bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparative analysis and optimization of nonlinear hysteresis models for a magnetorheological fluid dual-clutch of an electric vehicle transmission
Authors: Zhang Huan , Deng Lei , Zhao Jin , Li Weihua , Du Haiping
Published: August 2024

Electric vehicle (EV) drivetrains have witnessed remarkable progress, prompting intensified research into advanced transmission systems. Magnetorheological fluids (MRF) clutches offer precise modulation of input currents, enabling swift and seamless torque delivery for EV transmission systems, owing to their exceptional performance. The transmission of an EV requires MRF-based clutches to deliver a precise and rapid torque transfer during gear shifting. In these scenarios, the inherent current rate-dependent hysteresis of the MRF-based clutches between the output torque and input current poses a significant challenge in accurately regulating output torque. Therefore, an accurate clutch model of the MRF-based clutches that can describe the rate-dependent hysteresis is crucial to achieve precise control of the output torque. This study investigates the nonlinear hysteresis phenomena using a prototyped MRF dual-clutch (MRFDC) for the transmission system of EVs, followed by a comprehensive analysis of three widely used hysteresis models: two parametric models, including the Bouc-Wen (BW) model and algebraic model (AM), and a non-parametric model, the NARX model. Accuracy, fitting time, and stack size are selected as the main indicators to evaluate the three models comprehensively. Results indicate that the NARX model has exceptional accuracy compared to the others, while it has a much higher memory requirement. The algebraic model shows a great advantage in computational efficiency because it has a straightforward expression. The BW model is in the middle position for all three indicators. To optimize the classic BW model (CBW), a fractional-order modified BW model (FOMBW) is proposed based on the polynomial input function and fractional-order derivatives. The proposed FOMBW model demonstrates superior capability in capturing asymmetric and rate-dependent characteristics compared to the CBW model. These findings provide the basis for choosing an appropriate model to effectively capture nonlinear current hysteresis phenomena within MRFDC with the requirement for precise torque control during gear shifting.
GCRL: a graph neural network framework for network connectivity robustness learning
Authors: Zhang Yu , Chen Haowei , Chen Qiyu , Ding Jie , Li Xiang
Published: August 2024

The resilience and adaptability of complex networks is crucial in ensuring their functionality against disruptions. Particularly, maintaining network connectivity under various attack scenarios is a key aspect of such resilience. Network connectivity refers to the degree to which nodes within a network are interconnected and able to exchange information or resources. Its robustness reflects the ability of a network to maintain connectivity under various attacks. Such ability has profound physical significance, ensuring the stability and reliability of real-world systems. Currently, connectivity robustness assessments rely heavily on very time-consuming attack simulations. This paper introduces a graph neural network framework for network connectivity robustness learning (GCRL) to advance the study of network connectivity robustness. GCRL transforms initial degree distributions and network topology into informative embedding vectors, which are then processed by a robustness learning module mainly composed of multi-layer perceptron, achieving both high speed and precision. Our extensive experiments demonstrate the superior performance of GCRL obtained in less time compared to existing methods, especially in tough scenarios where test data distributions significantly differ from the training set. The framework also shows adaptability to networks of different sizes, making it a more generalized solution for complex network robustness learning.
Observation of competing interactions and field-induced ferromagnetism in noncentrosymmetric cubic Nd3Te4 lattice
Authors: Mishra Prabuddha Kant , Singh Haribrahma , Gautam Aarti , Kumar Gaurav , Umetsu Rie , Ganguli Ashok Kumar
Published: August 2024

Unconventional magnetism is key to advancement in a variety of spintronic applications and thus needs to be explored in new materials. Here, we report the in-depth magnetic studies of noncentrosymmetric Nd3Te4 and findings support the existence of Dzyaloshinskii-Moriya interaction in addition to symmetric exchange interaction among the moments. The large magnetic irreversibility with negative magnetization in M(T) data and associated first-order magnetic transition in isothermal magnetization data suggest the competing interactions resulting in multiple magnetic phases. The gradual evolution of magnetic texture has been probed by changes in the magnetization entropy having interesting characteristics like the inverse magnetocaloric effect. Most importantly, we have probed the existing nontrivial magnetic phase under a low applied magnetic field by ac-χ(T, H) and dc magnetization (M − H) data, having the same critical field in both measurements. The existence of multiple magnetic phases which are interconnected through first-order field-induced magnetic phase transitions have been probed at low field. The transition from paramagnetic to field-induced state for larger applied fields follows a second-order.
Analyzing performance of f-deformed displaced Fock state in continuous-variable quantum teleportation
Authors: Deepak  , Chatterjee Arpita
Published: August 2024

Performing non-Gaussian operations, namely photon addition, photon subtraction, photon-addition-then-subtraction, photon-subtraction-then-addition can successfully enhance the fidelity of the continuous-variable quantum teleportation. However, a shortcoming of these non-Gaussian resources is that they are probabilistic in nature. In this article, we investigate the success probability of the non-Gaussian resources for optimal performance of the ideal teleportation protocol. To this end, we first derive the analytical expression for the two-mode entangled channel having f-deformed displaced Fock state or photon-added displaced Fock state or photon-subtracted displaced Fock state at one port and vacuum at another port of a symmetric beam-splitter. The generalized displaced Fock states are obtained by replacing the conventional bosonic functions by the nonlinear f-deformed operators such as A = af(a
†
a) and

