bluuebunny/crossref_metadata_2025_split huggingface dataset:
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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.
Exposure to ambient PM2.5 and its association with the loss of labor productivity of manufacturing plants in India
Authors: Majumder Piyali , Chaudhary Ekta , Dey Sagnik 
Published: August 2024

Abstract
Evidence of the impacts of ambient air pollution on health in India has been expanding. However, the economic impact of air pollution has rarely been explored. Here, we examined the impact of exposure to ambient fine particulate matter (PM2.5) derived from satellite data at 1 km × 1 km resolution on the productivity of the manufacturing plants using a micro-level dataset for the period 2008–2009 and 2009–2010 across 465 districts in India. Using a system generalized methods of moments techniques, we estimated that for every 10% increase in PM2.5 exposure, labor productivity decreases by 14.8% after controlling for the confounding factors. For exposure exceeding the national ambient air quality standard of annual PM2.5 in India (40 μg m−3), the labor productivity decreases by a bigger margin (20%) for the same margin of increase in PM2.5. We found that labor productivity loss due to ambient air pollution was lower for plants using capital-intensive production techniques. The labor productivity in plants with a higher share of blue-collar workers was more sensitive to exposure to PM2.5 as opposed to plants with a higher share of supervisors or managerial staff. This suggests that plant-level managerial skill and capital-intensive production techniques (including expenditure on pollution control and abatement equipment) will be critical in mitigating air pollution-induced labor productivity loss across manufacturing plants in India.
Investigating the Impact of Ground Granulated Blast Furnace Slag (GGBS) and Cement Kiln Dust (CKD) on fine grained Soil Subgrade in Al Hillah City
Authors: Jamal Ahmed Zainab , Jafer Hassnen , Ahmed Al-Kremy Abdul Rudha 
Published: August 2024

Abstract
In many parts of the world, fine grained soils are regarded as one of the major concerns for civil engineering projects. To mitigate these problems, fine grained soils are typically replaced with stronger materials. However, due to the high cost of this approach, various researchers have tried alternative approaches, the most popular of which is soil stabilization. This method could be deemed suitable for pavements. The research focuses on stabilizing fine soil through the utilization of cement kiln dust (CKD) and ground granulated blast slag (GGBS). This research involves treating fine soil with 10% of the total binder consist from (GGBS+CKD), with the amount of GGBS to CKD determined by dry soil weight as follows: (10% GGBS + 0% CKD), (8% GGBS + 2% CKD), (6% GGBS + 4% CKD), (2% GGBS + 8% CKD), and (0% GGBS + 10% CKD). The compaction parameters, consistency limitations, and the findings of the Unconfined Compressive Strength (UCS) test were used to assess the enhancement levels. In addition, specimens were subjected to UCS testing after (7-28) days of curing. The findings demonstrated that the combination of GGBS activated by CKD notably enhanced the physical characteristics of fine soil. The Plasticity Index (PI) decreased from 7.4 for untreated soil to 4.8 for a mixture containing 10% binder, comprised of 2% GGBS and 8% CKD, and to 4.25 for a single-binder mixture with 10% CKD. Furthermore, the unconfined compressive strength test (UCS) results indicated that a 10% binder mixture of 8% GGBS and 2% CKD increased the UCS by 2.9 to 5.9 times compared to untreated soil after 7-28 days of curing.
Sustainable Use of Recycled Concrete Aggregate for Soil Improvement
Authors: Salah Hashem Asraa , Shaban Alaa M. 
Published: August 2024

Abstract
One of the most significant issues that engineers face in geotechnical engineering projects is weak and problematic soils. Therefore, there is always a necessity to develop sustainable methods to improve properties of weak soils in order to enhance the performance of the structures built upon them. The aim of this research is to improve the characteristics of loose, poorly graded sand soil using recycled concrete aggregate (RCA). RCA with various grain-size distributions was utilized as a partial replacement for the soil. The replacement contents were 5%, 10%, 15%, and 20% by the weight of the treated soils. The compaction and bearing resistance characteristics of the treated soils were investigated. The results showed that the inclusion of RCA significantly improves engineering characteristics of the soil in terms of California bearing ratio (CBR) and maximum dry density (MDD). Additionally, it was found the 15% represents optimum RCA content that can be utilized for stabilizing weak subgrade soils. Consequently, the study’s findings support the value of including RCA in field application stabilization plans for geotechnical and road engineers.
Study of the Dispersion of Bitumens Modified with Secondary Polyethylene Terephthalate
Authors: Ahmed Adnan Hayder Al-Razoqi , Alexandra Olegovna Shrubok , Igorevna Zholnerkevich Veronika , Wako Bleck Junior Happi 
Published: August 2024

Abstract
The paper considers the possibility of using polyethylene terephthalate (PET) waste in the production of bitumen. The article presents the results of modifying bitumen with PET powders. A method has been developed for producing polymer powders from waste PET bottles using a non-toxic solvent. The powders obtained using this technology are finely dispersed and can be used to modify bitumen. By mixing PET powder into petroleum bitumen, it is possible to obtain it can homogeneous binder with a leads to increase the plasticity interval and lowering the penetration values. It has been found that with the introduction of 4% wt. PET powder softening temperature of bitumen increases by 15°C, brittleness temperature decreases by 6.6°C, and penetration – by 22×0.1 mm. It has been established that while the change in the plasticity interval, the adhesive properties of bitumen increase, which is explained by the increased content of aromatic compounds and resins in them.