Geometric Module Research Articles

Effect of materials and design on PV cracking under mechanical loading

As the PV industry considers new cell and module designs of lower cost, the reliability and durability become a major issue. Hence, it is important to evaluate the influencing factors on the mechanical strength of solar cells. In this work, a 3D FE model is used to investigate the stresses which are generated from mechanical loading and the XFEM to predict the crack initiation and propagation. Several aspects related to geometric configurations and PV module material properties are investigated. A material imperfection in the form of a locally reduced Elastic modulus by 10% resulted in a decrease of failure load by 70%. PV modules with Si thicknesses of 0.1, 0.15 and 0.2 mm are expected to crack under a uniform mechanical loading of 5400 Pa at different loads. In the case of isotropic Silicon of 0.1 mm thickness, the fracture takes place at a load lower than 2400 Pa. PV modules modelled with isotropic silicon were found to fail at a lower load, approximately by 12%, compared to those with anisotropic silicon. Finally, the study showed that half-cell PV modules experience reduced mechanical stresses, cracking initiates in higher load and the crack propagation is arrested at the boundary of the cell.

Modulating mechanical performances of metallic amorphous materials through phase gradient

Increasing strength is usually at the cost of sacrificing ductility in structural materials. The tradeoff becomes even conspicuous in a category of metallic amorphous materials, the so-called metallic glasses (MGs) featured without any atomic-scale translational symmetry. Therefore, there remains little room for simultaneous optimization of strength and ductility in MGs through tailoring the morphology and kinetics of structural imperfections. Here we propose an alternative strategy for modulating the mechanical properties of MGs through introducing proper content of compositional or phase gradient inspired by the mechanistic strain gradient theory. We design two types of CuZr-based phase gradient metallic glasses (PGMGs) with different compositional concentration gradient directions in either continuous or stepped gradient form. Extensive molecular dynamics simulations demonstrate that phase gradient raises the concentration of mechanically stable icosahedral and icosahedron-like Voronoi polyhedra and, thus, increases the strength of MGs. In terms of plastic deformation, free volume mismatch between phases invalidates the autocatalytic activation mechanism of shear transformation zones, resulting in greater resistance to shear band propagation. The phase gradient also encourages branches of shear band and nucleation of multiple shear bands, which mechanism delocalizes deformation and postpones failure. These mechanisms lead to an improvement in the overall ductility of the PGMGs. The present strategy sheds light on evading the long-standing strength-ductility tradeoff in amorphous metals through extrinsic chemical and geometrical modulation that can be handled by appropriate thermal processing and fabrication technique.

Broadband mechanoresponsive liquid metal sensors

Stretchable electronics have the fundamental advantage of matching the complex geometries of the human body, providing opportunities for real-time biomechanical sensing. We report a method for high-frequency AC-enhanced resistive sensing that leverages deformable liquid metals to improve low-power detection of mechanical stimuli in wearable electronics. The fundamental mechanism of this enhancement is geometrical modulation of the skin effect, which induces current crowding at the surface of a liquid metal trace. In combination with DC sensing, this method quantitatively pinpoints mechanical modes of deformation such as stretching in-plane and compression out-of-plane that are traditionally impossible to distinguish. Here we explore this method by finite element simulations then employ it in a glove to detect hand gestures and tactile forces as well as a respiratory sensor to measure breathing. Moreover, this AC sensor uses lower power (100X) than DC sensors, enabling a new generation of energy-efficient wearables for haptics and biomedical sensing.

Heliconical Cholesterics Endows Spatial Phase Modulator with an Electrically Customizable Working Band

AbstractMultidimensional and large‐scale parallel manipulation of light, especially on‐demand tailoring of the working frequency and spatial phase front, is highly pursued in modern optics. Here, broadband tunable planar optics is demonstrated by electrically driving the nanohelix of photopatterned heliconical cholesterics. By preprogramming the initial orientation of the helixes using a dynamic‐mask photoalignment technique, spatial geometric phases can be arbitrarily encoded to the reflected light in a reconfigurable way. Due to the reversible electrically variant pitch of the heliconical superstructures, the reflective Bragg band can be precisely selected in the range from 380 to 1550 nm. In addition to wavelength selection and geometric phase modulation, spatial amplitude modulation and spin reversion can be further expected. This may offer a platform for full‐dimensional manipulation of light, including wavelength/frequency, phase, amplitude, time, and spin, thus upgrading optical information processing techniques.