B
=
af

(

a

†

a
)

−
1

. The Wigner characteristic functions describing these three aforementioned non-Gaussian states are determined and utilized to attain the teleportation fidelity for input coherent and squeezed vacuum states. It is found that the nonlinear substitution leads to an enhancement in teleportation fidelity beyond the threshold limit. Moreover, the entangled photon-subtracted displaced Fock state demonstrates maximum efficiency as a quantum channel for teleporting single-mode coherent and squeezed states. We provide the squeezing regime values corresponding to the optimal performance of the non-Gaussian states considered, which will be of significant interest to the experimental fraternity. Further, we show that the entangled photon-added displaced Fock states have larger amount of entanglement but the entangled photon-subtracted displaced Fock states have stronger Einstein-Podolsky-Rosen correlation.
Analysis of plasma wakefield generation by a Laguerre-Gaussian laser beam in laser-plasma accelerators with external injection
Authors: Abedi-Varaki Mehdi
Published: August 2024

This study presents a comprehensive modeling of wakefield generation through external injection utilizing a Laguerre–Gaussian (LG) laser beam in a bubble/blowout regime. The wakefield dynamics are simulated in two dimensions using the particle-in-cell (2D-PIC) method via Wake-T tool, aiming to investigate the underlying mechanisms and characteristics of this process. The simulation results provide insights into the behavior of electrons within the wakefield, their acceleration, phase spaces of the electron beam, velocity distribution, and longitudinal and transversal profiles of the laser electric field in the plasma. The presented model serves as a valuable tool for further investigations into wakefield generation with external injection using LG laser beams, facilitating advancements in this field of study.
Simulating thermodynamic properties of dinuclear metal complexes using Variational Quantum Algorithms
Authors: das Neves Silva Ana Clara , Queiroz Galvão Lucas , Cruz Clebson
Published: August 2024

In this paper, we investigate the use of variational quantum algorithms for simulating the thermodynamic properties of dinuclear metal complexes. Our study highlights the potential of quantum computing to transform advanced simulations and provide insights into the physical behavior of quantum systems. The results demonstrate the effectiveness of variational quantum algorithms in simulating thermal states and exploring the thermodynamic properties of low-dimensional molecular magnetic systems. The findings from this research contribute to broadening our understanding of quantum systems and pave the way for future advancements in materials science through quantum computing.
Bistability-assisted mechanical squeezing and entanglement
Authors: Agasti Souvik , Djorwé P
Published: August 2024

Based on a scheme proposed to experience the dynamical Casimir effect in optomechanical systems, we show how to squeeze mechanical motion and entangle the optical field with mechanical motion in an optomechanical system containing a parametric amplification. The scheme is based on optical bistability which emerges in the system for a strong enough driving field. By considering the steady state’s lower branch of the bistability, the system shows weak entanglement and almost no mechanical squeezing. When the steady state is on the upper branch of the bistable shape, both squeezing and entanglement are greatly enhanced. Specifically, the entanglement shows three degrees of magnitude enhancement. However, this giant entanglement is fragile against decoherence and thermal fluctuation. Regarding the mechanical squeezing, it reaches the standard quantum limit (SQL) in the upper branch of the bistability. Our proposal provides a way to improve quantum effects in optomechanical systems by taking advantage of nonlinearities. This scheme can be realized in similar systems such as superconducting microwave, and hybrid optomechanical systems.
An in-depth analysis of the optical and radiation shielding characteristics of PbF2-MoO3-Bi2O3-B2O3 glasses
Authors: Chandra Sekhar K , Saddeek Yasser B , Aly K A , Laxman Naik J , Albedah M A , Hanafy Hassan , Shareefuddin Md
Published: August 2024

This study investigates the optical and radiation shielding characteristics of different glass systems, such as PbF2, MoO3, Bi2O3, and B2O3, with varied concentrations of PbF2. The glass samples were manufactured by a melt quenching technique, with a composition of 20 Bi2O3—10 MoO3 - (70-x) B2O3-x PbF2, where x is a value between 0 and 20 mol%. The optical properties were evaluated using UV–vis spectrophotometry, while the radiation shielding parameters were calculated using Phy-X/PSD software. The mass attenuation coefficient (MAC) at 0.015 MeV exhibited an increase from 34.101 to 54.190 cm2 g−1 as the quantity of PbF2 rose. At an energy of 15.0 keV, the effective atomic number (Zeff) increased from 74.11 to 76.63, while the half-value layer (HVL) decreased from 0.00263 cm to 0.00188 cm. The values of the optical band gap (Eg) ranged from 2.577 to 2.105 eV, showing a decrease as the PbF2 content rose. The study shows that these glass structures can be utilized in advanced technological applications that demand higher optical and radiation shielding properties, thanks to their improved characteristics with increasing PbF2 content.
Evolution of the DM distribution function in the density spikes around PBHs
Authors: Eroshenko Yu.N.
Published: August 2024

