Complex 3D Research Articles

3D architecture and complex behavior along the simple central San Andreas fault

The central San Andreas Fault (CSAF) exhibits a simple linear large-scale fault geometry, yet seismic and aseismic deformation features vary in a complex way along the fault. Here we investigate fault zone behaviors using geodetic observation, seismicity and microearthquake focal mechanisms. We employ an improved focal-mechanism characterization method using relative earthquake radiation patterns on 75,164 Ml ≥ 1 earthquakes along a 2-km-wide, 190-km-long segment of the CSAF, from 1984 to 2015. The data reveal the 3D fine-scale structure and interseismic kinematics of the CSAF. Our findings indicate that the first-order spatial variations in interseismic fault creep rate, creep direction, and the fault zone stress field can be explained by a simple fault coupling model. The inferred 3D mechanical properties of a mechanically weak and poorly coupled fault zone provide a unified understanding of the complex fine-scale kinematics, indicating distributed slip deficits facilitating small-to-moderate earthquakes, localized stress heterogeneities, and complex multi-scale ruptures along the fault. Through this detailed mapping, we aim to relate the fine-scale fault architecture to potential future faulting behavior along the CSAF.

Adaptive Bayesian speckle reduction in the 2D DOST domain using 2D MC-GARCH-M model: Preserving image edges and textural information

Image denoising and image quality enhancement are major issues in image processing. Additive and multiplicative noise removal is one of the main image enhancement approaches. In this paper, we propose a novel 2D Merged Complex Generalized Autoregressive Conditional Heteroscedasticity Mixture (MC-GARCH-M) model for 2D complex stochastic processes. This model effectively captures non-Gaussian statistics and all three types of dependencies within the processes (RRD, IID, and RID) using the proposed linear mapping transformation T. Our statistical analysis shows that the 2D MC-GARCH-M model provides the best fit to the non-Gaussian statistics of the mapped matrix of 2D DOST coefficients compared to Gaussian, Generalized Gaussian, and Stable distributions. We confirm the presence of heteroscedasticity in the mapped matrix using the Engle hypothesis test, verifying ARCH/GARCH effects. The 2D MC-GARCH-M model, with its location-dependent conditional variances, provides a flexible approach for noise removal and modeling complex stochastic processes. Experimental results on artificially speckled aerial and actual SAR images demonstrate that our method outperforms state-of-the-art approaches in terms of speckle removal and preserving the edges and textural information of the image. Key innovations include: a new statistical analysis using linear mapping T showing non-Gaussian heavy-tailed distributions; an adaptive Bayesian estimator, MCMAP, based on the heteroscedastic model for speckle noise removal; and the method's applicability to images with varying mutual information (MI) between real and imaginary parts of 2D DOST coefficients. Our method is adaptive and robust to initial parameter settings, effectively utilizing magnitude and phase information, providing an optimal closed-form solution, reducing memory and computational requirements, and demonstrating robustness to speckle power levels. While requiring more computational resources compared to thresholding methods, our approach is less demanding than other Bayesian algorithms, making it suitable for offline processing.

Documentation and management of complex 3D morphologies through digital technology

Digital technologies offer an effective solution for documenting and managing complex three-dimensional morphologies, particularly in the context of Underground Built Heritage (UBH). Our research explores novel digitization strategies by employing various technological tools for 3D scanning. We focus on a selected case study: the Grotta di San Michele Arcangelo. Range-based techniques using LiDAR-UAV (Light Detection and Ranging set in Unmanned Aerial Vehicles) were used for the cave’s exterior, integrated with SLAM (Simultaneous Localization and Mapping) technology was employed for the cave’s interior and close-range photogrammetry techniques captured intricate wall decorations. The results yielded precise geometric information crucial for subsequent three-dimensional modelling of architectural and natural spaces. Furthermore, these techniques were integrated to enhance information management, facilitate conservation efforts, and update existing documentation. The resulting comprehensive database is an essential tool for understanding, monitoring, and effectively managing these intricate underground environments.

