge for building owners. This study addresses this challenge by developing an AI-based approach for rapid generation of spatial inventory of safety-related architectural features within school buildings from point cloud data. A method for creating cross-modality data from laser scans is proposed to not only address the limitations in individual 2D and 3D datasets but also enable the transfer of the segmentation and generalization performance from the state-of-the-art Segment Anything model to point cloud segmentation. Additionally, our proposed method also demonstrates great potential to address the challenge of extracting glass façade frames from point cloud datasets.

Does the interface between individual 3D acetabular implants and host bone influence the functional outcomes in patients with severe bone loss after revision surgery?

There is a wide range of commercially produced revision implants for adequate reconstruction of acetabular large bone defects today, however, it is not always possible to achieve long-term survival of these implants. There is an increasing number of scientific publications concerning the use of custom-designed 3D components, which make it possible not only to achieve stable fixation and connect the pelvic bones but also to restore hip joint biomechanics. To evaluate the positioning of 3D acetabular implants after revision hip arthroplasty and its impact on clinical and functional outcomes. we analyzed results in 48 patients with bone defect types IIIA and IIIB Paprosky types, after revision hip arthroplasty. A prospective study was conducted from 2017 to 2023. Revision arthroplasty due to aseptic loosening of the components was performed in 30 cases and as a second stage of periprosthetic infection treatment in 18 cases. We did not achieve a statistically significant difference when using additional flanges and clinical and functional results. In 2 cases we faced aseptic loosening in patients using flanges. In no case were we able to install an implant with 100% adherence to porous structure compared to preoperatively planned adherence. According to the WOMAC and VAS scales, increasing the contact area of the components showed a slight statistical difference in the improvement of clinical and functional results and the reduction of pain. When acetabular 3D components adhered to the bone by more than 68%, we did not register a single complication in the postoperative period, and acetabular 3D components adhered to the bone by less than 68%, a total of 8 (16.6%) complications were registered.

A system for finding identical geometries when building 3-D models

In today's fast-paced world of technology, where innovation plays a crucial role in our daily lives, the importance of search engines for identical models cannot be overstated. Every day, technological progress is becoming an integral part of our existence, penetrating various aspects of our lives. In this context, the search and identification of identical or similar 3D models become particularly relevant and become an integral part of innovative technologies. This article is an in-depth study in the field of creating and describing a mathematical model to search for similar 3D models. Developed using advanced comparison algorithms, this model has enormous potential and can be used in a variety of fields. It is important to emphasize that it is not limited to just one area of application, but finds its application in many areas of human activity. The application of such algorithms is important in various fields, including automated manufacturing, where accuracy and speed of component identification and classification are critical. They also find use in archaeology, where they help in the analysis and study of artifacts and structures. Architecture is also seeing great benefit from these innovations, allowing for more complex and detailed 3D models to be created for design and visualization. The new approach presented in the paper is based on an optimized matching algorithm that enables accurate recognition of geometric structures associated with 3D models without complex data preprocessing or image enhancement. This is a significant step forward in improving the efficiency and accuracy of identifying similar 3D models, even in low-resolution images. However, the application of this model is not limited to these areas. Its integration into real-time systems allows for the identification and classification of both individual 3D objects and complex 3D scenes created from image or video sequences. This is of great importance for the development of virtual and augmented reality technologies, where a high degree of detail and identification is necessary to create captivating and realistic visual effects. Thus, the presented mathematical model and the corresponding software open up enormous prospects for us in various fields. This is an important step forward in the development of technologies related to computer vision and image processing, as well as in the field of virtual and augmented reality, promoting progress and innovation

Development of an individual 3D particle reconstruction method for discrete mechanical modeling: Interpolation by Fourier composition

Reconstruction methods for granular materials are an essential step in achieving an accurate geometrical basis in the use of particle methods such as the well-known discrete element method. In this study, a novel continuous analytical method will be proposed to achieve individual 3D reconstructions of granular materials. For this purpose, the 2D Fourier Descriptor theory is used and a generalization of it to the 3D case is obtained, which is quite different from the spherical harmonics theory. Some limitations existing in the original model will be addressed by developing and calibrating a generalized approach that results in a total of four main models. Particles from a study sample are reconstructed, and the models are evaluated based on four generalized error metrics, as well as the presence of some specific limitations detected in the models. Finally, the models are compared using a decision-making method in different decisional scenarios.