At the cosmological stage of radiation dominance, dark matter density spikes should form around primordial black holes. In the case when dark matter particles are able to annihilate, the density in the central regions of the spikes decreases due to the elimination of particles, which gives an upper bound on the central density. In this paper, the modification of the central density profile is investigated, taking into account the distribution of the particle orbits. The orbits in spike around a primordial black hole are very elongated, almost radial, and the angular momentum distribution has an exponential form. For such an initial distribution function, it is obtained that a cusp with the exponent ≈-0.7 is formed in the central region, instead of an annihilation plateau. The presence of the cusp provides some correction to the rate of dark matter annihilation around primordial black holes.
Sliding into DM: determining the local dark matter density and speed distribution using only the local circular speed of the galaxy
Authors: Staudt Patrick G. , Bullock James S. , Boylan-Kolchin Michael , Kirkby David , Wetzel Andrew , Ou Xiaowei
Published: August 2024

We use FIRE-2 zoom simulations of Milky Way size disk galaxies to derive easy-to-use relationships between the observed circular speed of the Galaxy at the Solar location, v
c, and dark matter properties of relevance for direct detection experiments: the dark matter density, the dark matter velocity dispersion, and the speed distribution of dark matter particles near the Solar location. We find that both the local dark matter density and 3D velocity dispersion follow tight power laws with v
c. Using this relation together with the observed circular speed of the Milky Way at the Solar radius, we infer the local dark matter density and velocity dispersion near the Sun to be ρ = 0.42±0.06 GeV cm-3 and σ
3D = 280+19
-18 km s-1. We also find that the distribution of dark matter particle speeds is well-described by a modified Maxwellian with two shape parameters, both of which correlate with the observed v
c. We use that modified Maxwellian to predict the speed distribution of dark matter near the Sun and find that it peaks at a most probable speed of 257 km s-1 and begins to truncate sharply above 470 km s-1. This peak speed is somewhat higher than expected from the standard halo model, and the truncation occurs well below the formal escape speed to infinity, with fewer very-high-speed particles than assumed in the standard halo model.
Axion stars: mass functions and constraints
Authors: Chang Jae Hyeok , Fox Patrick J. , Xiao Huangyu
Published: August 2024

The QCD axion and axion-like particles, as leading dark matter candidates, can also have interesting implications for dark matter substructures if the Peccei-Quinn symmetry is broken after inflation. In such a scenario, axion perturbations on small scales will lead to the formation of axion miniclusters at matter-radiation equality, and subsequently the formation of axion stars. Such compact objects open new windows for indirect searches for axions. We compute the axion star mass function based on recent axion minicluster studies and Bose star simulations. Applying this mass function, we find post-inflation axion-like particles with masses 1.8 × 10-21eV <ma< 3.3 × 10-17eV are constrained by the lack of dynamical heating of stars in ultrafaint dwarfs. We also find that current microlensing surveys are insensitive to QCD axion stars. While we focus on the gravitational detectability of axion stars, our result can be directly applied to other interesting signatures of axion stars, e.g. their decay to photons, that require as input the abundance, mass, and density distribution of axion stars.
Co-variability Between the Broad Absorption Lines and Narrow Absorption Lines
Authors: Qin 覃 Bo-Lin 柏霖 , Li 黎 Jing 静 , Lu 陆 Wei-Jian 伟坚
Published: August 2024

We investigate the relationship between the variability of broad absorption lines (BALs) or narrow absorption lines (NALs) and that of continuum using a data set of two-epoch SDSS spectra containing 134 C iv NAL-BAL pairs. Our analysis reveals an anti-correlation between the fractional equivalent width (EW) variations in NALs (or BALs) and the fractional flux variations of the continuum, with Spearman rank correlation coefficients of r = −0.47 (p = 1E-08) and r = −0.58 (p = 1E-13), respectively. In addition, we find a positive correlation between the fractional EW variations in NALs and BALs (r = 0.72, p = 1E-22), and derive a regression equation ΔEWNAL/〈EWNAL〉 = 0.803ΔEWBAL/〈EWBAL〉 + 0.008, with an intrinsic scatter of 0.14. These results suggest that the variability in the ionizing continuum may play a significant role in the observed changes in C iv NALs and BALs, supporting the idea of photoionization-driven variability. The co-variability between C iv NALs and BALs may imply that they originate from outflows with similar physical conditions.
The effect of shaping on trapped electron mode stability: an analytical model
Authors: Garbet X. , Donnel P. , De Gianni L. , Qu Z. , Melka Y. , Sarazin Y. , Grandgirard V. , Obrejan K. , Bourne E. , Dif-Pradalier G.
Published: August 2024

A reduced model for trapped electron mode stability has been developed, which incorporates the basic effects of magnetic surface shaping, in particular, elongation and triangularity. This model shows that while elongation is stabilising, though weakly, negative triangularity usually leads to a more unstable plasma. This is in marked contrast with the experimental evidence of a better confinement at negative triangularity, and with recent gyrokinetic linear simulations. This paradox is solved when finite orbit and/or finite mode extent along field lines (mode ballooning) effects are included. These effects give more weight to particles trapped at low bounce angles, which are those that exhibit lower precession frequencies at negative—compared to positive—triangularity. As a result, the interchange growth rate becomes lower at negative triangularity and large temperature gradients, so that negative triangularity appears to have an overall stabilising effect. Mode ballooning appears to play the most important role in this reversal of stability.
Nonlinear MHD modeling of neon doped shattered pellet injection with JOREK and its comparison to experiments in KSTAR
Authors: Lee S.-J. , Hu D. , Lehnen M. , Nardon E. , Kim Jayhyun , Bonfiglio D. , Artola F.J. , Hoelzl M. , Na Yong-Su ,   JOREK team
Published: August 2024