FUNDAMENTALS OF MODULAR DESIGN OF PROSPECTIVE NEW GENERATION PASSENGER SLEEPING WAGONS

Purpose. The aim of the work is to study the directions for creating promising competitive passenger sleeping wagons based on the application of the principles of their modular design. Methodology. The research is based on the analysis of literature sources, formation of a system of wagon modules and development on their basis of possible variants of modular design of passenger sleeping wagons and assessment of prospects for their application based on mathematical analysis of the degree of unification of car designs. Findings. In the course of research, it was established that the design of promising passenger sleeping wagons of the new generation should be based on the principles of significantly improving the quality and comfort of passenger transport based on the use of new layout schemes and relevant principles of modular design. The basic geometric module of passenger wagons of any functional purpose assumes increase in width of its body of 3,4 m. Systems of modular design of passenger sleeping wagons in several variants developed on the basis of the offered new layout schemes providing application of two lateral or only one vestibule located in one end wagons or in the middle of their bodies. The proposed layout schemes also provide for increasing the width of longitudinal passages in passenger rooms, equipping wagons with different numbers of bathrooms depending on the type of wagons for their intended purpose, equivalent in size parameters berths in reserved or compartment wagons of the appropriate class. Originality. The scientific novelty of the work is that for the first time, based on selected concepts, a system of modules for the design of passenger wagons for various functional purposes, their definitions and evaluation of the prospects of the proposed modular design systems for passenger wagons. Practical value. Developed systems of modular design of promising passenger sleeping wagons and compartment wagons of the new generation provide a significant increase in their comfort levels, improve the quality and safety of passenger transport, as well as significantly reduce the timing and amount of funding for cars of different classes and purposes, maintenance and repairs during operation.

High-efficiency reflective metasurfaces for terahertz vortex wave generation based on completely independent geometric phase modulations at three frequencies

One of the considerable disadvantages of a metasurface (MS) is frequency-dependent behavior, which largely restricts its practical applications. To overcome this challenge, a MS is designed to generate vortex beams at three distinct terahertz (THz) frequencies. Specifically, a unit-cell structure comprising three resonators is proposed to operate at three distinct THz frequencies. The cross-polarized reflection coefficients of the unit-cell structure are greater than 70% under circularly polarized (CP) incidence. Based on the geometric phase principle, the full 2 π phase shift can be obtained at each frequency by rotating the corresponding orientation angles of the three resonators. By carefully arranging the unit-cell structure, orbital angular momenta (OAMs) with topological charges of l = ± 1 , − 2 , and, − 3 at 0.706 THz, 1.143 THz, and 1.82 THz, respectively, can be generated for a normally incident right CP wave, and OAMs with topological charges of l = − 3 , − 2 , and − 1 can be generated for a normally incident left CP wave. The generated reflective vortex beams through the designed MS have good mode purity up to 85% at 0.706 THz, 84% at 1.143 THz, and 74.7% at 1.82 THz, respectively. Moreover, the designed reflective MS reveals a convenient and low-cost way to generate vortex beams with different/same OAM modes at three different resonance frequencies and is beneficial for potential applications in THz communication.

Geometric metasurface for polarization synthesis and multidimensional multiplexing of terahertz converged vortices

The investigation of converged twisted beams with a helical phase structure has a remarkable impact on both fundamental physics and practical applications. Geometric metasurfaces consisting of individually orientated metal/dielectric meta-atoms provide an ultracompact platform for generating converged vortices. However, it is still challenging to simultaneously focus left-handed and right-handed circularly polarized incident beams with pure geometric phase modulation, which hinders the independent operation on topological charges between these two helical components. Here we propose and experimentally demonstrate an approach to design terahertz geometric metasurfaces that can generate helicity-independent converged vortices with homogeneous polarization states by the superposition of two orthogonal helical vortices with identical topological charges. Furthermore, the multiplexing of polarization-rotatable multiple vortices in multiple dimensions, i.e., in both longitudinal and transverse directions, and a vortex with an extended focal depth is confirmed by embedding polarization modulation into the geometric metasurfaces. The demonstrated approach provides a new way to simultaneously manipulate orthogonal helical components and expand the design dimension, enabling new applications of geometric metasurface devices in polarization optics, twisted-beam related image and edge detection, high capacity optical communication, and quantum information processing, to name a few.

