Current 3D Research Articles

The evolution of 2D vdW ferroelectric materials: Theoretical prediction, experiment confirmation, applications

Since J. Valasek first discovered ferroelectric materials in 1920, researchers have been exploring continuously in various fields through theory and experiments. With the rapid development of the computing technology, energy efficiency and size requirements of semiconductor devices are becoming increasingly demanding. However, the conventional ferroelectric materials, which have been limited by physical size restrictions, can no longer satisfy the above requirements. Two-dimensional (2D) ferroelectric materials can effectively overcome the size limitation of traditional ferroelectrics due to the weak van der Waals force between layers, which is easy to thin while retaining their own unique properties. Currently, a small number of 2D materials have been proved to be ferroelectric properties by experiments and have shown great application potential in nanoscale electrical and optoelectronic devices, expected to become the leaders of next-generation computing. In this review, the current 2D ferroelectric materials are summarized and discussed in detail from seven aspects: theoretical prediction, fabrication methods, ferroelectric characterization methods, principles of typical 2D ferroelectrics, optimization methods of ferroelectric performance, application, and challenges. Finally, the development of 2D ferroelectric materials looks into the future.

Development of Sustainable 3D Printable Ternary Composite

3D printing technology has the potential to reduce construction waste through its controlled manufacturing process and optimized material consumption, making it a promising sustainable construction approach with reduced environmental impact. However, current 3D printed structures often have a high carbon footprint due to the large amounts of fine-grained primary materials used in mortar composites. Most efforts to develop 3D printed structures are associated with concrete and Portland cement-based materials.This research presents an innovative approach for the reuse of gypsum to develop a material suitable for 3D printing, with the goal to industrialize the usage of waste gypsum in civil engineering. The objective of this study is to develop a sustainable gypsum-cement-pozzolanic (GCP) ternary binder composite, incorporating recycled industrial waste gypsum materials such as construction demolition waste gypsum (CDG), along with Portland cement and secondary pozzolanic materials. The GCP ternary composite combines the desired properties of gypsum, such as fast setting time, with those of Portland cement, which offers high final strength. The addition of a pozzolanic component is essential to ensure the chemical stability of this ternary system.As part of this study, ternary composites were designed and tested for their mechanical properties, durability, and suitability for 3D printing. Development and optimization of the ternary composite was carried out as a loop of procedures (Fig.1). Through these optimization procedures, a range of mixtures with necessary properties for 3D printing was found. To ensure sustainability and to assess the environmental impact of the mixtures, life cycle assessment (LCA) was performed.The LCA of GCP composites showed at least 1.5 times lower embodied energy and carbon dioxide emissions compared to Portland cement-based mortars. This is without considering avoided emissions from the end-of-life stage of gypsum waste, as well as higher technical properties that make GCP composites superior to regular cement composites. Considering these factors, GCP composites exhibit even lower emissions and are considered a viable alternative to Portland cement-based mortars.