3D nonlinear MHD simulations of neon-doped single shattered pellet injection (SPI) conducted with the JOREK code reveal rich physics during SPI-induced disruptions in KSTAR. In the early phase, pressure-driven modes dominate, and the perturbation of the plasma current is largely consistent with the perturbation of the Pfirsch–Schlüter current. As shards reach the q = 1 surface, resistive current perturbations by helical electron cooling start to dominate, and the electron temperature in the core begins to collapse with convective mixing of the density driven by the internal

1

/

1

kink mode. The confinement of the plasma is later completely destroyed as a cold bubble convects into the core via quasi-interchange mode. Comparisons with available experimental data demonstrate qualitative agreements between JOREK results and experiments, and possible reasons for deviations are discussed.
The application value of H-reflex induced by electrical stimulation of the sacral nerve at the S3 foramen in stimulus parameter programming
Authors: Min Yu , Jiang Haofei , Jin Hao , Yu Yanlan , Ding Guoqing , Jiang Haihong
Published: August 2024

Although manufacturers of sacral nerve stimulators and related research have provided many guiding opinions on the programming values of stimulation parameters, the stimulation parameters used for long-term treatment of patients are still largely influenced by the experience of doctors and the subjective feelings of patients. We found a high positive correlation between the appearance and amplitude growth trend of the H-reflex induced by S3 nerve root stimulation, the stimulation amplitude corresponding to the inflection point, and the subjective sensation threshold and pain threshold of patients who underwent the first phase of implantable stimulatory surgery, through electromyography collection. Based on this, we believe that the information on the H-reflex triggered by S3 nerve root stimulation will provide a more intuitive and quantifiable non-invasive way for doctors to assist in determining the appropriate stimulator amplitude for long-term treatment of patients. This will help reduce unnecessary pain and potential permanent nerve damage risks that may occur to patients during parameter programming and debugging processes. Especially when facing patients with communication barriers or lack of cooperation, it is more meaningful.
DopplerPTNet: Object Detection Network with Doppler Velocity Information for FMCW LiDAR Point Cloud
Authors: Gao Jiaqi , Zhou Wuping , Wang Xiaozhi
Published: August 2024

In the field of autonomous driving, LiDAR plays a crucial role in perception and detection. LiDAR based on Time-of-Flight (ToF) mode can only provide three-dimensional spatial coordinate information of point clouds. In point cloud object detection, the limited feature information of spatial coordinates to some extent restricts the further optimization and improvement of algorithm detection performance. However, LiDAR based on Frequency-Modulated Continuous-Wave (FMCW) mode can not only obtain the three-dimensional spatial coordinates of point clouds, but also directly measure the Doppler velocity information of points, effectively compensating for the limitation of relying solely on spatial coordinate information for object recognition. Therefore, based on the CARLA simulator, we construct the first FMCW LiDAR point cloud object detection simulation dataset, FMCWLidDet. What’s more, a novel 4D object detection algorithm, DopplerPTNet, is proposed based on the direct acquisition of point Doppler velocity information by FMCW LiDAR. The algorithm solves the problem of single spatial coordinate information feature in existing 3D object detection algorithms, which makes it difficult to further improve detection accuracy. The dataset is available at https://github.com/xilight123/FMCW-LiDAR-object-detection-dataset.
Advancements in Dual-Function UV-Visible Light Detection
Authors: Yuan Ze , Wang Liangpei , Wang Chenyu , Luo Shangcong , Huang Tianfeng , Zhang Junfeng , Dong Shurong , Luo Jikui , Ye Zhi
Published: August 2024

This research introduces a photodetector capable of differentiating between ultraviolet (UV) and visible light, employing the unique properties of zinc oxide (ZnO) and α-silicon (α-Si). The core of this innovative device is a pixel unit circuit, designed with three integral components: a delay circuit for precise timing, a photosensitive element composed of an α-Si thin film resistor and a ZnO thin film transistor (ZnO TFT) for respective light spectrum detection, and a source follower for effective signal transmission. Through simulations conducted in Cadence Virtuoso, the device’s responsiveness to varying intensities of UV and visible light was assessed. Adjustments to the α-Si resistance and ZnO TFT threshold voltage simulated realistic light conditions, demonstrating the device’s capability to generate distinct voltage changes in response to different light spectrums. These results underline the potential of the design for larger-scale implementations and highlight its adaptability and precision in detecting diverse light intensities. This research provides the basis for the development of simultaneous UV-visible detection and scalable light detection systems. In addition, taking advantage of the properties of ZnO TFT, which can also be used in the future in curved transparent environments.
Enhancing photoluminescence of monolayer MoS2 on h-BN substrate through plasmonic resonance for high-power optoelectronic devices
Authors: Dong Liang , Yang Hangsheng , Li Yubo
Published: August 2024

