bluuebunny/crossref_metadata_2025 huggingface dataset:
Distribution network risk assessment with distributed generation impact based on non-sequential Monte Carlo method
Authors: Zhang Dahua , Wang Xu , Zhang Xiaojuan , Lin Ziqing , Hu Baiji , Zhu Yayun , Cao Jingyi
Published: April 2024

At present, the development of renewable energy distributed generation is fast, and the distribution network is facing significant challenges. Unlike traditional fossil fuels, new energy generation is characterized by significant volatility caused by uncertain factors like environmental and meteorological conditions. This paper presents a risk assessment index system that utilizes the non-sequential Monte Carlo method to tackle the given issue. The system comprises two assessments: voltage limit risk and reliability index assessment. Through the application of the Monte Carlo method, a comparative analysis of the results with alternative calculation approaches is conducted. Subsequently, these findings are utilized to evaluate the effectiveness of two commonly employed protection strategies.
Research on AGV path planning in new energy battery workshop
Authors: Zhao Chundi , Zhang Linli , Liu Zhouyuan , Shang Xiao , Song Linsen , Zhang Yiwen , Gao Yan
Published: April 2024

In recent years, more and more factories have begun to use automatic guided vehicles (AGVs) instead of manual work in production, transportation, and other links, which has broad application prospects and market prospects. This paper takes the new energy battery workshop as the research object, analyzes the AGV operation plan in the workshop according to the overall workflow of the workshop material distribution, and uses AnyLogic to simulate the map to complete the algorithm test. A* global algorithm is optimized and improved, and the optimal shortest route with more smoothness from the beginning of the AGV to the end of the AGV is planned, which greatly improves the operation efficiency of the whole scheduling and has high practicability.
Transmission line defect detection based on feature fusion
Authors: Zhao Liuqi , Huang Min , Zhao Hanghang , Zhang Yuheng , Wen Xing , Zhang Xin , Wang Ning
Published: April 2024

Transmission lines are the lifeblood of the power system, and regular line inspections are required to ensure the operation of transmission lines. However, due to the special high-voltage environment of transmission lines, the pictures collected by UAVs are characterized by many background interferences and small defect samples, and it is difficult to directly detect line inspection methods based on deep learning. For this reason, this paper proposes a high and low-dimensional fusion framework, which divides the target detection task into four modules, and greatly reduces the technical volume of the model while retaining the high-dimensional features through the collaboration between modules. This paper also introduces the window attention mechanism based on YOLOv7, which allows the model to focus on local information and improve the model’s detection ability on small targets and also introduces the Wise-IOU loss to improve the model’s prediction accuracy and inference time. Experiments prove that the method in this paper can significantly improve the accuracy of model prediction while meeting the speed requirements of industrial scenarios.
Characterization of electric field distribution in valve-side casing of 500 kV converter transformer considering AC and DC mixed voltage
Authors: Chen Yongtao , Liu Zhi , Wang Pengbo , Yang Fan
Published: April 2024

The converter transformer, as one of the indispensable core devices in the ultra-high voltage direct current (UHVDC) transmission system, exhibits highly complex electric field distribution characteristics within the valve-side bushing. In-depth research into the electric field distribution characteristics and variation patterns of the valve-side bushing under both alternating current (AC) and direct current (DC) voltage conditions is crucial for ensuring the reliable operation of the transmission system. This study focuses on investigating the electric field distribution of the valve-side bushing under AC and DC voltage excitations. A full-scale finite element model of the valve-side bushing of the converter transformer is constructed using finite element simulation software for modeling and research purposes. The results indicate that the internal electric field distribution of the bushing is generally similar under both AC and DC voltage excitations. However, in the SF6-filled region, a steeper voltage drop is observed under DC excitation compared to AC. The space charges generated in the DC electric field due to field polarization have a significant impact on the distortion of the electric field on the inner and outermost layer electrode plates. These conclusions provide theoretical guidance for the insulation design of the valve-side bushing in converter transformers at the 500 kV voltage level and similar ratings.
Regional photovoltaic grid power generation output prediction based on SVM and autoencoder
Authors: Sun Mingyu , Lv Fenglei , Du Dongyan , Ren Jinggang , Guan Yongchang , Zhu Hongqing , Zhang Ranran , Ouyang Mengmeng
Published: April 2024

Photovoltaic power generation, which is an important component of clean energy, has been widely used around the world. However, the predictability of photovoltaic power generation systems has always been a challenge because it is affected by meteorological conditions and other external factors. Therefore, accurate photovoltaic power generation output prediction is crucial for the stability and reliability of the power system. This work aims to use two advanced machine learning technologies, supporting vector machine (SVM), and autoencoder (Autoencoder), to improve the prediction performance of regional photovoltaic grid power generation output. First, this article introduces the autoencoder and SVM network, and then we use the SVM algorithm to analyze and model these data. By using SVM, we can build a highly accurate photovoltaic power generation output prediction model that can predict on different time scales, from hourly level to daily level. Next, the principle and working mechanism of the support vector machine will be introduced in detail, and its advantages in processing complex data and high-dimensional features will be explained. Then, this article will use the grid search method to optimize the support vector machine model. A new cluster performance index based on electrical distance and regional voltage regulation capability is proposed, which divides the distribution network into multiple clusters. The voltage control strategy combines cluster autonomous optimization of time scales and distributed inter-cluster coordination optimization. Then we select evaluation indicators to evaluate the obtained model. The results show that the model can predict power generation output and provide a reference for making point arrangements. In terms of model building, this article will explain how to select appropriate features and parameters and use the collected data to train the SVM model. We also perform a detailed evaluation and analysis of the model’s performance. We use a variety of evaluation metrics to measure model accuracy and stability. Experimental results show that the SVM and autoencoder hybrid model have higher prediction accuracy and better generalization performance than traditional methods.
Failure analysis of strain clamps on a ± 800 kV transmission line
Authors: Teng Yulin , Xie Jinpeng , Chen Shanyi
Published: April 2024