#1343 Microfluidic kidney tubuloids-on-a-chip for improved drug translation

Abstract Background and Aims Current 2D in vitro models of renal epithelium lack key features of the in vivo setting, such as tubular structure and perfusion, resulting in low translatability to human situations and failure in clinical translation. Advanced renal in vitro models are of great importance to study kidney function under healthy and diseased conditions. Kidney tubular epithelial organoids, called tubuloids, are three-dimensional multicellular structures that recapitulate tubular function and have been used to model genetic, metabolic, and infectious renal diseases. To increase physiological relevance of the tubuloids, they can be integrated into an organ-on-a-chip system. This microfluidic technique is becoming increasingly recognized as a valuable tool for adding physiologically relevant cues to traditional cell culture including long-term gradient stability, continuous perfusion, and interaction with other cell types such as vasculature. Method Here, we introduce the OrganoPlate microfluidic platform, which can accommodate up to 64 independent microfluidic chips in a microtiter plate format, allowing the growth of 64 independent kidney tubuloid-derived barrier tissues in the form of perfused tubules. To this end, tubuloids derived from adult kidney tissue were plated as single cells in the OrganoPlate against an extracellular matrix (ECM). These renal tubules can be formed in just four days of culture in the device showing rapid and reproducible cell polarization, tight junction formation, proper expression of renal markers and functional transport. Results When integrated into an OrganoPlate system, kidney tubuloids form leak-tight, perfusable tubes with stable Trans Epithelial Electrical Resistance (TEER) and are suitable for high-throughput screening of compound effects through assessment of barrier integrity by use of OrganoTEER and by real-time imaging of transport. OrganoPlate grown kidney tubes treated with Pgp inhibitor Verapamil show significant reduction of Rhodamine123 transport through kidney tubule barrier which confirms show stable activity of Pgp transporter and usability of the model in studying renal drug clearance. Conclusion Our results demonstrate the suitability of our in vitro microfluidic kidney tubuloid-on-a-chip model in mimicking key physiological aspects of the kidney and offer new ways for studying organ physiology and renal disease mechanisms, toxicity screening, and personalized medicine. Additionally, the use of animal models, as well as the costs of drug development, can be reduced.

Contextual embedding learning to enhance 2D networks for volumetric image segmentation

The segmentation of organs in volumetric medical images plays an important role in computer-aided diagnosis and treatment/surgery planning. Conventional 2D convolutional neural networks (CNNs) can hardly exploit the spatial correlation of volumetric data. Current 3D CNNs have the advantage to extract more powerful volumetric representations but they usually suffer from occupying excessive memory and computation nevertheless. In this study we aim to enhance the 2D networks with contextual information for better volumetric image segmentation. Accordingly, we propose a contextual embedding learning approach to facilitate 2D CNNs capturing spatial information properly. Our approach leverages the learned embedding and the slice-wisely neighboring matching as a soft cue to guide the network. In such a way, the contextual information can be transferred slice-by-slice thus boosting the volumetric representation of the network. Experiments on challenging prostate MRI dataset (PROMISE12) and abdominal CT dataset (CHAOS) show that our contextual embedding learning can effectively leverage the inter-slice context and improve segmentation performance. The proposed approach is a plug-and-play, and memory-efficient solution to enhance the 2D networks for volumetric segmentation. Our code is publicly available at https://github.com/JuliusWang-7/CE_Block.

3D ocean assessments reveal that fisheries reach deep but marine protection remains shallow

The wave of new global conservation targets, the conclusion of the High Seas Treaty negotiations, and the expansion of extractive use into the deep sea call for a paradigm shift in ocean conservation. The current reductionist 2D representation of the ocean to set targets and measure impacts will fail at achieving effective biodiversity conservation. Here, we develop a framework that overlays depth realms onto marine ecoregions to conduct the first three-dimensional spatial analysis of global marine conservation achievements and fisheries footprint. Our novel approach reveals conservation gaps of mesophotic, rariphotic, and abyssal depths and an underrepresentation of high protection levels across all depths. In contrast, the 3D footprint of fisheries covers all depths, with benthic fishing occurring down to the lower bathyal and mesopelagic fishing peaking in areas overlying abyssal depths. Additionally, conservation efforts are biased towards areas where the lowest fishing pressures occur, compromising the effectiveness of the marine conservation network. These spatial mismatches emphasize the need to shift towards 3D thinking to achieve ocean sustainability.

Advancement in Cancer Vasculogenesis Modeling through 3D Bioprinting Technology.

Cancer vasculogenesis is a pivotal focus of cancer research and treatment given its critical role in tumor development, metastasis, and the formation of vasculogenic microenvironments. Traditional approaches to investigating cancer vasculogenesis face significant challenges in accurately modeling intricate microenvironments. Recent advancements in three-dimensional (3D) bioprinting technology present promising solutions to these challenges. This review provides an overview of cancer vasculogenesis and underscores the importance of precise modeling. It juxtaposes traditional techniques with 3D bioprinting technologies, elucidating the advantages of the latter in developing cancer vasculogenesis models. Furthermore, it explores applications in pathological investigations, preclinical medication screening for personalized treatment and cancer diagnostics, and envisages future prospects for 3D bioprinted cancer vasculogenesis models. Despite notable advancements, current 3D bioprinting techniques for cancer vasculogenesis modeling have several limitations. Nonetheless, by overcoming these challenges and with technological advances, 3D bioprinting exhibits immense potential for revolutionizing the understanding of cancer vasculogenesis and augmenting treatment modalities.

