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"Sharma, Ashutosh"
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High Entropy Alloy Coatings and Technology
Recently, the materials research community has seen a great increase in the development of multicomponent alloys, known as high entropy alloys (HEAs) with extraordinary properties and applications. In surface protection and engineering, diverse applications of HEAs are also being counted to benefit from their attractive performances in various environments. Thermally sprayed HEA coatings have outperformed conventional coating materials and have accelerated further advancement in this field. Therefore, this review article overviews the initial developments and outcomes in the field of HEA coatings. The authors have also categorized these HEA coatings in metallic, ceramic, and composite HEA coatings and discussed various developments in each of the categories in detail. Various fabrication strategies, properties, and important applications of these HEAs are highlighted. Further, various issues and future possibilities in this area for coatings development are recommended.
Journal Article
High Energy electron and proton acceleration by circularly polarized laser pulse from near critical density hydrogen gas target
Relativistic electron rings hold the possibility of very high accelerating rates, and hopefully a relatively cheap and compact accelerator/collimator for ultrahigh energy proton source. In this work, we investigate the generation of helical shaped quasi-monoenergetic relativistic electron beam and high-energy proton beam from near critical density plasmas driven by petawatt-circularly polarized-short laser pulses. We numerically observe the efficient proton acceleration from magnetic vortex acceleration mechanism by using the three dimensional particle-in-cell simulations; proton beam with peak energy 350 MeV, charge ~10nC and conversion efficiency more than 6% (which implies 2.4 J proton beam out of the 40 J incident laser energy) is reported. We detailed the microphysics involved in the ion acceleration mechanism, which requires investigating the role of self-generated plasma electric and magnetic fields. The concept of efficient generation of quasi-monoenergetic electron and proton beam from near critical density gas targets may be verified experimentally at advanced high power – high repetition rate laser facilities e.g. ELI-ALPS. Such study should be an important step towards the development of high quality electron and proton beam.
Journal Article
Editorial on the Special Issue “Fluorescence Imaging and Analysis of Cellular Systems”
Fluorescence imaging has indeed become a cornerstone in modern cell biology due to its ability to offer highly sensitive, specific, and real-time visualization of cellular structures and dynamic processes [...]
Journal Article
A Blockchain Framework for Securing Connected and Autonomous Vehicles
Recently, connected vehicles (CV) are becoming a promising research area leading to the concept of CV as a Service (CVaaS). With the increase of connected vehicles and an exponential growth in the field of online cab booking services, new requirements such as secure, seamless and robust information exchange among vehicles of vehicular networks are emerging. In this context, the original concept of vehicular networks is being transformed into a new concept known as connected and autonomous vehicles. Autonomous vehicular use yields a better experience and helps in reducing congestion by allowing current information to be obtained by the vehicles instantly. However, malicious users in the internet of vehicles may mislead the whole communication where intruders may compromise smart devices with the purpose of executing a malicious ploy. In order to prevent these issues, a blockchain technique is considered the best technique that provides secrecy and protection to the control system in real time conditions. In this paper, the issue of security in smart sensors of connected vehicles that can be compromised by expert intruders is addressed by proposing a blockchain framework. This study has further identified and validated the proposed mechanism based on various security criteria, such as fake requests of the user, compromise of smart devices, probabilistic authentication scenarios and alteration in stored user’s ratings. The results have been analyzed against some existing approach and validated with improved simulated results that offer 79% success rate over the above-mentioned issues.
Journal Article
Regimes during liquid drop impact on a liquid pool
Water drops falling on a deep pool can either coalesce to form a vortex ring or splash, depending on the impact conditions. The transition between coalescence and splashing proceeds via a number of intermediate steps, such as thick and thin jet formation and gas-bubble entrapment. We perform simulations to determine the conditions under which bubble entrapment and jet formation occur. A regime map is established for Weber numbers ranging from 50 to 300 and Froude numbers from 25 to 600. Vortex ring formation is seen for all of the regimes; it is greater for the coalescence regime and less in the case of the thin jet regime.
Journal Article
Vacuum brazing of Al2O3 and 3D printed Ti6Al4V lap-joints using high entropy driven AlZnCuFeSi filler
In this work, we studied the brazing characteristics of Al
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and 3D printed Ti–6Al–4V alloys using a novel equiatomic AlZnCuFeSi high entropy alloy filler (HEAF). The HEAF was prepared by mechanical alloying of the constituent powder and spark plasma sintering (SPS) approach. The filler microstructure, wettability and melting point were investigated. The mechanical and joint strength properties were also evaluated. The results showed that the developed AlZnCuFeSi HEAF consists of a dual phase (Cu–Zn, face-centered cubic (FCC)) and Al–Fe–Si rich (base centered cubic, BCC) phases. The phase structure of the (Cu–Al + Ti–Fe–Si)/solid solution promises a robust joint between Al
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and Ti–6Al–4V. In addition, the joint interfacial reaction was found to be modulated by the brazing temperature and time because of the altered activity of Ti and Zn. The optimum shear strength reached 84 MPa when the joint was brazed at 1050 °C for 60 s. The results can be promising for the integration of completely different materials using the entropy driven fillers developed in this study.