During X-ray testing of a ± 800 kV ultra-high voltage direct current transmission line tension clamp, it was found that the internal steel core had broken. To investigate the cause of the steel core fracture, the failed clamp was dissected and analyzed, including chemical material testing, mechanical performance testing, fracture morphology inspection, and analysis of the compression process of the tension clamp. A preliminary judgment was proposed on the cause of the internal steel core fracture of the tension clamp, And verification experiments and simulation analysis were conducted to preliminarily determine the cause of the steel core fracture, and it was ultimately concluded that the main reason for the steel core fracture was the incorrect compression sequence of the large cross-section wire tension clamp.
Stability modeling of MMC-HVDC transmission system with differential flat control
Authors: Feng Zheng , Li Hui
Published: April 2024

AC asymmetry grid conditions put forward higher requirements for the stability control and performance of MMC-HVDC transmission systems. This thesis proposes a small-signal modeling method of an MMC-HVDC transmission system with differential flat control for AC asymmetry grid conditions, incorporating dynamic models of MMC internal characteristics, new control systems, circulation suppressors, and DC lines. To prove the accuracy of the pre-sended small-signal model, a two-terminal MMC-HVDC test system with differential flat control is constructed on the PSCAD/EMTDC platform.
Suitability of capacitor allocation methods in different kinds of distribution networks
Authors: Chen Yun , Pan Ziyan , Liu Jilei , Liu Shipeng , Zhang Jie , Ma Yongfei , Li Yadi , Ge Yimeng , Lu Binxian
Published: April 2024

In the context of power systems with “a high percentage of renewable energy” and “a high percentage of power electronic equipment”, reactive power capacitors are demanded to support the voltage and keep the economic operation of the distribution network. Reasonable configuration of reactive power compensation including capacitor location and compensation capacity can keep the system voltage at a reasonable level, improve the system voltage stability, and reduce the network loss. In this paper, a reactive power optimization model based on three capacitor allocation methods, namely the reactive power sensitivity method, the active power loss method, and the voltage stability method, was established. Considering the stability and economy of distribution network operation, the suitability of the different methods in different kinds of distribution networks was evaluated in terms of the indicators of minimizing the network loss, minimizing the voltage offset, and minimizing the system operation cost. The simulation results show that the active power loss method is the most suitable for reducing voltage fluctuations and network losses.
Development of a high temperature and high pressure downhole string dynamic load monitor
Authors: Yu Yang , Li Guoliang , Zhao Chuang , Cao Yinping , Liu Wangheng
Published: April 2024

Due to the uncertainty of the formation and the complexity of downhole tubular string loads and deformations, the existing tubular string static theory is difficult to explain and lacks support from measured data of tubular string dynamic loads. Therefore, a dynamic loads monitor for oil and gas tubular string is developed, which can synchronously measure the internal pressure, external pressure, axial force, and vibration acceleration for the string during the whole operation process. The monitor realizes simultaneous monitoring of tubular string load and vibration response parameters while meeting the requirements of various sensor placement spaces and structural strength safety, simplifying the on-site test process. By adding a power control circuit to the circuit system, the working time of the monitor is extended. The high temperature calibration test of the monitor is carried out, and the maximum calibration error is 0.10%, which meets the requirements of measurement accuracy.
Remaining useful life prediction method of lithium-ion battery Based on KPCA-IVMD-SE-DNN
Authors: Zhou Chen , Xu Hanbin , Wei Yiying , Huang Chao
Published: April 2024

In this paper, aiming at the problems of feature processing and capacity regeneration in the prediction of remaining useful life (RUL) of lithium-ion batteries, an RUL prediction method based on kernel principal component analysis (KPCA), improved variational mode decomposition (IVMD), sample entropy (SE), and deep neural network (DNN) are proposed. Firstly, six health indicators (HI) are extracted by analyzing the character of batteries charging and discharging process, and their correlation with capacity is calculated. Secondly, the KPCA is used to denoise and simplify the dimension of the HI set and ensure that they fully contain the degradation information. Thirdly, the battery capacity is decomposed into trend and interference components by using the improved VMD of the central frequency method (CFM), and the reconstruction is carried out according to the SE of each component to increase the efficiency and accuracy of prediction. Finally, the prediction model is constructed based on DNN. The experimental analysis of NASA battery data sets shows that the proposed method has the best prediction accuracy, efficiency, and robustness than DNN, KPCA-DNN, KPCA-EMD-DNN, KPCA-VMD-SE-DNN, and so on.
Sensitivity of 3D Convective Urca Simulations to Changes in Urca Reactions
Authors: Boyd B , Smith Clark A , Calder A C , Townsley D M , Zingale M
Published: April 2024