Ultra‐Fast Shape‐Deformation and Highly‐Sensitive Detection of 4D Printed Electromagnetic Architectures

Abstract4D printing is widely used in soft actuators, flexible electronic devices, and soft robotics. However, existing 4D printed architectures suffer from drawbacks such as long shape‐deformation response time or low sensitivity to external stimuli, which greatly limit their applications. To address these shortcomings, 4D printed electromagnetic architectures (EMAs) with both ultra‐fast shape‐deformation and highly‐sensitive detection capabilities have been proposed in this study. The EMA is composed of a 4D printed polymeric scaffold, along with magnetic and conductive parts. Experiments show that the EMA exhibits not only an ultra‐fast shape‐deformation response time as low as 0.007 s but also a high sensitivity to detect an external pressure down to 0.4 Pa, which is beyond records of all current 4D printing reports. Numerical simulation reveals the rapid shape‐deformation and sensing mechanism of EMAs. The effects of cell structures on the performances of EMAs are investigated by parameter optimization. Finally, a “sensing‐to‐deforming” system has been demonstrated, which can smartly distinguish objects by pressures and then trap or release them by the shape deformation. This work realized the effective coupling of ultra‐rapid shape‐deformation and ultra‐low stress sensing of 4D printed architectures and provided an effective method for integrated development of smart devices in deformation and sensing.

Increased Multiplexity in Optical Tissue Clearing-Based Three-Dimensional Immunofluorescence Microscopy of the Tumor Microenvironment by Light-Emitting Diode Photobleaching

Optical tissue clearing and three-dimensional (3D) immunofluorescence (IF) microscopy is transforming imaging of the complex tumor microenvironment (TME). However, current 3D IF microscopy has restricted multiplexity; only 3 or 4 cellular and noncellular TME components can be localized in cleared tumor tissue. Here we report a light-emitting diode (LED) photobleaching method and its application for 3D multiplexed optical mapping of the TME. We built a high-power LED light irradiation device and temperature-controlled chamber for completely bleaching fluorescent signals throughout optically cleared tumor tissues without compromise of tissue and protein antigen integrity. With newly developed tissue mounting and selected region-tracking methods, we established a cyclic workflow involving IF staining, tissue clearing, 3D confocal microscopy, and LED photobleaching. By registering microscope channel images generated through 3 work cycles, we produced 8-plex image data from individual 400 μm-thick tumor macrosections that visualize various vascular, immune, and cancer cells in the same TME at tissue-wide and cellular levels in 3D. Our method was also validated for quantitative 3D spatial analysis of cellular remodeling in the TME after immunotherapy. These results demonstrate that our LED photobleaching system and its workflow offer a novel approach to increase the multiplexing power of 3D IF microscopy for studying tumor heterogeneity and response to therapy.

R-PointNet: Robust 3D Object Recognition Network for Real-World Point Clouds Corruption

Point clouds obtained with 3D scanners in realistic scenes inevitably contain corruption, including noise and outliers. Traditional algorithms for cleaning point cloud corruption require the selection of appropriate parameters based on the characteristics of the scene, data, and algorithm, which means that their performance is highly dependent on the experience and adaptation of the algorithm itself to the application. Three-dimensional object recognition networks for real-world recognition tasks can take the raw point cloud as input and output the recognition results directly. Current 3D object recognition networks generally acquire uniform sampling points by farthest point sampling (FPS) to extract features. However, sampled defective points from FPS lower the recognition accuracy by affecting the aggregated global feature. To deal with this issue, we design a compensation module, named offset-adjustment (OA). It can adaptively adjust the coordinates of sampled defective points based on neighbors and improve local feature extraction to enhance network robustness. Furthermore, we employ the OA module to build an end-to-end network based on PointNet++ framework for robust point cloud recognition, named R-PointNet. Experiments show that R-PointNet reaches state-of-the-art performance by 92.5% of recognition accuracy on ModelNet40, and significantly outperforms previous networks by 3–7.7% on the corruption dataset ModelNet40-C for robustness benchmark.