Polyelectrolyte‐Mediated Modulation of Spatial Internal Stresses of Hydrogels for Complex 3D Actuators

Hydrogel actuators with complex 3D initial shapes show numerous important applications, but it remains challenging to fabricate such actuators. This article describes a polyelectrolyte‐based strategy for modulating small‐scale internal stresses within hydrogels to construct complex actuators with tailored 3D initial shapes. Introducing polyelectrolytes into precursor solutions significantly enhances the volume shrinkage of hydrogel networks during polymerization, allowing us to modulate internal stresses. Photopolymerization of these polyelectrolyte‐containing solutions through a mask produces mechanically strong hydrogel sheets with large patterned internal stresses. Consequently, these hydrogel sheets attain complex 3D initial shapes at equilibrium, in contrast to the planar initial configuration of 2D actuators. We demonstrate that these 3D actuators can reversibly transform into other 3D shapes (i.e., 3D‐to‐3D shape transformations) in response to external stimuli. Additionally, we develop a predictive model based on the Flory‐Rehner theory to analyze the polyelectrolyte‐mediated shrinking behaviors of hydrogel networks during polymerization, allowing precise modulation of shrinkage and internal stress. This polyelectrolyte‐boosted shrinking mechanism paves a route to the fabrication of high‐performance 3D hydrogel actuators.

Polyelectrolyte-Mediated Modulation of Spatial Internal Stresses of Hydrogels for Complex 3D Actuators.

Hydrogel actuators with complex 3D initial shapes show numerous important applications, but it remains challenging to fabricate such actuators. This article describes a polyelectrolyte-based strategy for modulating small-scale internal stresses within hydrogels to construct complex actuators with tailored 3D initial shapes. Introducing polyelectrolytes into precursor solutions significantly enhances the volume shrinkage of hydrogel networks during polymerization, allowing us to modulate internal stresses. Photopolymerization of these polyelectrolyte-containing solutions through a mask produces mechanically strong hydrogel sheets with large patterned internal stresses. Consequently, these hydrogel sheets attain complex 3D initial shapes at equilibrium, in contrast to the planar initial configuration of 2D actuators. We demonstrate that these 3D actuators can reversibly transform into other 3D shapes (i.e., 3D-to-3D shape transformations) in response to external stimuli. Additionally, we develop a predictive model based on the Flory-Rehner theory to analyze the polyelectrolyte-mediated shrinking behaviors of hydrogel networks during polymerization, allowing precise modulation of shrinkage and internal stress. This polyelectrolyte-boosted shrinking mechanism paves a route to the fabrication of high-performance 3D hydrogel actuators.

Advances in scalable plasmonic nanostructures: towards phase-engineered interference lithography for complex 2D lattices

Advances in scalable plasmonic nanostructures: towards phase-engineered interference lithography for complex 2D lattices

A robust lattice Boltzmann scheme for high-throughput predicting effective thermal conductivity of reinforced composites

Accurately predicting effective thermal conductivity is of great importance for the design and performance evaluation of emerging composites. In this paper, an efficient and implementation-friendly lattice Boltzmann (LB) scheme for predicting the effective thermal conductivity of 3D complex structures is proposed. The key innovation is that the optimum convergence parameter of the 3D thermal LB method is found, which enables the LB equation to converge to steady heat conduction equation with the fastest speed and without losing any accuracy. To deal with the thermal contact resistance between different components, an interface treatment scheme is derived. In comparison with the existing schemes, the present scheme enjoys several hundred times higher computational efficiency. By virtue of this LB scheme, the effective thermal conductivity of the reinforced composites with different dimensional fillers are systematically calculated, and a comprehensive machine learning model is developed. This work provides a powerful numerical tool for high-throughput simulations of the 3D representative volume elements with high thermal conductivity ratios and large grid numbers. It may facilitate the application of data-driven techniques in study of the thermal transport properties of emerging composite materials and structures.

Unveiling Nanoparticles: Recent Approaches in Studying the Internalization Pattern of Iron Oxide Nanoparticles in Mono- and Multicellular Biological Structures.

