Simple Structure Research Articles

Efficiency limit for diamond metal/intrinsic/p-type Schottky barrier-based betavoltaic cells

Diamond materials hold great potentials with favorable characteristics for betavoltaic cells, thanks to their simple structure, high conversion efficiency, and radiation robustness. However, to explore its efficiency limit is greatly hindered by the material growth, doping techniques, and device design as well. In this work, a device model based on a diamond metal/intrinsic/p-type (MIP) Schottky barrier architect is analyzed for an accurate prediction of the efficiency limit for the betavoltaic cell based on such a structure. The study takes various factors of significance into account on the betavoltaic cell device characteristics, including the radiation source, thickness and doping concentration of the intrinsic layer, metal work function, as well as the metal/diamond interface traps and traps in the bulk. The current–voltage characteristics and fundamental parameters of the betavoltaic cells are thoroughly analyzed. According to our results, an open-circuit voltage of 2.04 V, a short-circuit current density of 87 nA·cm−2, and a fill factor of 0.9 for the diamond MIP betavoltaic cell can be achieved, which give a maximum energy conversion efficiency of 10.7%, at optimal conditions using 50 nm thick Al metal as the contact layer, 9 μm thick and 1 × 1014 cm−3-doping intrinsic layer, and 10 μm thick and 2 × 1017 cm−3-doping p-layer under a 2 μm 63Ni irradiation. This work also discusses the impact of the interface/bulk traps on the barrier heights of practical Schottky diodes and the device's performance as well.

Numerical investigation of PEMFC performance based on different multistage serpentine flow field designs

The design of excellent flow fields can significantly enhance the output current density and improve the uniformity of reactant distribution in PEMFC (Proton Exchange Membrane Fuel Cell). Serpentine flow field has gained more attention due to its outstanding performance and simple structure, but there is little research on multi-channel serpentine flow field considering the consumption of reactant. In this paper, flow fields named multi-s and multi-pis were designed by considering reactant consumption, and compared with the traditional serpentine flow fields including 3 s, 5 s, 3pis and 5pis. Compared with 3 s and 5 s, the average power density of multi-s increased by 0.687 % and 1.256 %, respectively. The average power density of multi-pis increased by 0.326 % and 1.829 %, as compared to that of 3pis and 5pis. Moreover, the utilization of multiple serpentine and parallel flow fields enhances the uniformity of reactant distribution and current density. The paper further investigates the impact of inlet humidity, operating temperature and supply back pressure on the performance of these six flow fields at a working voltage of 0.45 V. The variations of operating parameters exert varying degrees of influence on the different flow fields, multi-s and multi-pis flow fields consistently demonstrating superior performance to the other four flow fields. The present study offers practical guidance for the implementation of PEMFCs, encompassing optimization strategies for flow field design, operation.

Metabolomics Reveal Key Metabolic Pathway Responses to Anxiety State Regulated by Serotonin in Portunus trituberculatus.

Anxiety refers to the pathological persistence and intensification of emotional responses to danger, affecting health from psychological and physical aspects. Serotonin is an important neurotransmitter involved in the onset of anxiety. To explore the biological changes in the formation of anxiety in crustaceans under the regulation of serotonin, we applied the open field-like test method for assessing anxiety states of larval Portunus trituberculatus, a highly aggressive crustacean species with a more simple neural structure compared with rodents and mammals. Compared with the control group, serotonin treatment resulted in a significant decrease in the time spent by the larvae in the central zone, suggesting anxiety-like behavior. Clonazepam treatment reversed this result and provided further evidence that the behavior of larval P. trituberculatus displayed anxiety. Moreover, a non-targeted metabolomic analysis found a significant alteration in the metabolites involved in tryptophan metabolism pathways associated with anxiety, including L-kynurenine, N-acetyl serotonin, and serotonin. These metabolites are involved in the serotonin pathway, the kynurenine pathway, and other pathways that affect anxiety through tryptophan metabolism. There were no significant differences in tryptophan metabolism levels between the control and clonazepam treatment groups. Our results demonstrate the possible existence of anxiety-like behavior in the larvae of P. trituberculatus from two perspectives. Being a species with a simpler neural structure than that of mammals, the larvae of P. trituberculatus offer a convenient model for studying the mechanisms of anxiety in crustaceans.