Development of 3D printed tissue-mimicking materials: Combining fiber reinforcement and fluid content for improved surgical rehearsal

The prevalence of medical errors during surgical procedures has led to a higher emphasis on improving surgical outcomes, by improving surgical planning and training. Anatomical models have become valuable tools for pre-operative planning and current 3D printed models strive to better match real soft biological tissues. This study aimed to develop novel 3D printed material composites with controllable mechanical properties that mimic soft tissue. Concepts of microstructuring, fiber reinforcement and fluid infill in extrusion-based 3D printing are combined to design tunable materials towards target tissues of porcine muscle and liver. Material characterization was performed in triangular wave cyclic experiments under uniaxial tension with increasing displacements. Hereby, initial EI and final EII elastic moduli were evaluated. Further, the viscous response was characterized by the dissipated energy ratio UD and suture retention strength (SRS) was determined by single tensile pull-out tests Elastic moduli of printed materials were successfully tuned to 510 ± 10 kPa, closely resembling porcine muscle with 580 ± 150 kPa. The dissipated energy ratio UD of the silicone was increased from 0.09 ± 0.01 to 0.46 ± 0.17 by addition of gyroid infill and viscous fluid. Suture retention strength (SRS) for porcine liver tissue was 1.64 ± 0.42 N, while that of 3D printed silicone showed a mean SRS of 5.1 ± 0.6 N. Although the exact properties of porcine muscle and liver tissue require finer tuning, this study established techniques for refinement of 3D printed tissue-mimicking materials, ultimately enabling more accurate models for surgical rehearsal.

Multidimensional photodetection of light fields based on metasurfaces or two-dimensional materials

To completely record a light field, photodetectors should be able to obtain corresponding parameters, including the intensity, position, propagation direction, polarization, wavelength, and time. Recently, metasurface-mediated two-dimensional (2D) material photodetectors have provided solutions for compact and integrated devices to obtain the characteristics of a light field, and most current metasurface-mediated 2D material photodetectors have focused on certain criteria. However, few efforts have been devoted to integrating multidimensional photodetection because of conflicts between the different requirements for distinct parameters and difficulties in fabrication. Problems for multidimensional photodetection are discussed, and the solutions may provide insight into next-generation photodetectors.

Axial Rotation Analysis in Total Ankle Arthroplasty Using Weight-Bearing Computer Tomography and Three-Dimensional Modeling