Monocular 3D object detection with Pseudo-LiDAR confidence sampling and hierarchical geometric feature extraction in 6G network

The high bandwidth and low latency of 6G network technology enable the successful application of monocular 3D object detection on vehicle platforms. Monocular 3D-object-detection-based Pseudo-LiDAR is a low-cost, low-power solution compared to LiDAR solutions in the field of autonomous driving. However, this technique has some problems, i.e., (1) the poor quality of generated Pseudo-LiDAR point clouds resulting from the nonlinear error distribution of monocular depth estimation and (2) the weak representation capability of point cloud features due to the neglected global geometric structure features of point clouds existing in LiDAR-based 3D detection networks. Therefore, we proposed a Pseudo-LiDAR confidence sampling strategy and a hierarchical geometric feature extraction module for monocular 3D object detection. We first designed a point cloud confidence sampling strategy based on a 3D Gaussian distribution to assign small confidence to the points with great error in depth estimation and filter them out according to the confidence. Then, we present a hierarchical geometric feature extraction module by aggregating the local neighborhood features and a dual transformer to capture the global geometric features in the point cloud. Finally, our detection framework is based on Point-Voxel-RCNN (PV-RCNN) with high-quality Pseudo-LiDAR and enriched geometric features as input. From the experimental results, our method achieves satisfactory results in monocular 3D object detection.

Cascaded geometric feature modulation network for point cloud processing

Point cloud analysis is a critical technology in the field of 3D vision, such as autonomous driving and robot navigation. Utilizing the inherent geometric properties embedded in 3D point cloud data remains a great challenge. In this paper, a cascaded geometric feature modulation network is proposed to explore the shared geometric patterns of 3D point clouds from local to global. The contribution of this paper is threefold. First, we design a local geometric feature modulation (GFM) block that learns a pointwise transformation according to the surrounding semantic context of each point. Second, based on the dense connection of multiple GFM blocks, we design a novel global fusion mechanism to ensure the preservation of valuable structural information. Finally, we propose a novel spatial distribution consistency loss to remedy the situation of irrational sampling. Benefitting from the proposed loss function, our network enjoys better convergence performance at the same time. Extensive experimental results on different tasks of point cloud processing demonstrate the superiority and robustness of our proposed network.

Shape-Tuned Multiphoton-Emitting InP Nanotetrapods.

As the properties of a semiconductor material depend on the fate of the excitons, manipulating exciton behavior is the primary objective of nanomaterials. Although nanocrystals exhibit unusual excitonic characteristics owing to strong spatial confinement, studying the interactions between excitons in a single nanoparticle remains challenging due to the rapidly vanishing multiexciton species. Here, a platform for exciton tailoring using a straightforward strategy of shape-tuning of single-crystalline nanocrystals is presented. Spectroscopic and theoretical studies reveal a systematic transition of exciton confinement orientation from 3D to 2D, which is solely tuned by the geometric shape of material. Such a precise shape-effect triggers a multiphoton emission in single nanotetrapods with arms longer than the exciton Bohr radius of material. In consequence, the unique interplay between the multiple quantum states allows a geometric modulation of the quantum-confined Stark effect and nanocrystal memory effect in single nanotetrapods. These results provide a useful metric in designing nanomaterials for future photonic applications.

Traffic-Data Recovery Using Geometric-Algebra-Based Generative Adversarial Network.

Traffic-data recovery plays an important role in traffic prediction, congestion judgment, road network planning and other fields. Complete and accurate traffic data help to find the laws contained in the data more efficiently and effectively. However, existing methods still have problems to cope with the case when large amounts of traffic data are missed. As a generalization of vector algebra, geometric algebra has more powerful representation and processing capability for high-dimensional data. In this article, we are thus inspired to propose the geometric-algebra-based generative adversarial network to repair the missing traffic data by learning the correlation of multidimensional traffic parameters. The generator of the proposed model consists of a geometric algebra convolution module, an attention module and a deconvolution module. Global and local data mean squared errors are simultaneously applied to form the loss function of the generator. The discriminator is composed of a multichannel convolutional neural network which can continuously optimize the adversarial training process. Real traffic data from two elevated highways are used for experimental verification. Experimental results demonstrate that our method can effectively repair missing traffic data in a robust way and has better performance when compared with the state-of-the-art methods.

Dual-Wavelength Polarization-Dependent Bifocal Metalens for Achromatic Optical Imaging Based on Holographic Principle.