AdS3/RMT2 duality

We introduce a framework for quantifying random matrix behavior of 2d CFTs and AdS3 quantum gravity. We present a 2d CFT trace formula, precisely analogous to the Gutzwiller trace formula for chaotic quantum systems, which originates from the SL(2, ℤ) spectral decomposition of the Virasoro primary density of states. An analogy to Berry’s diagonal approximation allows us to extract spectral statistics of individual 2d CFTs by coarse-graining, and to identify signatures of chaos and random matrix universality. This leads to a necessary and sufficient condition for a 2d CFT to display a linear ramp in its coarse-grained spectral form factor.Turning to gravity, AdS3 torus wormholes are cleanly interpreted as diagonal projections of squared partition functions of microscopic 2d CFTs. The projection makes use of Hecke operators. The Cotler-Jensen wormhole of AdS3 pure gravity is shown to be extremal among wormhole amplitudes: it is the minimal completion of the random matrix theory correlator compatible with Virasoro symmetry and SL(2, ℤ)-invariance. We call this MaxRMT: the maximal realization of random matrix universality consistent with the necessary symmetries. Completeness of the SL(2, ℤ) spectral decomposition as a trace formula allows us to factorize the Cotler-Jensen wormhole, extracting the microscopic object ZRMT(τ) from the coarse-grained product. This captures details of the spectrum of BTZ black hole microstates. ZRMT(τ) may be interpreted as an AdS3 half-wormhole. We discuss its implications for the dual CFT and modular bootstrap at large central charge.

High-Performance Electrochromic Devices Based on Size-Controlled 2D WO3 Nanosheets Prepared Using the Intercalation Method.

It is difficult to obtain ultrathin two-dimensional (2D) tungsten trioxide (WO3) nanosheets through direct exfoliation from bulk WO3 in solution due to the strong bonding between interlayers. Herein, WO3 nanosheets with controllable sizes were synthesized via K+ intercalation and the exfoliation of WO3 powder using sonication and temperature. Because of the intercalation and expansion in the interlayer distance, the intercalated WO3 could be successfully exfoliated to produce a large quantity of individual 2D WO3 nanosheets in N-methyl-2-pyrrolidone under sonication. The exfoliated ultrathin WO3 nanosheets exhibited better electrochromic performance in an electrochromic device than WO3 powder and exfoliated WO3 without intercalation. In particular, the prepared small WO3 nanosheets exhibited excellent electrochromic properties with a large optical modulation of 41.78% at 700 nm and fast switching behavior times of 9.2 s for bleaching and 10.5 s for coloring. Furthermore, after 1000 cycles, the small WO3 nanosheets still maintained 86% of their initial performance.

Wi-Painter

WiFi has gradually developed into one of the main candidate technologies for indoor environment sensing. In this paper, we are interested in using COTS WiFi devices to identify material details, including location, material type, and shape, of stationary objects in the surrounding environment, which may open up new opportunities for many applications. Specifically, we present Wi-Painter, a model-driven system that can accurately detects smooth-surfaced material types and their edges using COTS WiFi devices without modification. Different from previous arts for material identification, Wi-Painter subdivides the target into individual 2D pixels, and simultaneously forms a 2D image based on identifying the material type of each pixel. The key idea of Wi-Painter is to exploit the complex permittivity of the object surface which can be estimated by the different reflectivity of signals with different polarization directions. In particular, we construct the multi-incident angle model to characterize the material, using only the power ratios of the vertically and horizontally polarized signals measured at several different incident angles, which avoids the use of inaccurate WiFi signal phases. We implement and evaluate Wi-Painter in the real world, showing an average classification accuracy of 93.4% for different material types including metal, wood, rubber and plastic of different sizes and thicknesses, and across different environments. In addition, Wi-Painter can accurately detect the material type and edge of the word "LOVE" spliced with different materials, with an average size of 60cm × 80cm, and material edges with different orientations.