Introduction/Purpose: Post-operative alignment is the most critical indicator for a successful total ankle arthroplasty (TAA). Total ankle malrotation is associated with an increased risk for polyethylene wear and medial gutter pain. Currently, there is no consensus on the correct way to measure the alignment of the tibial and talar component rotations in the axial plane. In the current study, the post-operative analysis system was assessed using weight-bearing computer tomography and a three- dimensional (3D) model. The purpose of the study was to assess the inter-observer and intra-observer agreement of this system. Methods: Four angles were measured by two raters independently in two separate readings: posterior tibial component rotation angle (PTIRA), posterior talar component rotation angle (PTARA), tibia talar component axial angle (TTAM), and tibial component to the second metatarsal angle (TMRA). Agreement analysis was quantified according to the interclass coefficient. Results: Sixty TAAs across 60 patients were evaluated. A good inter-observer agreement and intra-observer agreement when measuring the PTIRA, PTARA, and TTAM angles was observed along with an excellent inter-observer agreement and intra- observer agreement when measuring the TMRA angle. Conclusion: In conclusion, the current 3D model-based measurement system demonstrates good to excellent inter and intra- agreement. According to these results, 3D modelling can be reliably used to measure and assess the axial rotation of TAA components. Figure 3: Example case of how to measure the angles for assessing axial rotation of TAA and forefoot. 3 A) Posterior TIbial component Rotational Angle (PTIRA). The chosen slice is the one with the straightest posterior wall component of the tibia. The angle is measured on the following steps. First a reference is put on the posterior wall of the tibial component (blue horizontal line). Then the angle is measured starting from the center of the tibial axis (red dot explained on fig. 2), going to the fibular axis (medial part of the red line at the right of the draw), and finally ending 90° to the posterior reference line. 3 B) Posterior TAlar component Rotational Angle (PTARA). The chosen slice is the one with the straightest posterior wall component of the talus. For the angle measurement. The same steps as figure 3 A are followed with the talar component. 3 C) Talar component to 2nd Metatarsal Rotational Angle (TMRA): Defined as an intersection between a line parallel to the lateral border of the tibia component and a line perpendicular to the anatomical axis of the second metatarsal. This was measured using a 3D rendering tool

Semi-supervised 3D Object Detection with PatchTeacher and PillarMix

Semi-supervised learning aims to leverage numerous unlabeled data to improve the model performance. Current semi-supervised 3D object detection methods typically use a teacher to generate pseudo labels for a student, and the quality of the pseudo labels is essential for the final performance. In this paper, we propose PatchTeacher, which focuses on partial scene 3D object detection to provide high-quality pseudo labels for the student. Specifically, we divide a complete scene into a series of patches and feed them to our PatchTeacher sequentially. PatchTeacher leverages the low memory consumption advantage of partial scene detection to process point clouds with a high-resolution voxelization, which can minimize the information loss of quantization and extract more fine-grained features. However, it is non-trivial to train a detector on fractions of the scene. Therefore, we introduce three key techniques, i.e., Patch Normalizer, Quadrant Align, and Fovea Selection, to improve the performance of PatchTeacher. Moreover, we devise PillarMix, a strong data augmentation strategy that mixes truncated pillars from different LiDAR scans to generate diverse training samples and thus help the model learn more general representation. Extensive experiments conducted on Waymo and ONCE datasets verify the effectiveness and superiority of our method and we achieve new state-of-the-art results, surpassing existing methods by a large margin. Codes are available at https://github.com/LittlePey/PTPM.

YOLO2U-Net: Detection-guided 3D instance segmentation for microscopy

Microscopy imaging techniques are instrumental for characterization and analysis of biological structures. As these techniques typically render 3D visualization of cells by stacking 2D projections, issues such as out-of-plane excitation and low resolution in the z-axis may pose challenges (even for human experts) to detect individual cells in 3D volumes as these non-overlapping cells may appear as overlapping. A comprehensive method for accurate 3D instance segmentation of cells in the brain tissue is introduced here. The proposed method combines the 2D YOLO detection method with a multi-view fusion algorithm to construct a 3D localization of the cells. Next, the 3D bounding boxes along with the data volume are input to a 3D U-Net network that is designed to segment the primary cell in each 3D bounding box, and in turn, to carry out instance segmentation of cells in the entire volume. The promising performance of the proposed method is shown in comparison with current deep learning-based 3D instance segmentation methods.

SPGroup3D: Superpoint Grouping Network for Indoor 3D Object Detection

Current 3D object detection methods for indoor scenes mainly follow the voting-and-grouping strategy to generate proposals. However, most methods utilize instance-agnostic groupings, such as ball query, leading to inconsistent semantic information and inaccurate regression of the proposals. To this end, we propose a novel superpoint grouping network for indoor anchor-free one-stage 3D object detection. Specifically, we first adopt an unsupervised manner to partition raw point clouds into superpoints, areas with semantic consistency and spatial similarity. Then, we design a geometry-aware voting module that adapts to the centerness in anchor-free detection by constraining the spatial relationship between superpoints and object centers. Next, we present a superpoint-based grouping module to explore the consistent representation within proposals. This module includes a superpoint attention layer to learn feature interaction between neighboring superpoints, and a superpoint-voxel fusion layer to propagate the superpoint-level information to the voxel level. Finally, we employ effective multiple matching to capitalize on the dynamic receptive fields of proposals based on superpoints during the training. Experimental results demonstrate our method achieves state-of-the-art performance on ScanNet V2, SUN RGB-D, and S3DIS datasets in the indoor one-stage 3D object detection. Source code is available at https://github.com/zyrant/SPGroup3D.