A proposed setting for thermonuclear (Type Ia) supernovae is a white dwarf that has gained mass from a companion to the point of carbon ignition in the core. There is a simmering phase in the early stages of burning that involves the formation and growth of a core convection zone. One aspect of this phase is the convective Urca process, a linking of weak nuclear reactions to convection that may alter the composition and structure of the white dwarf. Convective Urca is not well understood and requires 3D fluid simulations to realistically model. Additionally, the convection is relatively slow (Mach number less than 0.005) so a low-Mach method is needed to make simulating computationally feasible. Using the MAESTROeX low-Mach hydrodynamics code, we investigate recent changes to how the weak reactions are modeled in the convective Urca simulations. We present results that quantify the changes to the reaction rates and their impact on the evolution of the simulation.
Amplification and Dissipation of Magnetic Fields in Accreting Compact Objects
Authors: Bugli M. , Guilet J. , Del Zanna L. , Mignone A. , Mattia G. , Berta V. , Raynaud R. , Reboul-Salze A.
Published: April 2024

Magnetic fields play a crucial role in shaping the dynamics of accreting compact objects. Whether we consider the formation of a proto-neutron star during the gravitational collapse of a massive star or the accretion disk around a black hole after a compact binary merger, a key process that remains challenging to include in large-scale simulations is the amplification and dissipation of magnetic fields driven by turbulent fluid motions. Despite the enormous increase in computational power currently available, the large separation between all the relevant spatial and temporal scales still poses severe limits to what can be achieved with ideal fluid simulations. One way to tackle such issue is to rely on sub-grid models, which however need to be appropriately tuned in light of models probing the small-scale dynamics. In this work we present the current state-of-the-art of dynamo models in proto-neutron stars, which aim at describing the amplification of magnetar-like magnetic fields during the gravitational collapse of a massive star. We also review some of the works from the past few years that included turbulent dynamos in accretion disks around a black holes, relying on a mean-field formalism. Finally, we will present a recent study on polar jets with explicit turbulent resistivity which showcases the importance of employing highly accurate numerical schemes.
pynucastro 2.1: an update on the development of a python library for nuclear astrophysics
Authors: Clark Alexander Smith , Johnson Eric T. , Chen Zhi , Eiden Kiran , Zingale Michael , Boyd Brendan , Johnson Parker T. , DaCosta Luis Rangel
Published: April 2024

pynucastro1 is an open-source python library that provides visualization and analyze techniques to classify, construct, and evaluate nuclear reaction rates and networks. It provides tools that allow users to determine the importance of each rate in the network, based on a specified list of thermodynamic properties. Additionally, pynucastro can output a network in C++ or python for use in simulation codes, include the AMReX-Astrophysics simulation suite. We describe the changes in pynucastro since the last major release, including new capabilities that allow users to generate reduced networks and thermodynamic tables for conditions in nuclear statistical equilibrium.
Secular Outflows from Long-Lived Neutron Star Merger Remnants
Authors: Radice David , Bernuzzi Sebastiano
Published: April 2024

We study mass ejection from a binary neutron star merger producing a long-lived massive neutron star remnant with general-relativistic neutrino-radiation hydrodynamics simulations. In addition to outflows generated by shocks and tidal torques during and shortly after the merger, we observe the appearance of a wind driven by spiral density waves in the disk. This spiral-wave-driven outflow is predominantly located close to the disk orbital plane and have a broad distribution of electron fractions. At higher latitudes, a high electron-fraction wind is driven by neutrino radiation. The combined nucleosynthesis yields from all the ejecta components is in good agreement with Solar abundance measurements.
Three-dimensional Modeling of the Solar Wind and Local Interstellar Medium Interaction with Pickup Ions in the Presence of Heliospheric Current Sheet
Authors: Bera R. K. , Fraternale F. , Pogorelov N. V.
Published: April 2024

Our three-dimensional, time-dependent, multi-fluid model has been used to investigate the solar wind (SW)–local interstellar medium (LISM) interaction with pickup ions (PUIs) treated as a separate fluid. A non-zero, but fixed, angle between the Sun’s magnetic and rotation axis is adopted. The flow of the plasma mixture (thermal SW protons, PUIs, and electrons), is described by the system of ideal magnetohydrodynamic equations with the source terms responsible for charge exchange between ions and neutral atoms. Different populations of neutral atoms are governed by the individual sets of the Euler equations. As the standard Rankine–Hugoniot relations are not appropriate to describe the anisotropic behavior of PUIs at the termination shock, we use a kinetically-derived set of boundary conditions at it. We extend our previous work [1] and perform these new simulations on a Cartesian grid. This approach allows us to maintain a uniform grid resolution in all directions, without compromising resolution, at large distances from the Sun. The possibility of transition of the SW flow to a stochastic regime in the region between the termination shock and heliopause is further investigated.
Space Weather with Quantified Uncertainties: Improving Space Weather Predictions with Data-Driven Models of the Solar Atmosphere and Inner Heliosphere
Authors: Pogorelov Nikolai V. , Arge Charles N. , Caplan Ronald M. , Colella Phillip , Linker Jon A. , Singh Talwinder , Van Straalen Brian , Upton Lisa , Downs Cooper , Gebhart Christopher , Hegde Dinesha V. , Henney Carl , Jones Shaela , Johnston Craig , Kim Tae K. , Marble Andrew , Raza Syed , Stulajter Miko M. , Turtle James
Published: April 2024