Gender differences in cycling motions: On objective functions for urban cycling

AbstractSocietal challenges such as climate change and the energy crisis are putting bicycles at the centre of many people's mobility considerations ‐ but not all. In addition to the need for improved infrastructure, stability and comfort when cycling also play a major role. Simulations offer the possibility to investigate the influence of biometric differences on comfort and stability more cost‐effectively than the measurement methods currently used for bike fitting. Bicycle dynamics and multibody simulation of cycling motions have been the subject of research for a long time. Often data‐driven models are used that follow pre‐established measurement data. Moreover, optimal control simulations for competitive sports are available, where for example, the travel distance during a given time is maximised. However, such models are usually based on the biometric data of an average 18–25 year old male, while the influence of gender differences on cycling motions are rarely explored. Yielding towards closing this gender data gap, we use a discrete mechanics and optimal control framework (DMOCC), which benefits from its structure preserving formulation and has been successfully used for biomechanical applications before. The implemented multibody model of a leg performing a cycling motion can be adapted to individual 3D scans via the geometry parameters and the bounds on the joint angles and torques, providing the possibility to investigate the influence of biometric diversity on the resulting motions. In this first approach, we discuss several possibilities to formulate appropriate objective functions for cycling. The final aim of this study is to supplement a given bike frame by software chosen adaptations so that it optimally fits to individual biometric conditions, thus increasing comfort, the sense of safety and performance, which ultimately enables greater participation in the mobility transition for women, children, and seniors.

Coming in handy: CeTI-Age — A comprehensive database of kinematic hand movements across the lifespan

The Tactile Internet aims to advance human-human and human-machine interactions that also utilize hand movements in real, digitized, and remote environments. Attention to elderly generations is necessary to make the Tactile Internet age inclusive. We present the first age-representative kinematic database consisting of various hand gesturing and grasping movements at individualized paces, thus capturing naturalistic movements. We make this comprehensive database of kinematic hand movements across the adult lifespan (CeTI-Age-Kinematic-Hand) publicly available to facilitate a deeper understanding of intra-individual–focusing especially on age-related differences–and inter-individual variability in hand kinematics. The core of the database contains participants’ hand kinematics recorded with wearable resistive bend sensors, individual static 3D hand models, and all instructional videos used during the data acquisition. Sixty-three participants ranging from age 20 to 80 years performed six repetitions of 40 different naturalistic hand movements at individual paces. This unique database with data recorded from an adult lifespan sample can be used to advance machine-learning approaches in hand kinematic modeling and movement prediction for age-inclusive applications.

Modified Molten Salt Assisted Exfoliation of Large‐Size 2D Materials

Abstract2D materials hold great promise for numerous applications, and the physical properties of individual 2D materials depend strongly on their thickness and lateral size. Currently, there is a lack of a straightforward method to produce 2D materials with both high‐quality and ultra‐high aspect ratios. Herein, a modified molten salt‐assisted exfoliation (MMSAE) strategy is proposed to prepare high‐quality 2D materials with large sizes and controllable thickness. The MMSAE approach involves using high‐frequency disturbance to create a shear force that peels off the layers of the intercalated material (e.g., graphene, MoS2, WS2, and clays), resulting in the successful and efficient exfoliation of 2D material. Taking h‐BN for example, the MMSAE approach achieves the preparation of 2D h‐BN with an average lateral size of 6.30 µm, thickness of 2.34 nm, and a high aspect ratio of 2692.

Chirality Versus Symmetry: Electron's Spin Selectivity in Nonpolar Chiral Lead-Bromide Perovskites.

In the last decade, chirality-induced spin selectivity (CISS), the spin-selective electron transport through chiral molecules, has been described in a large range of materials, from insulators to superconductors. Because more experimental studies are desired for the theoretical understanding of the CISS effect, chiral metal-halide semiconductors may contribute to the field thanks to their chiroptical and spintronic properties. In this regard, this work uses new chiral organic cations S-HP1A and R-HP1A (HP1A = 2-hydroxy-propyl-1-ammonium) to prepare 2D chiral halide perovskites (HPs) which crystallize in the enantiomorphic space groups P43 21 2 and P41 21 2, respectively. The fourfold symmetry induces antiferroelectricity along the stacking axis which, combined to incomplete Rashba-like splitting in each individual 2D polar layer, results in rare spin textures in the band structure. As revealed by magnetic conductive-probe atomic force microscopy (AFM) measurements, these materials show CISS effect with partial spin polarization (SP; ±40-45%). This incomplete effect is efficient enough to drive a chiro-spintronic device as demonstrated by the fabrication of spin valve devices with magnetoresistance (MR) responses up to 250 K. Therefore, these stable lead-bromide HP materials not only represent interesting candidates for spintronic applications but also reveal the importance of polar symmetry-breaking topology for spin selectivity.