Mechanical, metallurgical, and corrosion investigations on wire arc additively manufactured low carbon steel

Current global 3D metal printing research is focused on powder bed fusion and direct energy deposition. Several materials are presently under research to assess their post-additive manufacturing properties, and to process them in industries using advanced manufacturing techniques. This study investigates the electrochemical, mechanical, and metallurgical properties of wire arc additively manufactured (WAAM) ER70S-6 alloy walls on AISI S235 plates, emphasizing ship component fabrication. The corrosion resistance of both materials is assessed through Electrochemical Impedance Spectroscopy and Potentiodynamic Polarization testing in a 3.5% NaCl solution. Surface and cross-sectional morphologies are analyzed using optical microscopy and scanning electron microscopy, while energy dispersive spectroscopy is employed to study the chemical composition of the corrosion products. Moreover, the hardness of the WAAM-deposited ER70S6 wall is found to be highest in the uppermost layers of deposition due to the presence of acicular ferrite and ferritic-bainite microstructure. Tensile and Charpy impact strength for AISI S235 and WAAM-deposited ER70S-6 exhibit similar performance at room and low temperatures. Interestingly, the WAAM sample demonstrates a 60% higher elongation than the AISI S235 sample. Hence, the findings from this experimental work suggest that WAAM-deposited carbon steel exhibits favourable corrosion resistance and comparable mechanical properties to AISI S235. Consequently, WAAM is emerging as a promising alternative to conventional manufacturing techniques for fabricating ship components, repairing and modifying hull structures, manufacturing nozzles, propeller blades, fin-like structures, structural components, and more.

Development and validation of 3-dimensional simulators for penile prosthesis surgery.

The acquisition of skills in penile prosthesis surgery has many limitations mainly due to the absence of simulators and models for training. Three-dimensional (3D) printed models can be utilized for surgical simulations, as they provide an opportunity to practice before entering the operating room and provide better understanding of the surgical approach. This study aimed to evaluate and validate a 3D model of human male genitalia for penile prosthesis surgery. This study included 3 evaluation and validation stages. The first stage involved verification of the 3D prototype model for anatomic landmarks compared with a cadaveric pelvis. The second stage involved validation of the improved model for anatomic accuracy and teaching purposes with the Rochester evaluation score. The third stage comprised validation of the suitability of the 3D prototype model as a surgical simulator and for skill acquisition. The third stage was performed at 3 centers using a modified version of a pre-existing, validated questionnaire and correlated with the Rochester evaluation score. We sought to determine the suitability of 3D model for training in penile prosthesis surgery in comparison with the available cadaveric model. The evaluation revealed a high Pearson correlation coefficient (0.86) between questions of the Rochester evaluation score and modified validated questionnaire. The 3D model scored 4.33 ± 0.57 (on a Likert scale from 1 to 5) regarding replication of the relevant human anatomy for the penile prosthesis surgery procedure. The 3D model scored 4.33 ± 0.57 (on a Likert scale from 1 to 5) regarding its ability to improve technical skills, teach and practice the procedure, and assess a surgeon's ability. Furthermore, the experts stated that compared with the cadaver, the 3D model presented greater ethical suitability, reduced costs, and easier accessibility. A validated 3D model is a suitable alternative for penile prosthesis surgery training. This is the first validated 3D hydrogel model for penile prosthesis surgery teaching and training that experts consider suitable for skill acquisition. Because specific validated guidelines and questionnaires for the validation and verifications of 3D simulators for penile surgery are not available, a modified questionnaire was used. The current 3D model for penile prosthesis surgery shows promising results regarding anatomic properties and suitability to train surgeons to perform penile implant surgery. The possibility of having an ethical, easy-to-use model with lower costs and limited consequences for the environment is encouraging for further development of the models.