High-Performance Supercapacitive Pressure Sensors via Height-Grading Micro-Domes of Ionic Conductive Elastomer.

Soft capacitive sensors present numerous appealing characteristics, including simple structure, low power consumption, and fast response. However, they often suffer from low sensitivity and a limited linear sensing range. Herein, a concept is presented to enhance the sensitivity and linearity of supercapacitive pressure sensors by functionally grading the heights of macrodomes constructed from a highly elastic and ionic conductive elastomer made of poly(vinyl alcohol) and phosphoric acid (PVA/H3PO4). The resultant supercapacitive sensors exhibit a high sensitivity (423.42 kPa-1), wide linear sensing range (0-400 kPa), ultralow limit of detection (0.48 Pa), and high durability (stable signal outputs up to 5000 cycles of loading/unloading). Additionally, the sensors can maintain consistent sensing performance within a temperature range of 25-40 °C. The potential of the sensor in health monitoring is demonstrated through ultrahigh-resolution weight measurement, pulse detection, and respiration monitoring.

Weyl points in a twisted multilayer photonic system

We numerically demonstrate the creation of Weyl points in a twisted multilayer photonic system. In our system, each layer is anisotropic with plasmonic response along a direction perpendicular to the layer and with anisotropic dielectric response within the layer. We show that Weyl points can be created by controlling the twist angles between the layers so that spatial-inversion symmetry is broken. Compared with existing approaches, our findings offer a potentially simpler structure for creating Weyl points and highlight the prospect of using twisted multilayer systems for achieving topological photonic effects.

Flight Control Based on Adaptive Output Feedback for Quadrotors Using Quaternions

Quadrotors are expected to play an active role in out-of-sight areas such as equipment inspection and transportation of supplies to disaster areas. However, ensuring stable and highly accurate automatic flights has become a significant challenge. Trajectory tracking control methods that combine adaptive output feedback control based on almost strictly positive real characteristics and backstepping strategy have been proposed for a quadrotor control system with nonlinearities. These methods have simple structures and are robust. However, existing adaptive control methods have the problem of singularities, owing to the use of Euler angles in the modeling of quadrotors. In this paper, we propose an adaptive control method that enables a wider range of attitude changes using quaternions. The effectiveness of the proposed method is validated through numerical simulations.

Integrating Single-Walled Carbon Nanotubes into Supramolecular Assemblies: From Basic Interactions to Emerging Applications.

Integrating single-walled carbon nanotubes (SWCNTs) into supramolecular self-assemblies harnesses the distinctive mechanical, optical, and electronic properties of the nanoparticles alongside the structural and chemical properties of the assemblies. Organic molecules capable of forming supramolecular assemblies through hydrophobic, van der Waals, and π-π interactions have been demonstrated to be particularly effective in dispersing and functionalizing SWCNTs, as these same interactions facilitate the binding to the hydrophobic graphene-like surface of the SWCNTs. This review discusses a variety of self-assembling structures that were shown to integrate SWCNTs, ranging from simple micelles and ring structures to complex DNA origami and three-dimensional hydrogels formed by low-molecular-weight gelators. We explore the integration of SWCNTs into various supramolecular assemblies and highlight emerging applications of these composite materials, such as the mechanical enforcement of self-assembling hydrogels and leveraging the near-infrared (NIR) fluorescence properties of SWCNTs for monitoring the molecular self-assembly process. Notably, the distinctive NIR fluorescence of SWCNTs, which overlaps with the biological transparency window, offers significant opportunities for noninvasive sensing applications within the supramolecular platforms. Future research into a deeper understanding of the interactions between SWCNTs and different supramolecular frameworks will expand the potential applications of SWCNT-integrated supramolecular assemblies in fields like biomedical engineering, electronic devices, and environmental sensing.