Nanoparticle (NP)-based solutions for oncotherapy promise an improved efficiency of the anticancer response, as well as higher comfort for the patient. The current advancements in cancer treatment based on nanotechnology exploit the ability of these systems to pass biological barriers to target the tumor cell, as well as tumor cell organelles. In particular, iron oxide NPs are being clinically employed in oncological management due to this ability. When designing an efficient anti-cancer therapy based on NPs, it is important to know and to modulate the phenomena which take place during the interaction of the NPs with the tumor cells, as well as the normal tissues. In this regard, our review is focused on highlighting different approaches to studying the internalization patterns of iron oxide NPs in simple and complex 2D and 3D in vitro cell models, as well as in living tissues, in order to investigate the functionality of an NP-based treatment.

A Modified Substructure Method for Complex 3D Finite Element Analysis of PVDs with Vacuum Preloading

A Modified Substructure Method for Complex 3D Finite Element Analysis of PVDs with Vacuum Preloading

A hybrid analysis method for calculating the cogging torque of consequent pole hybrid excitation synchronous machine

PurposeWhen solving the cogging torque of complex electromagnetic structures, such as consequent pole hybrid excitation synchronous (CPHES) machine, traditional methods have a huge computational complexity. The notable feature of CPHES machine is the symmetric range of field-strengthening and field-weakening, but this type of machine is destined to be equipped with a complex electromagnetic structure. The purpose of this paper is to propose a hybrid analysis method to quickly and accurately solve the cogging torque of complex 3D electromagnetic structure, which is applicable to CPHES machine with different magnetic pole shapings.Design/methodology/approachIn this paper, a hybrid method for calculating the cogging torque of CPHES machine is proposed, which considers three commonly used pole shapings. Firstly, through magnetic field analysis, the complex 3D finite element analysis (FEA) is simplified to 2D field computing. Secondly, the discretization method is used to obtain the distribution of permeance and permeance differential along the circumference of the air-gap, taking into account the effect of slots. Finally, the cogging torque of the whole motor is obtained by using the idea of modular calculation and the symmetry of the rotor structure.FindingsThis method is applicable to different pole shapings. The experimental results show that the proposed method is consistent with 3D FEA and experimental measured results, and the average calculation time is reduced from 8 h to 4 min.Originality/valueThis paper proposes a new concept for calculating cogging torque, which is a hybrid calculation of dimension reduction and discretization modules. Based on magnetic field analysis, the 3D problem is simplified into a 2D issue, reducing computational complexity. Based on the symmetry of the machine structure, a modeling method for discretized analytical models is proposed to calculate the cogging torque of the machine.

Effective Denoising Algorithms for Converting Indoor Blueprints Using a 3D Laser Scanner

This paper focuses on converting complex 3D maps created by LiDAR and SLAM technology into simple 2D maps to make them easier to understand. While 3D maps provide a lot of useful details for robots and computer programs, they can be difficult to read for humans who are used to flat maps. We developed a new system to clean up these 3D maps and convert them into intuitive and accurate 2D maps. The system uses three steps designed to correct different kinds of errors found in 3D LiDAR scan data: clustering-based denoising, height-based denoising, and Statistical Outlier Removal. In particular, height-based denoising is the method we propose in this paper, an algorithm that leaves only indoor structures such as walls. The paper proposes an algorithm that considers the entire range of the point cloud, rather than just the points near the ceiling, as is the case with existing methods, to make denoising more effective. This makes the final 2D map easy to understand and useful for building planning or emergency preparedness. Our main goal is to map the interior of buildings faster and more effectively, creating 2D drawings that reflect accurate and current information. We want to make it easier to use LiDAR and SLAM data in our daily work and increase productivity.