DEVELOPING 4D MALAYSIAN MARINE CADASTRE DATA MODEL BASED ON LADM - PRELIMINARY WORKS

Abstract. The complex interactions between terrestrial and aquatic dimensions make effective land management in coastal and marine settings particularly difficult. For thorough spatial data administration, it has been acknowledged that the traditional Land Administration Domain Model (LADM) must incorporate temporal and three-dimensional (3D) elements. The main goal of this work is to develop a 4D Malaysian marine cadastre data model based on LADM that integrates the temporal dimensions (3D + T= 4D) to the current 3D Malaysian marine cadastre data model and to assess how well the extended model handles issues with the representation of 3D marine environment. The depiction and management of temporal data related to shifting coastlines, fluctuating tides, and seasonal fluctuations in the maritime environment are some of the key questions investigated. The findings of this study are expected to provide valuable insights for augmenting land administration methodologies in dynamic marine settings. The outcomes of this research hold significance for sustainable marine development and effective spatial data management in coastal regions.

Ideal Photodetector Based on WS2/CuInP2S6 Heterostructure by Combining Band Engineering and Ferroelectric Modulation.

Two-dimensional van der Waals (2D vdW) heterostructure photodetectors have garnered significant attention for their potential applications in next-generation optoelectronic systems. However, current 2D vdW photodetectors inevitably encounter compromises between responsivity, detectivity, and response time due to the absence of multilevel regulation for free and photoexcited carriers, thereby restricting their widespread applications. To address this challenge, we propose an efficient 2D WS2/CuInP2S6 vdW heterostructure photodetector by combining band engineering and ferroelectric modulation. In this device, the asymmetric conduction and valence band offsets effectively block the majority carriers (free electrons), while photoexcited holes are efficiently tunneled and rapidly collected by the bottom electrode. Additionally, the ferroelectric CuInP2S6 layer generates polarization states that reconfigure the built-in electric field, reducing dark current and facilitating the separation of photocarriers. Moreover, photoelectrons are trapped during long-distance lateral transport, resulting in a high photoconductivity gain. Consequently, the device achieves an impressive responsivity of 88 A W-1, an outstanding specific detectivity of 3.4 × 1013 Jones, and a fast response time of 37.6/371.3 μs. Moreover, the capability of high-resolution imaging under various wavelengths and fast optical communication has been successfully demonstrated using this device, highlighting its promising application prospects in future optoelectronic systems.

Parallel BFS through pennant data structure with reducer hyper‐object based data hiding for 3D mesh images

AbstractData hiding refers to the technique employed to conceal sensitive data within various forms of multimedia, such as video, audio, text, 2D images, 3D images, and more, while also being able to successfully extract said information from the multimedia file. Although numerous data hiding techniques exist for 2D images, research on data hiding in 3D images is still in its early stages. Current 3D image steganography methods suffer from limitations in terms of embedding capacity and time complexity. To overcome these difficulties, we propose a novel 3D image steganographic technique using Parallel Breadth First Search (PBFS) with hyper‐object. Our approach utilizes the PBFS searching strategy, along with layer synchronization, to embed private information within the vertices of the 3D mesh images. To maximize the effectiveness of the process and minimize time, cost, complexity, and execution, we involved the data structure “bag” to parallelize the BFS. The implementation of bags is based on the pennant data structure. This methodology obtained an impressive Embedding Capacity (EC) of 9.00 bits per vertex (bpv) while maintaining superior visual quality and time complexity. Consequently, the proposed methodology holds great potential for widespread utilization across numerous sectors, including private and government organizations such as intelligence agencies, intellectual property rights management, cloud data security, defense, covert communication, medical imagery, and more.