To address Objective II of the National Space Weather Strategy and Action Plan “Develop and Disseminate Accurate and Timely Space Weather Characterization and Forecasts” and US Congress PROSWIFT Act 116–181, our team is developing a new set of open-source software that would ensure substantial improvements of Space Weather (SWx) predictions. On the one hand, our focus is on the development of data-driven solar wind models. On the other hand, each individual component of our software is designed to have accuracy higher than any existing SWx prediction tools with a dramatically improved performance. This is done by the application of new computational technologies and enhanced data sources. The development of such software paves way for improved SWx predictions accompanied with an appropriate uncertainty quantification. This makes it possible to forecast hazardous SWx effects on the space-borne and ground-based technological systems, and on human health. Our models include (1) a new, open-source solar magnetic flux model (OFT), which evolves information to the back side of the Sun and its poles, and updates the model flux with new observations using data assimilation methods; (2) a new potential field solver (POT3D) associated with the Wang–Sheeley–Arge coronal model, and (3) a new adaptive, 4-th order of accuracy solver (HelioCubed) for the Reynolds-averaged MHD equations implemented on mapped multiblock grids (cubed spheres). We describe the software and results obtained with it, including the application of machine learning to modeling coronal mass ejections, which makes it possible to improve SWx predictions by decreasing the time-of-arrival mismatch. The tests show that our software is formally more accurate and performs much faster than its predecessors used for SWx predictions.
Modeling electron acceleration during the contraction of a magnetic island
Authors: Deuja Atit , Che Haihong
Published: April 2024

Magnetic reconnection releases the magnetic energy through the contraction of multi-magnetic island leading to the electron acceleration as proposed by Drake et. al in 2006. However, how the released magnetic energy is converted into electron’s kinetic energy is still theoretically not well understood. We model in particular the kinetic process assuming the adiabatic contraction of magnetic island that induces electric field which is proportional to the vector potential of the magnetic island and approximate the magnetic island with an ellipse. Under this model, we show that the energy gain is achieved through the work of inductive electric field. We further show that the curvature drift which is along the inductive electric field dominates the energy gain. We compared our model with the magnetic island formed by tearing instability in a 2.5D particle-in-cell simulation of magnetic reconnection and found the results from the model consistent with that of the simulation.
Peculiarities of charged particle kinetics in spherical plasma
Authors: Kolobov V I , Arslanbekov R R , Liang H
Published: April 2024

We describe transient kinetic simulations of partially ionized weakly-collisional plasma around spherical bodies absorbing or emitting charged particles. Numerical solutions of kinetic equations for electrons and ions in 1D2V phase space are coupled to an electrostatic solver using the Poisson equation or quasineutrality condition for small Debye lengths. The formation of particle groups and their contributions to electric current flow and screening of charged bodies by plasma are discussed for applications to Langmuir probes and solar wind.
An operator-splitting numerical scheme for relativistic magnetohydrodynamics
Authors: Phillips David , Komissarov Serguei
Published: April 2024

We describe a novel operator-splitting approach to numerical relativistic magnetohydrodynamics designed to expand its applicability to the domain of ultra-high magnetisation. In this approach, the electromagnetic field is split into the force-free component, governed by the equations of force-free degenerate electrodynamics (FFDE), and the perturbation component, governed by the perturbation equations derived from the full system of relativistic magnetohydrodynamics (RMHD). The combined system of the FFDE and perturbation equations is integrated simultaneously, for which various numerical techniques developed for hyperbolic conservation laws can be used. At the end of every time-step of numerical integration, the force-free and the perturbation components of the electromagnetic field are recombined and the result is regarded as the initial value of the force-free component for the next time-step, whereas the initial value of the perturbation component is set to zero. To explore the potential of this approach, we build a 3rd-order WENO code, which was used to carry out 1D and 2D test simulations. Their results show that this operator-splitting approach allows us to bypass the stiffness of RMHD in the ultra-high-magnetisation regime where the perturbation component becomes very small. At the same time, the code performs very well in problems with moderate and low magnetisation too.
Challenges Modeling the Low-Luminosity Type Iax Supernovae
Authors: Feldman C , Eisenberg E , Townsley D M , Calder A C
Published: April 2024

Numerical models allow the investigation of phenomena that cannot exist in a laboratory. Computational simulations are therefore essential for advancing our knowledge of astrophysics, however, the very nature of simulation requires making assumptions that can substantially affect their outcome. Here, we present the challenges faced when simulating dim thermonuclear explosions, Type Iax supernovae. This class of dim events produce a slow moving, sparse ejecta that presents challenges for simulation. We investigate the limitations of the equation of state and its applicability to the expanding, cooling ejecta. We also discuss how the“fluff”, i.e. the low-density gas on the grid in lieu of vacuum, inhibits the ejecta as it expands. We explore how the final state of the simulation changes as we vary the character of the burning, which influences the outcome of the explosion. These challenges are applicable to a wide range of astrophysical simulations, and are important to discuss and overcome as a community.
Tackling exascale systems for astrophysics
Authors: Roussel-Hard Lou , Audit Édouard , Dessart Luc , Padioleau Thomas , Wang Yushan
Published: April 2024