Residual Stress‐Driven Non‐Euclidean Morphing in Origami Structures

Morphing origami has numerous potential engineering applications owing to its intrinsic morphing features from 2D planes to 3D surfaces. However, the current 1D hinge deformation‐driven transformation of foldable origami with rigid or slightly deformable panels cannot achieve a 3D complex and large curvilinear morphing. Moreover, most active origami structures use thin hinges with soft materials on their creases, thus resulting in a lower load capability. This study proposes a novel origami morphing method demonstrating large free‐form surface morphing, such as Euclidean to non‐Euclidean surface morphing with shape‐locking. Tensorial anisotropic stress in origami panels is embedded during the extrusion‐based 3D printing of shape memory polymers. The extrusion‐based 3D printing of isotropic SMPs can produce tensorial anisotropic stress in origami panels during fabrication, which can realize significant non‐Euclidean surface morphing with multiple deformation modes. The connecting topology of the origami unit cells influences the global morphing behavior owing to the interaction of the deformation of adjacent panels. Non‐Euclidean morphing integrated with 4D printing can provide multimodal shape locking at material and structural levels.

3D Domain Arrangement in van der Waals Ferroelectric α‐In2Se3

AbstractOne of the exceptional features of the van der Waals (vdW) ferroelectrics is the existence of stable polarization at a level of atomically thin monolayers. This ability to withstand a detrimental effect of the depolarization fields gives rise to complex domain configurations characterized, among others, by the presence of layered “antipolar” head‐to‐head (H‐H) or tail‐to‐tail (T‐T) dipole arrangements. In this study, tomographic piezoresponse force microscopy (TPFM) is employed to study the 3D polarization arrangement in vdW ferroelectric α‐In2Se3. Sequential removal of thin layers from the polar surface using the PFM tip reveals a complex 3D profile of the domain walls in the α‐In2Se3 crystals. Antiparallel domain layers stacked along the polar direction are also observed by PFM imaging of the non‐polar surfaces showing that H‐H and T‐T domain boundaries are commonly present in α‐In2Se3. Application of TPFM to the electrically written domains allows evaluation of their geometrical lateral‐to‐vertical size aspect ratio, which shows a strong prevalence for the sidewise expansion in comparison to the forward growth. Local I–V measurements reveal a strong polarization direction dependence of conductivity due to the modulation of the energy barrier height as corroborated by theoretical modeling.

Evaluation of stress distribution of different marginal designs on PEEK and PEKK substructure materials, cortical and cancellous Bone:A finite element analysis

High-performance polymers are used in different fixed prosthesis treatments due to their many advantages such as biocompatibility, shock absorption ability, high fracture resistance. The effects of marginal design on the forces on high-performance polymers are unknown. This study aimed was to investigate the stress distribution of different marginal designs on Polyetheretherketone (PEEK) and Polyetherketoneketone (PEKK) substructure materials, cortical bone and cancellous bone by finite element analysis. A first maxillary molar tooth was modeled in 3D using the "3D Complex Render" method. Considering the ideal preparation conditions (Taper angle was 6°, step depth was 1 mm, occlusal reduction was 2mm), four different configurations were modeled by changing the marginal design (chamfer, deep chamfer, shoulder 90°, shoulder 135°). PEEK, PEKK substructure, and composite superstructure were designed on created models. A total of 150N oblique force from two points and a total of 300N vertical force from three points were applied from occlusall. and the maximum principal stress, minimum principal stress, von Mises stress findings in the cortical bone, spongiose bone, and substructure were examined. The study was carried out by static linear analysis with a three-dimensional finite element stress analysis method. The highest maximum principal stress value in the cortical bone was observed when the PEEK+Shoulder 135° step at vertical force. The highest minimum principal stress value in the cortical bone was observed when the PEEK+Shoulder 90° step, and PEEK+deep chamfer step at oblique force. The highest maximum principal stress value in spongiose bone was observed when the PEEK+Shoulder 90° step. The highest minimum principal stress value in spongiose bone was observed when the PEEK+deep chamfer step at vertical force. The highest von Mises stress value in the substructure was observed when the PEKK+Deep chamfer step at oblique force. The lowest maximum principal stress value in the cortical bone was observed when the PEKK+Shoulder 135° step at oblique force. The lowest minimum principal stress value in the cortical bone was observed when the PEEK+Shoulder 135° step, and PEKK+shoulder 135° step at vertical force. The lowest maximum principal stress value in spongiose bone was observed when the PEEK+Shoulder 90° step. The lowest minimum principal stress value in spongiose bone was observed when the PEEK+Shoulder 135° step and PEKK+Shoulder 135° step at vertical force. The lowest von Mises stress value in the substructure was observed when the PEEK+Deep chamfer step at vertical force. When cortical and spongiose bone stress were evaluated, no destructive stress was observed. Considering the stresses occurring in the substructure the highest stress was observed in the chamfer step.