Hexagonal boron nitride (h-BN) is an excellent substrate material due to its superb thermal conductivity, excellent thermal stability and smooth surface qualities. In this study, a silicon-based h-BN substrate was prepared using the magnetron sputtering technique. Subsequently, monolayer MoS2 obtained by the CVD method was transferred onto the h-BN substrate. Then, Au-core-Ag-shell nanoparticles with a diameter of 80 nm were prepared by a self-assembly method and uniformly dispersed and spin-coated on the MoS2 surface. Applying a laser with a wavelength of 532 nm can effectively excite the plasma resonance effect of the system. Under the intense thermal effect induced by the high laser power and plasma effect, the h-BN substrate ensures that MoS2 exhibits less red-shift and higher photoluminescence intensity compared with SiO2 substrate due to its excellent thermal conductivity. This means that h-BN substrates are particularly suitable for optoelectronic applications under high laser power environments, and can maintain excellent optoelectronic device performance while effectively curbing overheating to ensure long-term stable device operation.
Machine Learning for Identification of Cancer Cells Based on Electrical Property Using Microfluidic Impedance Flow Cytometry
Authors: Hu Sixing , Li Guangyang , Xue Lingyue , Xu Mingzhu , Xiang Anli , Cao Zhen
Published: August 2024

Microfluidic impedance flow cytometry (IFC) has become an essential tool for cell analysis, providing a rapid and non-invasive approach to the early diagnosis of cancer. However, a large amount of data was generated during microfluidic IFC, which requires highly efficient data processing tools. In recent years, machine learning has emerged as an efficient tool for data analysis. Here we present a microfluidic IFC chip combined with an enhanced deep neural network for the detection of cancer cells based on electrical properties. The effectiveness of the enhanced deep neural network is illustrated by achieving a high identification accuracy of 93%, surpassing the 86% accuracy of a conventional fully connected neural network.
Optical micro/nano fiber enabled accelerometer
Authors: Yu Wen , Zhang Lei
Published: August 2024

Highly sensitive and miniaturized accelerometers play an important role in structural health, earthquakes, and body motion monitoring. Herein, an optical micro/nanofiber (MNF) enabled accelerometer is proposed. The structural parameters of the MNF accelerometer are optimized based on theoretical simulation. The accelerometer shows excellent linear relationship between displacement and force, and achieves a minimum detectable acceleration of 0.15 m/s2. For vibration sensing, the natural frequency of the sensor can be as high as 753 Hz.
High-Performance 1D CdS/2D Te Heterojunction Photodetector
Authors: Liu Shuo , Zhang Liang , Wang Xiaozhi
Published: August 2024

Van der Waals heterostructures facilitate the versatile combination of two-dimensional (2D) materials with semiconductors that possess distinctive band structures, offering a promising platform for developing photodetectors with extensive spectral range and high sensitivity. This study showcases the flawless integration of one-dimensional (1D) CdS nanowires with 2D Te nanosheets, resulting in the creation of a high-performance, mixed-dimensional heterojunction photodetector. Bandgap engineering at the CdS/Te interface promotes Type II band alignment, markedly improving the separation and transport of photo-generated charge carriers. Additionally, the interaction between materials of different dimensions significantly enhances the device’s photoresponsivity and detectivity. When operated at low bias, the photodetector displays an impressive rectification ratio of up to 8.57×103, along with superior photoelectric performance spanning the ultraviolet to visible light spectrum. Particularly under 450 nm wavelength laser illumination, the device displayed a high photoresponsivity of up to 126 A W−1, a high detectivity of 1.03×1010 Jones, an external quantum efficiency of up to 3.49×102%, and a rapid response time of 35 ms. The photoresponsivity of this device was at least an order of magnitude higher than other reported mixed-dimensional heterojunction photodetectors. The development of the 1D CdS/2D Te heterostructure opens new pathways for high-sensitivity, broad-spectrum photodetection technologies.
Integrating zinc iodine battery with zinc hybrid capacitor in one cell
Authors: Liu Shiwei , Luo Shiqiang , Yang Guoshen , Nie Zanxiang
Published: August 2024

The rapid development of flexible wearable electronics and new energy harvesting technologies desire efficient energy storage devices with high safety. Here, we investigate a flexible thin-film device integrating two electrochemical systems: zinc iodine battery and zinc hybrid capacitor. Compared with symmetric supercapacitors devices, both capacity and shelf-life are improved. The integrated device demonstrated an optimized capacity of 0.26 mAh/cm2.
Self-catalyzed Growth and Optoelectronic Properties of High-Quality CsPbBr3 and CsPbI3 Nanowires Based on Chemical Vapor Deposition
Authors: Huang Lanqing , Lu Hanglin , Liu Xingpeng , Song Zhuo , Su Jiande , Li Zhuofeng , Song Shuxiang , Liu Linsheng
Published: August 2024