Exascale systems are now appearing in the US, Japan, in Europe with two exascale procurements in the coming years and very likely in China. These systems offer great opportunities for the numerical astrophysics community and also raise serious technical challenges. Many legacy codes need to be fully rewritten to benefit from exascale architectures. Performance portability is a key issue as well as the management of the huge amount of data generated by exascale simulations. In this paper we discuss the path we have chosen to exascale and the software engineering solution chosen to address performance portability in the long run and data management. A new radiation hydrodynamics code based on the Kokkos library is presented as well as test runs and preliminary performance analysis. The first target of this new code will be to study supernova ejecta. They are multidimensional fluids, structured on both small and large scales following the non-linear development of complex fluid instabilities during and after the explosion. They contain a wealth of information about stellar composition and the evolution of elements in the interstellar medium. Their study requires significant computing resources that can be met through the use of exascale machines and suitable software.
Methods and Challenges of Including Dust Evolution in Magnetohydrodynamic Simulations
Authors: Slavin Jonathan D.
Published: April 2024

Dust plays many important roles in the interstellar medium, affecting the heating, cooling and chemistry in all the different phases of the medium. Up until recently though, if dust was included in simulations at all, it was typically assumed that it was tightly coupled to the gas. More recent work has allowed for the dust to decouple from the gas, though this brings with it challenges for including all the dust destruction processes, especially grain-grain collisions. We discuss the various methods of simulating dust evolution in the context of magneto-hydrodynamical simulations of the interstellar medium and their strengths and weaknesses. In particular we present a method for improving on the tight coupling approach that shows promise for simulating the evolution of small grains subject to shocks.
Does Central Asian forest growth benefit from a warming-wetting climate? Insights from tree-ring records
Authors: Wang Ting , Bao Anming , Xu Wenqiang , Zheng Guoxiong , Du Weibing , Yu Tao , Huang Xiaoran , Gao Ziqian , Bao Jiayu , Stoffel Markus , Maeyer Philippe De , Wulf Alain De
Published: April 2024

Climate warming has pronounced impacts on high-elevation regions, including arid Central Asia, and has multiple impacts on the environment. Forests in these mountainous areas provide essential services by regulating regional climate, sequestering carbon, and supporting soil and water conservation. However, trends in forest productivity and their response to climate change remain unclear. To address this knowledge gap, we collected tree cores from five sample plots in the western Tianshan region. We used tree-ring widths to reconstruct net primary productivity (NPP) and investigated the sensitivity of forest NPP to climate change by analysing weather station data and employing LASSO regression to identify climatic factors influencing forest growth. Our results demonstrate that the reconstructed forest NPP effectively captured significant carbon shifts and revealed a non-significant increase in forest productivity associated with climate warming and higher precipitation between 1970 and 2020 at low and middle elevations in the Tianshan mountains. Humidity is the primary limiting factor affecting forest growth in this region. Conversely, the relationship between temperature and forest growth is not consistent as precipitation increases. Our findings suggest that continued warming will exacerbate water stress in forests.
Characterizing the performances of different observational precipitation products and their uncertainties over Africa
Authors: Ayugi Brian Odhiambo , Chung Eun-Sung , Babaousmail Hassen , Lim Kam Sian Kenny Thiam Choy
Published: April 2024

Validation of observed gridded precipitation datasets sourced from satellites or reanalysis over Africa remains a challenge due to the dearth of in-situ products that can act as a true estimate. To address this gap, this study compares the performance of different precipitation products (gauge, reanalysis, and satellite-based) sourced from the Frequent Rainfall Observations on GridS (FROGS) database over Africa. Satellite products are classified as corrected (incorporating gauge observations into their algorithms) or uncorrected, which implies that temporal variations depend entirely on the satellite. The main aim is to identify regions where precipitation products depict minimal uncertainties, supporting the use of the datasets in understanding precipitation variability in the specific regions. This is achieved by applying the triple collocation approach, which takes advantage of three collocated datasets of the same variable to derive the mean square error without requiring knowledge of the true value. The results show that light precipitation (1–5 mm d−1) was prevalent in most regions of Africa during the study duration (2001–2016). Estimating the spatial distribution of daily precipitation greater than the 90th percentiles suggests that extreme precipitation is mainly detected over the Central Africa region and coastal regions of West Africa, where the majority of uncorrected satellite products show consistent performance. The satellite product CMORPH_V1_RAW shows higher estimates of 90th percentile precipitation among the uncorrected satellite products. The ability of precipitation products to detect rainy or non-rainy days shows that corrected satellite products depict notable agreement for probability of detection and false alarm ratio over most regions of Africa. Overall, better performance is demonstrated by the IMERG products, ARCv2, CHIRPSv2 and PERSIANN_CDRv1r1 (corrected), and GPCC, CPC_v1.0 and REGEN_ALL (gauge) during the study period. Among the reanalysis products, ERA5 datasets shows good performance in estimating daily precipitation over Africa. The optimal maps that show the classification of products in regions where they depict reliable performance can be recommended for various usage by different stakeholders.
Divergent responses of subtropical evergreen and deciduous forest carbon cycles to the summer 2022 drought
Authors: Han Lei , Chen Yanan , Wang Ying , Sun Yuan , Ding Zhi , Zhang Hongsheng , Tang Xuguang
Published: April 2024