Spatially Resolved Defect Characterization and Fidelity Assessment for Complex and Arbitrary Irregular 3D Printing Based on 3D P-OCT and GCode.

To address the challenges associated with achieving high-fidelity printing of complex 3D bionic models, this paper proposes a method for spatially resolved defect characterization and fidelity assessment. This approach is based on 3D printer-associated optical coherence tomography (3D P-OCT) and GCode information. This method generates a defect characterization map by comparing and analyzing the target model map from GCode information and the reconstructed model map from 3D P-OCT. The defect characterization map enables the detection of defects such as material accumulation, filament breakage and under-extrusion within the print path, as well as stringing outside the print path. The defect characterization map is also used for defect visualization, fidelity assessment and filament breakage repair during secondary printing. Finally, the proposed method is validated on different bionic models, printing paths and materials. The fidelity of the multilayer HAP scaffold with gradient spacing increased from 0.8398 to 0.9048 after the repair of filament breakage defects. At the same time, the over-extrusion defects on the nostril and along the high-curvature contours of the nose model were effectively detected. In addition, the finite element analysis results verified that the 60-degree filling model is superior to the 90-degree filling model in terms of mechanical strength, which is consistent with the defect detection results. The results confirm that the proposed method based on 3D P-OCT and GCode can achieve spatially resolved defect characterization and fidelity assessment in situ, facilitating defect visualization and filament breakage repair. Ultimately, this enables high-fidelity printing, encompassing both shape and function.

Dearomative Intramolecular meta-Thermocycloadditions of Benzene Rings via Wheland Intermediates.

Dearomative cycloadditions are powerful synthetic transformations utilizing aromatic compounds for cycloaddition reactions. They have been extensively applied to the synthesis of biologically relevant compounds not only because of the complexity generated from simplicity but also the atom- and step-economy. For the most studied yet challenging benzene ring systems, ortho- and para-cycloadditions have been realized both photochemically and thermally, while the meta-cycloadditions are still limited to the photochemical processes tracing back to the 1960s. Herein, we for the first time realized the thermal cycloadditions of benzene rings with alkenes in a meta fashion via Wheland intermediates. A broad spectrum of readily available C(sp2)-rich aniline-tethered enynes were transformed into C(sp3)-rich 3D complex polycyclic architectures simply by stirring in TFA. Moreover, the reaction could be performed in gram-scales and the products could be diversely elaborated.

Dearomative Intramolecular meta‐Thermocycloadditions of Benzene Rings via Wheland Intermediates

Dearomative cycloadditions are powerful synthetic transformations utilizing aromatic compounds for cycloaddition reactions. They have been extensively applied to the synthesis of biologically relevant compounds not only because of the complexity generated from simplicity but also the atom‐ and step‐economy. For the most studied yet challenging benzene ring systems, ortho‐ and para‐cycloadditions have been realized both photochemically and thermally, while the meta‐cycloadditions are still limited to the photochemical processes tracing back to the 1960s. Herein, we for the first time realized the thermal cycloadditions of benzene rings with alkenes in a meta fashion via Wheland intermediates. A broad spectrum of readily available C(sp2)‐rich aniline‐tethered enynes were transformed into C(sp3)‐rich 3D complex polycyclic architectures simply by stirring in TFA. Moreover, the reaction could be performed in gram‐scales and the products could be diversely elaborated.