Efficient Object Detection Using Semantic Region of Interest Generation with Light-Weighted LiDAR Clustering in Embedded Processors.

Many fields are currently investigating the use of convolutional neural networks to detect specific objects in three-dimensional data. While algorithms based on three-dimensional data are more stable and insensitive to lighting conditions than algorithms based on two-dimensional image data, they require more computation than two-dimensional data, making it difficult to drive CNN algorithms using three-dimensional data in lightweight embedded systems. In this paper, we propose a method to process three-dimensional data through a simple algorithm instead of complex operations such as convolution in CNN, and utilize its physical characteristics to generate ROIs to perform a CNN object detection algorithm based on two-dimensional image data. After preprocessing the LiDAR point cloud data, it is separated into individual objects through clustering, and semantic detection is performed through a classifier trained based on machine learning by extracting physical characteristics that can be utilized for semantic detection. The final object recognition is performed through a 2D-based object detection algorithm that bypasses the process of tracking bounding boxes by generating individual 2D image regions from the location and size of objects initially detected by semantic detection. This allows us to utilize the physical characteristics of 3D data to improve the accuracy of 2D image-based object detection algorithms, even in environments where it is difficult to collect data from camera sensors, resulting in a lighter system than 3D data-based object detection algorithms. The proposed model achieved an accuracy of 81.84% on the YOLO v5 algorithm on an embedded board, which is 1.92% higher than the typical model. The proposed model achieves 47.41% accuracy in an environment with 40% higher brightness and 54.12% accuracy in an environment with 40% lower brightness, which is 8.97% and 13.58% higher than the general model, respectively, and can achieve high accuracy even in non-optimal brightness environments. The proposed technique also has the advantage of reducing the execution time depending on the operating environment of the detection model.

2D Germanane‐MXene Heterostructures for Cations Intercalation in Energy Storage Applications

AbstractHeterostructures offer an exceptional possibility of combining individual 2D materials into a new material having altered properties compared to the parent materials. Germanane (GeH) is a 2D material with many favorable properties for energy storage and catalysis, however, its performance is hindered by its low electrical conductivity. To address the low electrochemical performance of GeH, a heterostructure of GeH and Ti3C2Tx is fabricated. The Ti3C2TX is a layered material belonging to the family of MXenes. The resulting heterostructure (GeMXene) at a defined mass ratio of GeH and Ti3C2Tx shows superior capacitive performance that surpasses that of both pristine materials. The effect of the size of cations and anions for intercalation into GeMXene in different aqueous salt solutions is studied. GeMXene allows only cation intercalation, which is evidenced by the gravimetric electrochemical quartz crystal microbalance (EQCM) technique. The capacitive performance of the GeMXene is compared in neutral, acidic, and alkaline electrolytes to determine the best electrochemical performance. This unleashes the potential use of GeMXene heterostructure in different electrolytes for supercapacitors and batteries. This work will pave the way to explore the heterostructures of other 2D materials such as novel MXenes and functionalized germanane for highly energy‐storage efficient systems, and beyond.

Revealing enhanced X-Ray radiation shielding of 2D layered materials and their laminar heterostructures

This paper demonstrates a new concept of using two-dimensional (2D) layered materials and their heterostructures for enhanced X-ray radiation shielding. This phenomenon is revealed by characterization on the X-ray shielding performances of several 2D materials with high atomic numbers (Z), including MoS2, antimonene (Sb), and MXene prepared as multi-layered and heterostructure films by assembly their few-layer sheets. Results showed considerable X-ray shielding enhancement of (40–50%) at 30 kVp for individual 2D multi-layered films compared with their bulk structures of these materials. Furthermore, when these multi-layered films were combined into laminar heterostructures structures (e.g., MoS2 + MXene), further enhancement of 60% was achieved. The mechanism of the observed X-ray shielding enhancement by these multi-layered 2D structures is not clear at this stage. It is postulated to be the result of an additional multiple scattering and reflections of photons between multiple layers of 2D crystals inside the film, which does not occur in their uniform bulk materials. The presented results suggest that multi-layered 2D materials with high atomic numbers (Z) and their laminar heterostructures could offer a new and promising strategy for designing of a new generation of Pb-free radiation-shielding materials, which is urgently needed across broad sectors.Graphical abstract