Recently, ultrathin metalenses have attracted dramatically growing interest in optical imaging systems due to the flexible control of light at the nanoscale. In this paper, we propose a dual-wavelength achromatic metalens that will generate one or two foci according to the polarization of the incident. Based on geometric phase modulation, two unit cells are attentively selected for efficient operation at distinct wavelengths. By patterning them to two divided sections of the metalens structure plane, the dual-wavelength achromatic focusing effect with the same focal length is realized. In addition, the holographic concept is adopted for polarization-dependent bifocal generation, in which the objective wave is originated from two foci that are respectively formed by two orthogonal polarization states of circularly polarized light, namely Left-handed circularly polarized (LCP) light and Right-handed circularly polarized (RCP) light. The incident light is considered as the reference light. The achromatic focusing and polarization-dependent bifocusing are numerically verified through simulations. The proposed design opens the path for the combination of multi-wavelength imaging and chiral imaging, which may find potential applications, such as achromatic optical devices and polarization-controlled biomedical molecular imaging systems.

Generating diverse functionalities simultaneously and independently for arbitrary linear polarized illumination enabled by a chiral transmission-reflection-selective bifunctional metasurface.

A multifunctional metasurface is capable of manipulating electromagnetic waves and achieving kaleidoscopic functions flexibly, which significantly improves the integration and utilization of a single metasurface and has become one of the hotspots in electromagnetics. However, the majority of designs to date can only operate for limited polarization states in half-space and are difficult to show diverse functions at the same time, which restrict the widespread applications of multifunctional metadevices. Herein, an inspiring strategy of a chiral transmission-reflection-selective bifunctional metasurface is proposed to generate two independent functions in co-polarized reflection channel for left-handed circular polarized (LCP) incidence utilizing rotation-induced geometric phase modulation and in co-polarized transmission channel for right-handed circular polarized (RCP) incidence utilizing scaling-induced propagation phase modulation, and both functions appear concurrently under arbitrary linear polarized (LP) incident waves. To verify the feasibility of this methodology, three proof-of-concept metadevices composed of a dual-mode orbital angular momentum (OAM) generator, a bifocal metalens and an integrated metadevice of OAM generator and metalens are constructed and their performances in simulations and experiments are in good accordance with the theoretical ones. This exotic design of bifunctional metasurface will open up a promising way for multifunctional metadevices in engineering applications.

Intrinsic topological magnons in arrays of magnetic dipoles

We study a simple magnetic system composed of periodically modulated magnetic dipoles with an easy axis. Upon adjusting the geometric modulation amplitude alone, chains and two-dimensional stacked chains exhibit a rich magnon spectrum where frequency gaps and magnon speeds are easily manipulable. The blend of anisotropy due to dipolar interactions between magnets and geometrical modulation induces a magnetic phase with fractional Zak number in infinite chains and end states in open one-dimensional systems. In two dimensions it gives rise to topological modes at the edges of stripes. Tuning the amplitude in two-dimensional lattices causes a band touching, which triggers the exchange of the Chern numbers of the volume bands and switches the sign of the thermal conductivity.

Synergistically Modulating Geometry and Electronic Structures of a Chalcogenide Photocatalyst via an Ion-Exchange Strategy.

Maneuvering the architecture and composition of semiconductors is essential to optimizing their performance in photocatalytic solar-to-fuel conversion. Here, we show that ion exchange, having a disparate mechanism with direct nucleation and growth of semiconductor crystals, can provide a new platform for rational control over the geometry and electronic structures of chalcogenide semiconductor photocatalysts. As a demonstration, the ZnSe nanocubes possessing a hollowed architecture and doped with a controllable amount of Ag+ ions are accessed via sequential ion exchange. The kinetics of the exchange reaction offers a knob for regulating the electronic structures of the Ag-doped ZnSe hollow cubes and, hence, their functions in light harvesting and photogenerated charge separation. Such synergistically geometric and optoelectronic modulation of ZnSe brings an order of magnitude enhancement in photocatalytic H2 evolution activity relative to commercial ZnSe powders. Our study corroborates that ion exchange may open up new horizons for judicious fabrication and engineering of semiconductor-based photocatalyst materials.

D-Orbital Reconstructions Forced by Double Bow-Shaped Deformations and Second Coordination Sphere Effects of Cu(II) Heme Analogs in HER.

Both geometric architecture and electronic configurations of heme proteins contribute to its activity. In this work we designed and synthesized a series of four copper(II) porphyrin complexes (4-, 3-, 2- and 1-Cu) where the molecular conformations are modulated by a pair of stepwise shortened straps on the same porphyrin side (cis-ortho) to give double bow-shaped skeletons. Single crystal structures demonstrate that the straps gradually increase the saddle deformation and the deviation of the metal centers, which is in accordance with two, unusual d-orbital reconstructions of two different ground states, as revealed by 4 K EPR and DFT calculations. In the study of the electrocatalytic hydrogen evolution reaction (HER), 1-Cu, with the shortest straps, showed the most apparent improvement of activity. Second coordination sphere (SCS) effects created by the double bow-shaped architecture and the strong saddle porphyrin core in 1-Cu are found to play key roles in proton trapping during the catalytic process. The work contributes a novel strategy to improve the catalytic performance of heme analogs through ligand geometric modulation.