Ongoing shifts in climate, especially extreme drought, is posing considerable threats to the forest carbon uptake worldwide. In China, summer 2022 was the warmest and driest since the beginning of meteorological measurements. This study synthesized the tower-based carbon fluxes and climate data from two subtropical evergreen and deciduous forest ecosystems to investigate the effects of such summer drought. Interestingly, the net ecosystem production (NEP = −NEE) only exhibited a slight decrease at the deciduous forest while it even enhanced at the evergreen forest during the summer 2022 drought. Further analysis revealed that although reductions in gross primary productivity (GPP) and ecosystem respiration (R
e) were found at both sites, larger decrease in R
e than GPP at the evergreen forest led to stronger NEP compared to the previous year. However, the NEP of two forest ecosystems sharply reduced in the following 2023, which can be ascribed to the legacy effects of the summer 2022 drought. The results of multiple linear regression revealed that soil water content (SWC) was recognized as the primary driver of GPP and R
e, and downwelling shortwave radiation (R
g) regulated the variability of NEP during the summer 2022. Across these forest carbon fluxes including GPP, R
e and NEP, the deciduous forest exhibited larger resistance, whereas the evergreen forest showed stronger resilience. All analyses emphasized the diverse adaptive strategies among vegetation types, which acted an important role in assessing ecosystem carbon sequestration in face of future climate change.
Computational insights into the electronic structure of functionalized monolayer hBN supercapacitor electrodes
Authors: Sruthi T , Mathew Vincent
Published: April 2024

In this study, we examined a collection of functionalized hBN monolayers’ electronic structure and quantum capacitance. Different ad-atom adsorption techniques have been used to functionalize the hBN monolayer. Density functional theory calculations are performed to precisely determine the electrical structure of an ad-atom doped hBN monolayer with a range of doping concentrations. The quantum capacitance of every functionalized system was then calculated. Above 500 μF/cm2, a noticeable quantum capacitance has been seen. Our calculations demonstrate that ad-atom doping at hollow position of hBN honeycomb lattice greatly increases the quantum capacitance of hBN monolayer. The microscopic cause of this system’s increased quantum capacitance has been investigated. Our DFT-based calculation shows that the system has a very large quantum capacitance due to the creation of new electronic states in the vicinity of the band edge and the Fermi level shift caused by the ad-atom adsorption.
Simultaneous synthesis of carbon quantum dots and porous carbon using one step hydrothermal method for supercapacitor applications
Authors: Sharanappa Sapna , Vijaykumar S P , Suresh D S , Ganesha H , Ba Shbil Abdullah , Basappa M
Published: April 2024

The present work reports a one-step hydrothermal carbonization process used to simultaneously produce carbon quantum dots and porous carbon. The synthesized materials were characterized by using a Fourier transform infrared spectrometer (FT-IR) to study the chemical interactions, the optical properties were studied with the help of UV-visible absorption spectroscopy, and the morphology analysis of CQDs and porous carbon was examined using FESEM, in which spherical morphology confirms the formation of CQDs, while XRD confirms the amorphous nature of the porous carbon. The application of CQDs and porous materials is evaluated as a supercapacitor material by using the cyclic voltammetry technique. The specific capacitance of porous carbon was 161.33 Fg−1 at 0.01mVs−1 scan rate. This makes CQDs and porous carbon a promising candidate for applications in energy storage devices such as supercapacitors.
Performance analysis of A3B2X9 (Cs3Bi2I9) based perovskite photovoltaic tandem structure with crystalline Silicon (c-Si)
Authors: Goutham Kumar Shreyus , Surya Teja Reddy Tadi , Suraj N , Prashanth C R , Honnavar Gajanan V
Published: April 2024

The efficiency of solar cells with single-junction utilizing organic-inorganic hybrid perovskites have attained a value more than 25.5%. The device power conversion efficiency (PCE) can be improved further either by optimizing the absorber layer (Perovskite film) or by investigating the novel device structures such as tandem based solar cells with perovskite and silicon. This combination of top cell (Perovskite solar cell) and bottom cell (Silicon solar cell) can improve the PCE which surpasses the Shockley-Queisser limit of single-junction solar cells by utilizing a wider range of solar spectrum. This paper presents an optimization and simulation of standalone Cs3Bi2I9 perovskite solar cell which was later integrated with a c-Si solar cell to simulate a tandem structure using SCAPS-1D software. The aim is to investigate the performance enhancement of the perovskite solar cell by optimizing it and stacking it on top of a high-efficiency c-Si solar cell using a four-terminal (4T) structure. At a short-circuit current density of 16.165 mA/cm2 and an open-circuit voltage of 1.41 V, the simulation findings demonstrate that the Cs3Bi2I9 perovskite solar cell exhibits a high-power conversion efficiency of 20.37%. The tandem structure demonstrates an enhanced power conversion efficiency of 31.59% which is significantly higher than the efficiency of the individual cells. The Cs3Bi2I9 perovskite solar cell is a great choice for application in tandem systems with c-Si solar cells for high-efficiency, according to the simulation results. This study provides valuable insights for the development of efficient perovskite/c-Si tandem solar cells.
Deep Learning Neural Network (DNN) Coupled Laser Induced Breakdown Spectroscopy for Quantitative Analysis of Copper Alloys
Authors: Pauly Leya , Mary John Lekha , Suryan Sivadas M S , Anoop K K
Published: April 2024