Borehole‐Based Interval Kriging for 3D Lithofacies Modeling

AbstractDeveloping a three‐dimensional (3D) lithofacies model from boreholes is critical for providing a coherent understanding of complex subsurface geology, which is essential for groundwater studies. This study aims to introduce a new geostatistical method—interval kriging—to efficiently conduct 3D borehole‐based lithological modeling with sand/non‐sand binary indicators. Interval kriging is a best linear unbiased estimator for irregular interval supports. Interval kriging considers 3D anisotropies between two orthogonal components—a horizontal plane and a vertical axis. A new 3D interval semivariogram is developed. To cope with the nonconvexity of estimation variance, the minimization of estimation variance is regulated with an additional regularization term. The minimization problem is solved by a global‐local genetic algorithm embedded with quadratic programming and Brent's method to obtain kriging weights and kriging length. Four numerical and real‐world case studies demonstrate that interval kriging is more computationally efficient than 3D kriging because the covariance matrix is largely reduced without sacrificing borehole data. Moreover, interval kriging produces more realistic geologic characteristics than 2.5D kriging, while conditional to spatial borehole data. Compared to the multiple‐point statistics (MPS) algorithm—SNESIM, interval kriging can reproduce the geological architecture and spatial connectivity of channel‐type features, meanwhile producing tabular‐type features with better connectivity. Because the regularization term constrains kriged value toward 0 or 1, interval kriging produces more certainty in sand/non‐sand classification than 2.5D kriging, 3D kriging, and SNESIM. In conclusion, interval kriging is an effective and efficient 3D geostatistical algorithm that can capture the 3D structural complexity while significantly reducing computational time.

Innovative 3D printing of mechanoluminescent composites: Vat photopolymerization meets machine learning

Mechanoluminescent (ML) materials, which refers to a class of material that emits light upon being subjected to external mechanical stimuli, have been drawing attention due to their unique multifunctionality and potential applications in next-generation structural health monitoring. However, the practical use of ML materials has been limited by the lack of a universally acceptable framework for producing high-intensity ML composites and the challenges in fabricating complex 3D shapes. As a breakthrough, this paper presents a novel approach for producing SrAl2O4:Eu2+, Dy3+ particle-based ML composites using Vat photopolymerization (VP)–based 3D printing, and its process optimization via machine learning-based algorithm. In this study, multi-objective Bayesian optimization (MBO) with Gaussian process regression (GPR) as surrogate model is adopted to fine-tune three critical process parameters in VP process: ML particle content, layer thickness, and cure ratio, aiming for both strong ML properties and reduced printing time. GPR models the complex process input-output relationship, utilizing data collected from experiments. The Pareto-optimal solutions identified by MBO not only enabled the rapid production of high-performance ML specimens but also provided empirical insights into how process parameters affect the end product’s ML property and overall printing time. Furthermore, a micromechanical method for analyzing ML particle volume fraction dependence of the ML intensity is presented to analyze the result. Finally, the practical applicability of the proposed framework was validated by testing ML-based stress sensors and mechanical components using the optimized VP process.

Mapping the transition from quasi-2D to 3D spin textures in NiFe nanomagnets

With increasing interest in understanding and mapping the spin textures within magnetic nanostructures, this work reports a study of the transition from quasi-2D magnetic behavior in thin-film ferromagnetic nanostructures to 3D thick-film nanostructures. A series of arrays of 480 × 250 nm2 elliptical Ni81Fe19 nanomagnets patterned using deep ultraviolet (DUV) lithography with thickness (t) ranging from 20 to 250 nm were studied. It is shown through magnetometry and micromagnetics that as the film thickness increases, the nanomagnets transition from effectively planar 2D magnets, with uniform spin textures extending through the film thickness for t ≤ 50 nm, to 3D nanomagnets with more complex non-uniform 3D spin textures for t ≥ 100 nm. These results demonstrate that the fabrication of thick-film nanomagnets via DUV lithography is a viable route to producing consistent 3D magnetic nanostructures for potential applications, such as magnonics.