Extraction of Winter Wheat Planting Plots with Complex Structures from Multispectral Remote Sensing Images Based on the Modified Segformer Model

As one of the major global food crops, the monitoring and management of the winter wheat planting area is of great significance for agricultural production and food security worldwide. Today, the development of high-resolution remote sensing imaging technology has provided rich sources of data for extracting the visual planting information of winter wheat. However, the existing research mostly focuses on extracting the planting plots that have a simple terrain structure. In the face of diverse terrain features combining mountainous areas, plains, and saline alkali land, as well as small-scale but complex planting structures, the extraction of planting plots through remote sensing imaging is subjected to great challenges in terms of recognition accuracy and model complexity. In this paper, we propose a modified Segformer model for extracting winter wheat planting plots with complex structures in rural areas based on the 0.8 m high-resolution multispectral data obtained from the Gaofen-2 satellite, which significantly improves the extraction accuracy and efficiency under complex conditions. In the encoder and decoder of this method, new modules were developed for the purpose of optimizing the feature extraction and fusion process. Specifically, the improvement measures of the proposed method include: (1) The MixFFN module in the original Segformer model is replaced with the Multi-Scale Feature Fusion Fully-connected Network (MSF-FFN) module, which enhances the model’s representation ability in handling complex terrain features through multi-scale feature extraction and position embedding convolution; furthermore, the DropPath mechanism is introduced to reduce the possibility of overfitting while improving the model’s generalization ability. (2) In the decoder part, after fusing features at four different scales, a CoordAttention module is added, which can precisely locate important regions with enhanced features in the images by utilizing the coordinate attention mechanism, therefore further improving the model’s extraction accuracy. (3) The model’s input data are strengthened by incorporating multispectral indices, which are also conducive to the improvement of the overall extraction accuracy. The experimental results show that the accuracy rate of the modified Segformer model in extracting winter wheat planting plots is significantly increased compared to traditional segmentation models, with the mean Intersection over Union (mIOU) and mean Pixel Accuracy (mPA) reaching 89.88% and 94.67%, respectively (an increase of 1.93 and 1.23 percentage points, respectively, compared to the baseline model). Meanwhile, the parameter count and computational complexity are significantly reduced compared to other similar models. Furthermore, when multispectral indices are input into the model, the mIOU and mPA reach 90.97% and 95.16%, respectively (an increase of 3.02 and 1.72 percentage points, respectively, compared to the baseline model).

Demagnetization optimization of hybrid excitation eddy current damper under intensive impact load

The hybrid excitation eddy current damper is a novel principle damper characterized by high reliability, simple structure, and controllable magnetic field. Under intensive impact loads, hybrid excitation electromagnetic damping can induce demagnetization effects, resulting in significant fluctuations of the electromagnetic damping force at 6-8 ms. To mitigate this phenomenon, this study established a finite element model of the hybrid excitation damper using COMSOL and developed a control module based on a BP neural network in Simulink. Through co-simulation of COMSOL and Simulink, the difference between the maximum and minimum electromagnetic damping force at 6-8ms is reduced from 45 kN to 5 kN. This research provides valuable technical references for the optimization and practical application of eddy current dampers.

Metasurface Source Antenna Gain Improvement Using Simple Side Metal Structure.

As metasurfaces are in the spotlight, research is being conducted to incorporate them into transmitarray (TA) antennas. Among these, as an attempt to create a low-profile design, a patch antenna classified as low-gain can be utilized as an appropriate source antenna. However, for high efficiency of the TA, the gain of the source antenna must be fundamentally improved. For this, a simple side metal structure was applied to a metallic cross-type slot transmitarray. This acts as a resonant element and reflector by utilizing the electromagnetic wave radiated from the source antenna. The changes in the center frequency and gain due to the application of the side metal structure to the source antenna were analyzed. The gain of the source antenna was improved by a total of 4.63 dB. This is expected to be applied to create various source waves and to conduct future research on improving the gain in transmitarray antennas.