In this work, high-quality CsPbBr3 and CsPbI3 nanowires were successfully grown on Si substrates via a vapor-liquid-solid (VLS) self-catalyzed growth mechanism using chemical vapor deposition (CVD). The nanowires were characterized by various techniques, including Scanning Electron Microscopy (SEM), Photoluminescence (PL), and Raman spectroscopy. The results demonstrate that the perovskite nanowires grown under the VLS mechanism exhibit strong photoluminescence intensity and excellent crystalline quality. Based on these high-quality nanowires, we further fabricated single-nanowire photodetectors. The CsPbBr3 nanowire photodetector exhibited a high photocurrent to dark current ratio of 4.2×103 and an outstanding responsivity of 1338 A/W. Meanwhile, the CsPbI3 nanowire photodetector also showed a photocurrent to dark current ratio of 10.6 and a responsivity of 991 A/W. These results indicate that perovskite nanowires grown by the VLS growth mechanism hold great promise for applications in photodetection.
A water-sulfolane hybrid electrolyte enabling ultra-low-temperature flexible magnesium-ion supercapacitors
Authors: Tan Xiaohong , Yang Guoshen , Xie Yinghao , Zhou Hang
Published: August 2024

With the continuous development of wearable electronics, flexible energy storage devices have received widespread attention. Aqueous magnesium-ion supercapacitors (MSCs) have attracted great attention due to their high safety and low cost. However, the freeze of aqueous electrolytes will lead to rapid deterioration of aqueous MSCs performances, which hinders their application in wearable electronics. Herein, we propose a water-sulfolane hybrid electrolyte based on low-concentration Mg(ClO4)2 and sulfolane exhibiting excellent low-temperature properties. By investigating the physical characterization and electrochemical performance testing of the hybrid electrolyte revealed that sulfolane contains functional groups disrupting the hydrogen-bond network between water molecules, resulting in superior anti-freezing properties. The hybrid electrolytes endow flexible aqueous MSCs with a wider temperature operating range (-50 °C~25 °C), high specific capacitance of 22 F/g at -30 °C, and superior flexibility. This work provides an effective and feasible strategy for flexible energy storage systems operating in ultra-low temperatures environments.
Suppression of vortex electromagnetic wave divergence by modulating the wave front dislocation line
Authors: Zhang Yang , He Liangzhi , Yan Xu , Deng Lichen
Published: August 2024

When the beam is transmitted in the atmosphere, under the influence of atmospheric turbulence, the beam will occur energy attenuation, wavefront distortion and other phenomena, which will seriously affect the transmission and reception of information. Based on the PB phase principle enhances the focusing capability of the auto-focusing Airy Gaussian vortex beam by regulating the wavefront error curvature of the vortex beam, and then enhances the resistance of the beam against atmospheric turbulence. The results reveal the curvature of the wavefront error curve and the peak intensity of the light field of the beam can be changed by changing the beam polarization state parameters n and m in the focal plane. The auto-focusing Airy Gaussian vortex beam with the polarization state parameters m=1, n=4 increases the peak magnitude of the light field intensity in the focal plane of the auto-focusing Airy Gaussian vortex beam with the polarization state parameters m=0, n=0 to 1.99 times. Demonstrate that geometric phase tuning the auto-focusing Airy Gaussian vortex beams can enhance resistance to atmospheric turbulence. Furthermore, this paper extends this method to the field of millimeter wave. Under the transmission distance of 1km, the peak intensity of the initial vortex electromagnetic wave after the geometric phase adjustment is increased to 3.33 times, and the receiving radius of the target surface is reduced to 0.24 of the initial vortex electromagnetic wave. It shows that geometric phase-regulated vortex electromagnetic wave can suppress vortex electromagnetic wave divergence and have potential applications in wireless communication.
Estimating lithium-ion battery capacity from relaxation voltage using a machine learning approach
Authors: Zhu Guopeng , Lv Xinquan , Zhang Yan , Lu Yi , Han Guangming , Chen Meixin , Zhou Yue , Jin Hao
Published: August 2024

Lithium-ion batteries are pivotal in the development of electric vehicles and energy storage systems, with their State of Health (SOH) being crucial for both academic research and industry applications. Estimating battery capacity accurately presents significant challenges due to the complex aging mechanisms involved. In this study, we introduce a novel approach using a one-dimensional convolutional neural network (1D CNN) that leverages relaxation voltage data to predict battery capacity. The model is mainly structured with two convolutional layers, one maxpool layer, and two fully connected layers, each specially optimized to meet the unique requirements of this application. A key feature of this model is the use of a unit kernel size in the initial layer, which enhances the capture of non-linearities in the data. Our results demonstrate a prediction percentage error of -0.03% ± 0.77%, outperforming many state-of-the-art models in terms of accuracy and robustness. Furthermore, the model’s compactness, with approximately 5k parameters, suggests its suitability for edge deployment in future applications, promising significant advancements in real-time battery management.
Two-dimensional phononic crystal based on-chip surface acoustic waves convergence
Authors: Li Zhaoling , Xuan Weipeng , Xie Weilun
Published: August 2024

Surface acoustic wave (SAW) technology is widely used in microwave electronics, and is often used to produce such devices as filters, sensors and actuators. In this work, a structure constructed by a two-dimensional phononic crystal (PnC) is designed to converge two incident SAWs. Based on the equifrequency contour (EFC) theory and unique band characteristic at high-symmetric point, aggregating SAWs within the PnC region and then the converged output could be observed. This specific utilization of PnC to converge SAWs can be a useful complement to the delicate manipulation, which can be highly desirable for developing SAWs device.
Threshold Voltage Modulation Strategies for ZnO TFT and Enhanced Performance of Depletion-Mode Inverters
Authors: Wang Liangpei , Yuan Ze , Wang Chenyu , Luo Shangcong , Huang Tianfeng , Zhang Junfeng , Ye Zhi
Published: August 2024