Journal Article
Deep Learning‐Based Approach for Enhancing Streamflow Prediction in Watersheds With Aggregated and Intermittent Observations
Accurate daily streamflow estimates are crucial for water resources management. Yet, many regions lack high‐temporal‐resolution data due to limited monitoring infrastructure, often relying on monthly aggregates or intermittent observations. Predicting streamflow in these sparsely sampled watersheds remains challenging. This study proposes a deep learning‐based approach using Long Short‐Term Memory, leveraging its inherent advantages in learning long‐term dependencies within hydrological variables and processes to enhance streamflow predictions in sparsely sampled watersheds. The approach was evaluated for simulating daily flow patterns from monthly aggregated and monthly or weekly intermittent observations in two contrasting hydrological settings: near‐natural and human‐influenced watersheds. Results showed that the proposed approach reliably predicts daily flows from monthly aggregates with a median Nash‐Sutcliffe efficiency (NSE) of 0.61 for near‐natural and 0.48 for human‐influenced watersheds. The proposed approach performed even better for daily flow predictions from monthly or weekly intermittent observation, achieving a median NSE of 0.70 and 0.55 for near‐natural and human‐influenced watersheds, respectively. The proposed approach remained robust across different seasons and hydrological regimes, with a median percentage bias of ±5%, except in arid regions. Moreover, data sensitivity analysis indicated that data from wet seasons were crucial for improving model predictions and that weekly data could yield results comparable to daily observations. Overall, this study demonstrates that the deep learning‐based approach offers a robust and accurate representation of daily streamflow patterns from aggregated or intermittent observations, providing valuable hydrological insights and promising solutions for improving water resource management in regions with limited monitoring infrastructures.
Journal Article
Integrating Reservoir Dynamics Into Differentiable Process‐Based Hydrological Model for Enhanced Streamflow Estimation
Reliable hydrological predictions are crucial for water allocation, reservoir operations, and flood control. While data‐driven models like Long Short‐Term Memory (LSTM) offer high accuracy, they lack physical interpretability and cannot estimate internal hydrological states or reservoir‐specific variables. To overcome these limitations, we propose a differentiable framework, dPLHBVRes, which integrates a simplified process‐based model (HBV) enhanced with a reservoir module and neural network‐based parameterization. This approach improves streamflow prediction while enabling the estimation of untrained hydrological variables, including evapotranspiration, soil moisture, reservoir storage, and outflows. For 38 regulated catchments in Peninsular India, dPLHBVRes achieved streamflow accuracy comparable to LSTM (median NSE 0.66 vs. 0.67), but with the added advantage of enabling investigation of internal states. Furthermore, in the absence of observed reservoir‐specific inputs, incorporating remote sensing‐based observations, such as water spread area, into dPLHBVRes further enhanced model performance. For instance, in the Rengali catchment, this additional input improved streamflow prediction from NSE 0.65 to 0.74 while also enhancing simulations of untrained variables: evapotranspiration (bias reduced from 0.46 to 0.39 mm), soil moisture (bias reduced from −21.42 to −7.34 mm), reservoir outflows (NSE from 0.42 to 0.49), and reservoir storage (correlation from 0.37 to 0.74). Overall, dPLHBVRes offers a practical and interpretable alternative to black‐box models for regulated catchments, providing accurate streamflow predictions while maintaining physical interpretability through access to internal untrained hydrological variables.
Journal Article
A Compact Ultra-Wideband Millimeter-Wave Four-Port Multiple-Input Multiple-Output Antenna for 5G Internet of Things Applications
This paper presents a compact design for a four-element multiple-input multiple-output (MIMO) antenna for millimeter-wave (mmWave) communications covering the bands of n257/n258/n261. The MIMO design covers the frequency range of 24.25–29.5 GHz, with a wide bandwidth of 5.25 GHz. The element of the MIMO antenna structure uses a single circular patch with an inset feed, and, in order to improve the reflection coefficient (S11), a half-disk parasitic patch is positioned on top of the circular patch. Moreover, to fine-tune the antenna’s characteristics, two vertical stubs on the extreme ends of the ground plane are introduced. For this design, a Rogers RT/Duroid 5880 substrate with ultra-thin thickness is used. After the optimization of the design, the four-port MIMO antenna attained a tiny size, with the dimensions 16.2 mm × 16.2 mm × 0.254 mm. In terms of the MIMO parameters, the ECC (Envelop Correlation coefficient) is less than 0.002 and the DG (Diversity Gain) is greater than 9.99 dB in the mentioned band, which are within the tolerance limits. Also, in spite of the very small size and the four-port configuration, the achieved isolation between the neighboring MIMO elements is less than −23.5 dB.
Journal Article