Conventional Laser Induced Breakdown Spectroscopy (LIBS) quantitative analysis, employing both calibration and calibration-free techniques, is challenged by spectral overlapping, self-absorption, and spectral broadening effects, leading to decreased accuracy. Recently, integration of machine learning (ML) algorithms with LIBS has been increasingly employed to tackle these challenges. This article explores the augmentation of LIBS with deep learning neural networks (DNN) to enhance accuracy of quantitative analysis of multi-elemental copper alloys. Sufficient training data was acquired by simulating optical emission spectra for bronze (Cu-Sn) and admiralty brass (Cu-Zn-Sn) alloys under standard laser produced plasmas conditions, encompassing different alloy concentrations, electron temperatures, and densities. We designed a regularized DNN structure, fine-tuned using a validation dataset to optimize quantitative results. The model’s accuracy was assessed with test dataset. The quantitative results demonstrated reduced loss as training spectra increased from 500 to 5000 for both alloys. The decline in mean squared error, from 2.793×10−3 to 4.283×10−5 for bronze and from 3.245×10−2 to 5.598×10−4 for admiralty brass alloys, as training data increased from 500 to 5000, underscores the proposed DNN model’s potential for metallurgical alloy quantification.
Electrical nature and surface enhanced Raman spectroscopy of Ag nanoparticles decorated graphene Sheet
Authors: Sachidanand P S , Mohan Manu , Reshmi S , Viswan Gauthami , Bhattacharjee Kuntala
Published: April 2024

The interaction between graphene and silver nanoparticles (AgNPs) is investigated by studying the surface-enhanced Raman spectroscopy (SERS) that shows a large enhancement of Raman signal from the AgNPs coated graphene compared to the bare graphene. In addition, the electrical nature of the sample is examined using a two-probe probe station to acquire current-voltage (I-V) characteristics exhibiting a semi-conducting current behaviour of the AgNPs-graphene heterojunctions, in contrast to a semi-metallic electronic behaviour observed for the pristine graphene. We propose band gap engineering of graphene from semi-metallic to semi-conducting via breaking of A-B sub-lattice symmetry due to deposition of AgNPs which is believed to be associated by causing deformations at the regions where graphene is in close proximity with the AgNPs. Furthermore, there is also a possibility of doping induced by AgNPs to graphene and thereby, emergence of a bandgap due to tuning of the electronic structure. In this work, we report these investigations which possess profound implications for application of graphene as a semi-conducting material.
Nilgiri tree peels-derived activated porous carbon for high performance supercapacitor applications
Authors: Ba shbil Abdullah , Nagaraju Y S , Ganesha H , Veeresh S , Suresh D S , Vijaykumar S P , Sharanappa Sapna
Published: April 2024

In this study, the activated porous carbon (APC-x) was collected from Nilgiri tree peels, making it a sustainable and cost-effective green synthesis approach. The raw material undergoes carbonization and activating it using KOH as chemical activation agent. The chemical composition, crystallinity and morphology of the surface of ACP-x was examined by Fourier Transform infra-red (FTIR), X-ray diffraction and Field emission scanning electron microscope (FESEM). The cyclic voltammetry technique was used to measure the electrochemical performance of APC-x using PVA/KOH gel electrolyte in two-electrode system. The highest specific capacitance value 140 F/g was obtained for APC-0 at scan rate of 10 mV/s. The use of Nilgiri tree peels as precursor for activated porous carbon production presents a sustainable approach for developing high-performance supercapacitor materials.
Synthesis and Characterization of Barium Hexagonal Ferrite (BaFe12O19)
Authors: Naveen C. S , Rohidas Anand , Raghu S , Havareddi B. N , Ramanna R , Mattyappannavar S
Published: April 2024

Barium Ferrites is a good class of electrical material due to its high resistivity. It is mainly used in electronics and telecommunication industry due to their novel electrical properties. We have synthesized barium hexagonal ferrite (BaFe12O19) by solid state reaction method. The prepared precursor was calcined at temperature 750°C and characterized by FTIR, XRD and SEM. X-ray diffraction analysis confirmed the single-phase formation of BaFe12O19. Using the Debye Scherrer formula, the crystallite size of BaFe12O19 was calculated, and the average particle sizes were 26.52 nm, 27.17 nm, 19.17 nm and 23.67 nm. The result confirms that barium ferrite powder has a constant size of less than 30 nm. Four peaks at 2θ values of 35.52, 43.22, 57.21 and 62.84 degrees, corresponding to (110) (111) (210) and (211) planes of Barium ferrite have been observed. The FTIR spectra showed important signature absorption spectra of hexaferrite at 314 cm−1, 499 cm−1 & 628 cm−1. These peaks confirm the formation of barium hexaferrites. The SEM images of barium hexaferrite at 750°C showed large number of vertically grown barium hexaferrite nanorods with a grain size between 110 nm to 130 nm. Thus, barium ferrite is a good candidate in the use of electronics and communication sector.
Structural and Optical Properties of PMMA-MgO Nanocomposite Film
Authors: Shruthi K N , Ramaraja Varma V , Kumar Mohan , Sushma  , Sanjeev Ganesh
Published: April 2024