Community structure of tropics emerging from spatio-temporal variations in the Intertropical Convergence Zone dynamics

The Intertropical Convergence Zone (ITCZ) is a narrow tropical belt of deep convective clouds, intense precipitation, and monsoon circulations encircling the Earth. Complex interactions between the ITCZ and local geophysical dynamics result in high climate variability, making weather forecasting and prediction of extreme rainfall or drought events challenging. We unravel the complex spatio-temporal dynamics of the ITCZ and the resulting teleconnection patterns via a novel tropical climate classification achieved using complex network analysis and community detection. We reduce the high-dimensional complex ITCZ dynamics into a simple yet insightful community structure that classifies the tropics into seven regions representing distinct ITCZ dynamics. The two largest communities, encompassing landmasses over the Northern and Southern hemispheres, are associated with coherent seasonal ITCZ dynamics and have significant long-range connections. Temporal analysis of the community structure highlights that the tropical Pacific and Atlantic Oceans communities exhibit substantial variation on multidecadal scales. Further, these communities exhibit incoherent dynamics due to atmosphere-ocean interactions driven by equatorial and coastal oceanic upwelling.

Atomic-level design of biomimetic iron-sulfur clusters for biocatalysis.

Designing biomimetic materials with high activity and customized biological functions by mimicking the central structure of biomolecules has become an important avenue for the development of medical materials. As an essential electron carrier, the iron-sulfur (Fe-S) clusters have the advantages of simple structure and high electron transport capacity. To rationally design and accurately construct functional materials, it is crucial to clarify the electronic structure and conformational relationships of Fe-S clusters. However, due to the complex catalytic mechanism and synthetic process in vitro, it is hard to reveal the structure-activity relationship of Fe-S clusters accurately. This review introduces the main structural types of Fe-S clusters and their catalytic mechanisms first. Then, several typical structural design strategies of biomimetic Fe-S clusters are systematically introduced. Furthermore, the development of Fe-S clusters in the biocatalytic field is enumerated, including tumor treatment, antibacterial, virus inhibition and plant photoprotection. Finally, the problems and development directions of Fe-S clusters are summarized. This review aims to guide people to accurately understand and regulate the electronic structure of Fe-S at the atomic level, which is of great significance for designing biomimetic materials with specific functions and expanding their applications in biocatalysis.

Optimization of Organic Rankine Cycle for Hot Dry Rock Power System: A Stackelberg Game Approach

Due to its simple structure and stable operation, the Organic Rankine Cycle (ORC) has gained significant attention as a primary solution for low-grade thermal power generation. However, the economic challenges associated with development difficulties in hot dry rock (HDR) geothermal power systems have necessitated a better balance between performance and cost effectiveness within ORC systems. This paper establishes a game pattern of the Organic Rankine Cycle with performance as the master layer and economy as the slave layer, based on the Stackelberg game theory. The optimal working fluid for the ORC is identified as R600. At the R600 mass flow rate of 50 kg/s, the net system cycle work is 4186 kW, the generation efficiency is 14.52%, and the levelized cost of energy is 0.0176 USD/kWh. The research establishes an optimization method for the Organic Rankine Cycle based on the Stackelberg game framework, where the network of the system is the primary optimization objective, and the heat transfer areas of the evaporator and condenser serve as the secondary optimization objective. An iterative solving method is utilized to achieve equilibrium between the performance and economy of the ORC system. The proposed method is validated through a case study utilizing hot dry rock data from Qinghai Gonghe, allowing for a thorough analysis of the working fluid and system parameters. The findings indicate that the proposed approach effectively balances ORC performance with economic considerations, thereby enhancing the overall revenue of the HDR power system.

Optical and Structural Properties of Anisotropic ZnS Nanoparticle Suspensions.

We studied the optical and structural properties of highly ordered arrays of surfactant-capped ZnS nanowires (NWs) and nanorods (NRs) in organic suspensions. The photoluminescence (PL) emission measured under different concentrations and postsynthesis washing cycles interestingly showed increasing emission upon decreasing nanoparticle (NP) concentration. Synchrotron small angle X-ray scattering measurements elucidated the liquid-crystal-like structure of the NPs in suspension under different concentrations and temperatures. The NWs are stacked in a simple structure with a hexagonal cross-section, whereas the structure of the NRs is more complex, resembling a smectic-c liquid crystal, and shows unusual thermal expansion versus temperature. The results point out that a certain amount of bound surfactant must be present on the NP surface to maximize the PL intensity.