This work presents the analysis and comparison of the adjustment mechanism and electrical characteristics of the depletion-mode transistors fabricated by the three threshold modulation methods. the electrical performance test of the depletion-mode inverter composed of each depletion-mode TFTs is completed, among which, the depletion transistor fabricated by electron beam evaporation of 100 nm AL as the gate material has excellent performance, with a switching ratio of 108, a subthreshold swing of 281 mV/dec, and a turn-on voltage adjusted from 0 V to -6.8 V, which has the advantages of strong device stability and good negative modulation. the output current of the depletion inverter composed of the AL gate is up to the μA level. excellent driving capability is shown, A foundation is established for the subsequent construction of complex circuits based on zinc oxide thin-film transistors.
Development of a hydrogen permselective silica membrane and its application to the thermochemical water splitting method
Authors: Nomura Mikihiro
Published: August 2024

The thermochemical water splitting IS process is one of the hydrogen production method to use a heat directly. The temperature of the thermal decomposition of water (4000 K) can be reduced under 700 K by introducing I2 and SO2 as recycling catalysts. One of the problems in the IS process is that the conversion of the HI decomposition reaction is low at about 20%. If a membrane reactor with the H2 permselective membrane is applied to the HI decomposition reaction, the hydrogen can be extracted to improve the HI conversion. We focus on a counter diffusion CVD method for the preparation method of the membranes. Two reactants (e.g. silica precursor and oxidant) are provided at the opposite side of the porous substrates and hybrid silica is deposited inside the pore of the substrate. The pore sizes are controlled by introducing organic functional groups to silica precursor. In this study, the silica hybrid membrane with high H2 permeation performance and high H2/HI selectivity were developed by introducing organic functional groups. Effects of the organic functional groups were summarized by using a pore model. The HI gas and other inorganic gases permeation performance were tested through silica hybrid membranes.
Hydrogen and Hydrocarbon Fuels from Abundant Resources and Solar Energy: Current Activities and Perspectives in DLR’s Institute of Future Fuels
Authors: Roeb Martin , Sattler Christian
Published: August 2024

Chemical energy carriers, centralized around Hydrogen products, will play an essential role in the energy system of the future. The advantages of comparable power density and reliability to fossil fuels, as well as their versatile applications, make them an important component of the energy transition. DLR is conducting research on technologies that ensure the efficient production of chemical energy carriers. De-carbonizing the energy system and of industry needs not only electricity from carbon-neutral sources but also green fuels. DLR’s Institute of Future Fuels steps in at that point by developing and investigating production methods for hydrogen and synthetic hydrocarbon fuels. Those are mostly based on the utilisation of concentrated sunlight but in additionally also on other renewable resources. The development goes all along the way of identifying and qualifying suitable functional materials, over integrating them in specific components allowing to introduce renewable energy into the process, towards the demonstration of a representative overall process along the chain from raw materials to the targeted fuel. Hydrogen plays a key role in this context, either as the fuels itself or as a core building block of synthetic hydrocarbon fuels and chemicals. The institute has a specific focus on thermochemical processes, in particular for water and CO2 splitting, but also on air separation, thermochemical heat storage and upgrading of biomass and other hydrocarbons.
Hydrogen permselective membrane prepared by plasma treatment of porous ceramic substrates
Authors: Sato Haruki , Ito Myuwako , Nomura Mikihiro
Published: August 2024

Demand for hydrogen is increasing as we move toward a decarbonized society. Membrane separation methods have attracted attention as a way to reduce the cost of hydrogen production. In this study, we focused on boron nitride (BN) as a chemically stable porous substrate material. CVD treatment at 250°C has been investigated for porous BN, but selectivity did not develop. Therefore, we focused on atmospheric pressure plasma as a treatment method. The objective of this study is to develop a membrane with high hydrogen selective permeance by immersion in silicon alkoxide after plasma treatment. After plasma treatment for 100 s, the H2 permeance was 3.52×10−6 [mol m−2 s−1 Pa−1] and the H2/N2 permeance ratio was 18.25. Since molecular hydrogen can pass between the BN crystal faces, it is considered that silica reacted with the hydroxy groups formed on BN by plasma treatment and blocked the space between crystals. Therefore, it is suggested that the performance of the separation membrane changes depending on the number and distribution of hydroxyl groups by plasma treatment, and further stabilization is needed.
Alkaline composite inorganic membrane for high temperature carbon dioxide separation
Authors: Ito Itsuki , Yanai Daiki , Tsuyuki Hiroto , Nomura Mikihiro
Published: August 2024