This study investigates the structural, morphological, and optical properties of poly(methyl methacrylate) (PMMA) films incorporated with magnesium oxide (MgO) nanoparticles. The PMMA-MgO polymer nanocomposite (PNC) films were fabricated via solution casting method using varying weight percentages (1-4 wt%) of MgO nanoparticles. X-ray diffraction analysis confirmed the integration of MgO nanofillers in the PMMA matrix. Fourier transform infrared spectroscopy revealed interactions between PMMA and MgO nanoparticles. Atomic force microscopy demonstrated increased surface roughness in PNC films with higher MgO loading. Optical characterization using UV-visible spectroscopy showed enhanced absorption in the UV region and a noticeable peak at 280 nm due to MgO nanoparticles. The refractive index of PMMA-MgO PNCs increased with rising MgO content while the optical bandgap marginally decreased. The study highlights the potential of PMMA-MgO PNC films for advanced optoelectronic applications requiring high optical transparency and tuneable refractive index.
Synthesis, Characterization, Dielectric, and Electrical Conductivity Studies of Zinc Oxide Nanoparticles
Authors: Sharma A , Kumar P , Mahapatra S P
Published: April 2024

A co-precipitation technique is used to synthesize of ZnO nanoparticles. The synthesized ZnO nanoparticles were characterized by UV-visible spectroscopy, scanning electron microscopy (SEM), high-resolution transmission electron microscopy (HR-TEM), photoluminescence, Fourier-Transform Infrared Spectroscopy (FTIR), and X-ray diffraction (XRD). The size of the nanoparticles was wide-ranging from 1 nm to 100 nm, corresponding to the HR-TEM analysis. The photoluminescence study of ZnO nanoparticle shows emission in the UV region. The particle dimension of ZnO nanoparticle has also been studied through XRD. Dielectric spectroscopy of synthesized ZnO NPs pellet has been studied at a wide frequency range 10−1 to 105 Hz. The capacitance and dielectric permittivity of ZnO nanoparticles drop continuously with frequency as dipoles have less time to align in the field. Dielectric permittivity of ZnO pellets increase up to 5 mm thickness and subsequently drop, perhaps due to raise in resistivity. The dielectric loss of ZnO pellet has been examined as a function of frequency. The electrical conductivity of ZnO nanoparticles rise exponentially with frequency. Based on the dielectric studies, the dielectric permittivity and electrical conductivity of ZnO are highly depending on thickness and frequency range. The percolation threshold of ZnO pellets has been found between 4-5 mm thicknesses.
Effect of aspect ratio on the nonlinear optical properties of ZnO nanorods
Authors: Ramya M , Satheesh Swathy , Sreedhar Sreelakshmi , Marydasan Jitha P , Kailasnath M
Published: April 2024

In this work, we synthesized ZnO nanorods with two different aspect ratios using the solution method. Morphological and crystalline structures were investigated using TEM and XRD analysis. Also investigated the effect of aspect ratio on the optical properties of ZnO nanorods. With an increasing aspect ratio of the nanorods, the absorption peak is red-shifted and near-band edge emission is suppressed. The nonlinear absorption is enhanced with the increasing aspect ratio of ZnO nanorods.
Green synthesis and characterization of Zirconium Oxide with antimicrobial activities
Authors: Veerabhadraswamy B N , Pradeep H K , Swaroop K , Manoj K M , Dhanush Nadigar M V , Akash Patel M P , Bhagya N P
Published: April 2024

Green synthesis methods have garnered considerable attention due to their eco-friendly and sustainable nature. In this study, we report a green synthesis approach to fabricate Zirconium Oxide nanoparticles (ZrO2 NPs) using cinnamon extract and citric acid as reducing and stabilizing agents. The green synthesis process was carried out through a sol-gel reaction method. The obtained ZrO2 NPs were characterized using various analytical techniques, including UV-Vis, XRD and FTIR. The characterization results confirmed the successful synthesis of crystalline ZrO2 NPs with a narrow size distribution and the presence of functional groups from cinnamon and citric acid on the NP surface. The synthesized ZrO2 NPs were further investigated for their antimicrobial properties against bacteria and fungi. The results demonstrated potent antibacterial and antifungal activities of the green-synthesized ZrO2 NPs. Overall; this study showcases a green and sustainable approach to synthesize ZrO2 NPs using cinnamon extract and citric acid as reducing and stabilizing agents. The resulting ZrO2 NPs exhibited promising antibacterial and antifungal properties, making them potential candidates for various biomedical and environmental applications, particularly in the development of novel antimicrobial agents and coatings.