Photonic reservoir computing for parallel task processing based on a feedback-free spin-polarized VCSEL

Time delayed reservoir computing (RC) is a novel artificial neural network that is easy to implement in hardware due to its extremely simple structure. Because of its time-division multiplexed information processing, laser-based photonic time-delayed RCs usually realize parallel processing with polarization/wavelength multiplexing. However, the performance of two different tasks is difficult to regulate separately and simultaneously in the time delayed RC system, especially for the chip-scale configuration. Here, we propose a feedback-free RC system based on a spin-polarized vertical-cavity surface-emitting semiconductor laser (VCSEL), which simplifies the whole system structure and can process time series prediction and waveform recognition tasks in parallel, with employing the input and output coding to provide the effect from past states. By separately setting the number of past states introduced by the coding for the two tasks, the performance of the two tasks can be adjusted respectively. Furthermore, by appropriately tuning the pump polarization ellipticity which is the unique feature for the spin-polarized VCSEL, the computational ability of the proposed RC can be focused on one of the two parallel tasks. Therefore, the proposed RC system is capable of dealing with different tasks with high performance, and also expected to provide a viable solution for integrated neuromorphic computing systems due to its compact, feedback-free structure.

Truncated Differential-Neural Key Recovery Attacks on Round-Reduced HIGHT

Recently, differential-neural cryptanalysis, which integrates deep learning with differential cryptanalysis, has emerged as a powerful and practical cryptanalysis method. It has been particularly applied to lightweight block ciphers, which are characterized by simple structures and operations, and relatively small block and key sizes. In resource-constrained environments, such as Internet of Things (IoT), it is essential to verify the resistance of existing lightweight block ciphers against differential-neural cryptanalysis to ensure security. In differential-neural cryptanalysis, a deep learning model, known as a neural distinguisher, is trained to differentiate a target cipher from others, facilitating key recovery through statistical analysis. For successful differential-neural cryptanalysis, it is crucial to develop a highly accurate neural distinguisher and to optimize the key recovery attack algorithm. In this paper, we introduce a novel neural distinguisher and key recovery attack against the 15-round reduced HIGHT cipher. Our proposed neural distinguisher is capable of distinguishing HIGHT ciphertext by analyzing only a portion of the ciphertext, which we refer to as a truncated neural distinguisher. Notably, our experiments demonstrate that the truncated neural distinguisher achieves performance comparable to existing distinguishers trained on entire ciphertext blocks, while enabling a more efficient key recovery attack through a divide-and-conquer strategy. Furthermore, we observe a significant improvement in key recovery efficiency compared to traditional cryptanalysis methods.

A TDLAS gas detection method based on digital signal modulation

This research presents a novel TDLAS gas concentration detection method based on digital modulation. The method inherits the advantage of a simple system structure from Direct Absorption Spectroscopy, as well as the excellent sensitivity of Wavelength Modulation Spectroscopy. A low-frequency sawtooth wave is employed to drive the laser, resulting in a digital direct absorption spectrum signal. By constructing a cosine modulation sequence in the time domain and performing linear interpolation on the direct absorption spectrum, it successfully converts to a wavelength-modulated absorption spectrum and simultaneously generates a digital reference signal at double the frequency. Ultimately, phase-locked amplification technology can be used to calculate the amplitude of the second harmonic. Since both the modulation and reference signals are generated digitally, they ensure a strict frequency-doubling relationship and phase correlation, which not only reduces the complexity of the hardware system but also effectively minimizes system errors. Because digital technology generates both the modulation and reference signals, it ensures that they have the same phase and a strict frequency-doubling relationship. Experimental results demonstrate that, when fitted by a cubic polynomial, the second harmonic amplitude and the gas concentration have a correlation coefficient (R2) as high as 0.99999 and a root mean square error as low as 0.0011. This proves the method's great accuracy and reliability in practical applications.