The development of carbon dioxide separation and capture technologies is necessary to achieve a carbon-neutral society. Focusing on Direct Air Capture (DAC), a technology for the direct capture of carbon dioxide from the atmosphere. Although Li4SiO4-based membranes have been reported as a separation membrane development using alkali ceramics, the carbon dioxide desorption temperature of Li4SiO4 is high, around 700°C. To use it for CO2 methanation and methanolation reactions, it’s necessary to develop separation membranes for m-DAC using materials that can desorb CO2 at around 300°C. This hasn’t yet been considered. Therefore, the aim of this study was to develop an effective alkaline adsorbent for CO2 recovery that absorbs and desorbs CO2 at around 300°C. Membranes with Na2CO3 supported on an α-Al2O3 substrate showed a permeability of 3.58 x 10−10 [mol m−2 s−1 Pa−1] for N2 and 3.93 x 10−10 [mol m−2 s−1 Pa−1] for CO2 after two coatings, which is selective for CO2. However, the membranes didn’t show CO2 adsorption and the substrate broke after three coatings, suggesting that the α-Al2O3 substrate was alkali-sensitive and that the thermal decomposition of Na2CO3 wasn’t advanced.
Integrating Experimental and Numerical Data for Improved Steam Reforming Simulation with Deep Learning
Authors: Pizoń Zofia , Kimijima Shinji , Brus Grzegorz
Published: August 2024

In this paper, a data-driven methane steam reforming simulation is developed and used to predict the post-reaction mixture composition. Until today, methane steam reforming remains a predominant hydrogen production method, yet modeling its complex reactions remains a significant challenge due to the intricate interplay of process variables. Here, we show an artificial neural network simulator that can effectively model these reactions, offering precise predictions based on parameters like temperature, inlet gas composition, methane flow, and nickel catalyst mass. Our approach to data curation integrates experimental, interpolated, and theoretically calculated values and refining the model by assessing the relative importance of each data category. Various neural network structures were tested before ultimately identifying an optimal architecture with a 5-6-8-6-4 network structure. The network underwent 6000 epochs of training, leading to a model that demonstrates excellent agreement with experimental observations, as evidenced by the mean squared error of 0.000217 and the Pearson correlation coefficient of 0.965. Moreover, all process trajectories predicted by the network are characterized by a smooth course and are within a physical range of values. Therefore, this work overcomes a common challenge in chemical process simulation using neural networks and also sets a possible direction for future research in this field.
A recognition algorithm applied to apple orchard picking robot
Authors: Li Yida , Lin Shengchun , Ma Jingjuan
Published: August 2024

In complex orchard environments, the recognition accuracy of picking robots is often affected by lighting conditions, leaf occlusion, and overlapping apple images. To address the problem of inaccurate judgment of apple fruit maturity by picking robots in orchards, we conducted research on Red Fuji apples and proposed a comprehensive model for judging apple maturity. The model first accurately identifies apples through the instance segmentation algorithm Mask R-CNN, divides apple boundaries, and then combines apple image RGB and apple fruit shape proportion to comprehensively analyze apple Ripeness Ratio and fruit Aspect Ratio, and finally obtains apple maturity and judges whether to pick. We applied and tested the model through the MinneApple dataset, A total of 10,305 apples were detected, including 3,732 fully ripe apples, 4,074 half ripe apples, and 2,499 unripe apples, and the experimental results showed that the accuracy of the model was 91.4%, which can better meet the requirements of picking robot recognition. We hope that our model can provide ideas for research on different apple maturity judgments based on mechanical automation picking.
Analysis and study on path planning algorithms in the further mobile action
Authors: Yang Hangqi
Published: August 2024

This study investigates the use of four common path planning algorithms - RRT (Rapidly-Exploring Random Tree), Dijkstra, A*, and ACO (Ant Colony Optimization) - for autonomous endpoint search in pre-defined grid mazes. Path planning is a crucial problem in robot navigation, involving the ability to find optimal paths with the minimum running time in complex environments. In this research, it firstly introduces the basic principles and mechanisms of each algorithm. RRT is a random sampling-based algorithm that explores the search space by generating a tree-like structure randomly. Based on graph search, Dijkstra’s method determines the best solution by figuring out the shortest routes between the starting point and other places. A* algorithm combines heuristic functions and the concept of Dijkstra’s algorithm to reduce the search space and improve search efficiency. ACO algorithm simulates the behavior of ants searching for food by updating pheromone information and employing search strategies to find the optimal path. Next, a series of experiments to evaluate the performance of these four algorithms in pre-defined mazes. Search time, path lengths, and accuracy of the optimal path for each algorithm are recorded. Eventually, the result analysis of the four algorithms and the comparison are given in the last two sections of this article. In addition, A* algorithm has the best performance under the conditions set out in this article.
A review of advanced techniques in simultaneous localization and mapping
Authors: Song Ruojing , Zhang Han , Yao Yutong , Ahmed Dastagir
Published: August 2024

Simultaneous Localization and Mapping (SLAM), as one of the key elements of robot vision, has become an emerging topic in the past 3 decades. The focus of SLAM is to reconstruct the map surrounding the robot from sensors like camera or LiDAR and meanwhile, find the location of the robot itself inside that map. With the contribution of researchers, many different techniques and algorithms have been developed to improve the accuracy of SLAM. The main difference between those techniques is the choice of sensor to solve the SLAM problem. Some approaches are based on LiDAR sensors, which are LiDAR SLAM. Some of them are based on cameras, e.g.: Monocular, stereo, or RGB-D cameras, which are also known as visual SLAM (VSLAM). We will also review how deep learning methods like CNN and RNN together optimize VSLAM computation and remove some of the old modules from the traditional SLAM framework. By comparing the most recent techniques, we will start with some general differences between these techniques and mention some explicit differences in terms of applications. Finally, we will discuss the advantages and drawbacks of both techniques and propose some challenges and future direction towards both techniques.