Facet and d-band center engineering of CuNi nanocrystals for efficient nitrate electroreduction to ammonia.

Electrocatalytic nitrate reduction offers a sustainable route to ammonia synthesis and wastewater treatment. However, the nitrate-to-ammonia conversion remains inefficient due to the sluggish kinetics and diverse side reactions. Herein, well-faceted CuNi nanocrystals with Ni-rich surfaces and favorable d-band centres were synthesized with the assistance of γ-cyclodextrin via a solvothermal process. When used as catalysts for nitrate electroreduction, they delivered an ammonia yield of 1.374 mmol h-1 mg-1 (0.5496 mmol h-1 cm-2) at -0.3 V with the faradaic efficiency and selectivity reaching 94.5% and 65.0%, respectively, surpassing pure Cu or Ni nanocrystals and most reported catalysts. Such excellent performances originated from the optimal geometric and electronic structures and special element distribution, which optimized the adsorption behaviors and accelerated the reaction kinetics. A NO3--NO2--NH3 pathway was proposed with the chemical process following the initial electron transfer process as the rate-determining step. This work sheds light on the design of efficient catalysts to achieve carbon neutrality through simultaneous geometric and electronic structure modulation.

GMRNet: A Novel Geometric Mean Relation Network for Few-Shot Very Similar Object Classification

With the widespread use of Deep Learning (DL), the use of DL has increased to provide a solution to the problem of object recognition and classification. In addition to classifying many different types of objects, the Deep Metrics Learning(DML) technique is effective in classifying objects that are visually very similar to each other. In this study, a novel Relation Network (RN) based DML has been designed to classify objects in two different datasets we created. We distinguished groups of objects that had a high degree of similarity to each other. These objects have been categorized using few-shot learning(FSL) since they are quite similar to one another. The impact of changing the number of classes and samples in the database on the network’s performance has been studied. It is shown how the network’s accuracy varies depending on the N-way (number of classes) and K-shots (number of samples) combinations used in its design. Additionally, the performance of the network has improved by an average of 15% thanks to the contribution of the recently introduced geometric mean module to the RN in our study. The accuracy rate of our recommended RN in screw and spare parts datasets is 96.1% and 92.3%, respectively. The first dataset consists of 1800 screw images with 18 classes, while the second dataset consists of 4100 spare parts images with 20 classes. The effectiveness of our method is expressed by the two datasets that we have extensively experimentally studied.

Geometrical Configuration Modulation Via Ion Doping and Defect Engineering in Spinel Oxides for Enhanced Oxygen Revolution Activity

Geometrical Configuration Modulation Via Ion Doping and Defect Engineering in Spinel Oxides for Enhanced Oxygen Revolution Activity

CAST: Learning Both Geometric and Texture Style Transfers for Effective Caricature Generation.

Given a photo of a subject, ability to generate a caricature image that captures distinct characteristics of the subject but with certain exaggeration of their prominent features is of fundamental importance to image processing and facial recognition. There are two main challenges in this task: shape exaggeration and style transfer. The former morphs and exaggerates key facial features of the subject, while the latter generates caricature images in a certain artistic style. In this paper, we propose a CAricature Style Transfer (CAST) framework for caricature generation. There are two modules in the proposed framework. The first is a geometric warping module. Different from the existing style transfer methods, we incorporate the Whitening and Coloring Transformation (WCT) in the geometric style transfer. The WCT is learned on photo and caricature landmarks or the caricature landmark space of a specific artist and is capable of transforming input photo landmarks to caricature landmarks. The second module is a texture style rendering module. We propose a new style transfer method by considering a semantic region-aligned style transfer via affinity constraint. Given a reference caricature image as the style reference, this module is capable of transferring styles between the same or similar semantic regions in caricatures and photos. Furthermore, it can transfer visual attributes of the reference caricatures (such as mouth shape and expressions) to the output caricatures. Experiments have shown desirable effects of the proposed method in transferring both the geometric and artistic texture styles of caricatures. Both qualitative and quantitative results show that the CAST framework is more effective compared than the state-of-the-art caricature generation methods.