Developing Deformable Facets for Heliostats: An Increased Solar Concentration for Thermochemical Applications with Central Tower Receivers

In the present article a new design of a deformable facet and a heliostat with several deformable facets is presented. Previous work has been made around designing heliostats with concentrating facets in the facilities of the Plataforma Solar de Hermosillo. The evaluation and design process of facets and their evolution is also shown and reviewed. The facet design was initially conceived as a parabolic trough surface, however, through a modification of the facet, the surface was transformed into a concave surface. The resulting spot produced by the facet assumes nearly a spherical surface. A heliostat constructed with these facets shows similar optical performance with a previous one made with spherical facets; this spherical heliostat has a higher peak and mean flux. Nevertheless, the deformable facet heliostat presents a simpler construction and structure. A comparison between both heliostats will be presented.

A magneto-elastica reinforced elastomer makes soft robotic grippers

Soft robotics represents a burgeoning and interdisciplinary domain characterized by diverse research avenues encompassing actuation strategies, materials science, fabrication techniques, morphing mechanisms, and control methods. This work introduces a novel approach to soft actuators and robotic grippers by intricately exploring the magneto-elastica of spherical magnet chains. First, a kind of magneto-elastica reinforced elastomer is achieved by embedding millimetre-sized spherical neodymium-iron-boron (NdFeB) magnets into the polymer elastomer. By modulating the coupling effect of the magnetic and elastic contributions, the bending deformation of the magneto-elastica reinforced elastomer can be initialized. Subsequently, we analytically derive the coupled stiffness formula to integrate the discrete-scale magneto-elastica with the continuum elasticity using the transformed section method. For comparison, we further employ the multi-physics coupling simulation method to reveal the complex magnetics-mechanics interactions within the composite beam. As a proof of concept, we demonstrate two distinct configurations of rope-motor-driven robotic grippers respectively: three-finger soft robotic gripper and five-finger soft anthropomorphic hand. Pick-and-place experiments indicate that they have the good applicability to grasp objects with various sizes, surfaces and forms, including delicate, deformable, and irregularly shaped objects. In conclusion, the proposed method offers compelling advantages, including structural simplicity and modularity, facile manufacturability utilizing commonplace materials and 3D printing, in-situ active magneto-elastica-driven resilience and selectable actuation modalities via either magnetic or mechanical stimuli. We anticipate that the exploratory research can potentially manifest further contributions to soft actuators and soft robots.

Submerged and completely open solid–liquid triboelectric nanogenerator for water wave energy harvesting

AbstractTriboelectric nanogenerator (TENG) is an emerging wave energy harvesting technology with excellent potential. However, due to issues with sealing, anchoring, and difficult deployment over large areas, TENG still cannot achieve large‐scale wave energy capture. Here, a submerged and completely open solid–liquid TENG (SOSL‐TENG) is developed for ocean wave energy harvesting. The SOSL‐TENG is adapted to various water environments. Due to its simple structure, it is easy to deploy into various marine engineering facilities in service. Importantly, this not only solves the problem of difficult construction of TENG networks at present, but also effectively utilizes high‐quality wave energy resources. The working mechanism and output performance of the SOSL‐TENG are systematically investigated. With optimal triggering conditions, the transferred charge (Qtr) and short‐circuit current (Isc) of SOSL‐TENG are 2.58 μC and 85.9 μA, respectively. The wave tank experiment is taken for fully demonstrating the superiority of the SOSL‐TENG network in large‐scale collection and conversion of wave energy. Due to the excellent output performance, TENG can harvest wave energy to provide power for various commercial electronic devices such as LED beads, hygrothermograph, and warning lights. Importantly, the SOSL‐TENG networks realizes self‐powered for electrochemical systems, which provides a direction for energy cleanliness in industrial systems. This work provides a prospective strategy for large‐scale deployment of TENG applications, especially for harvesting wave energy in spray splash zones or at the surface of the water.image