Sikkim Manipal University

ABSTRACT This investigation focuses on the development of dual‐layer coating of AlN and TiAlN by Chemical vapor deposition (CVD) to improve surface protection for advanced engineering applications. The Aluminium nitride (AlN) interlayer was first deposited on p‐type c‐Si (100) substrates over a temperature range of 200°C to 800°C, followed by the TiAlN top layer. The effect of deposition temperature on the structural, morphological, mechanical, and electrochemical properties of the coatings was systematically investigated. Surface morphology and microstructure were examined using Field emission scanning electron microscope (FESEM), while phase composition was studied by X‐ray diffraction (XRD). Atomic force microscope (AFM) provided insights into nanoscale surface roughness, and Raman spectroscopy confirmed the vibrational modes of the coating phases. The X‐ray photoelectron spectroscopy (XPS) analysis confirmed the formation of strong Titanium‐Nitride (Ti─N), Aluminium nitride (Al─N), and Aluminium Titanium nitride (Al─Ti─N) bonds, indicating successful synthesis of a chemically stable dual‐layer structure. Nanoindentation revealed that the dual‐layer coating deposited at 700°C exhibited superior hardness and Young's modulus values of 29.64 and 209.69 GPa, respectively, indicating improved mechanical integrity. Electrochemical corrosion testing demonstrated excellent corrosion resistance, with the lowest corrosion current density observed for the coating formed at 700°C. Collectively, the results confirm that CVD‐deposited AlN/TiAlN dual‐layer coating offer a promising route for surface engineering applications that requires high hardness, better tribological property, and corrosion resistance in aggressive environments.

MXenes, a class of 2D nanomaterial comprising transition metal carbides and nitrides, a promising material pertains to be effective for energy storage as it exhibits characteristics like high conductivity, tuneable surface chemistry, and layered morphology. This study investigates the influence of hydrofluoric acid (HF) concentration during synthesis of MXenes from MAX phase. MXene synthesis was carried out under the controlled experimental variables like avoiding ultrasonic treatment and temperature variation, in order to examine the influence of etchant concentration. Correlating their morphological behaviour with electrochemical performance as MXene may be a suitable candidate for future energy storage materials. Among the yielded samples, 30

Purpose: This study explores Indian physical education (PE) teachers' understanding, perceptions, and implementation of physical literacy (PL), addressing a significant gap in Indian literature. Method: The quantitative data were collected via a survey of 493 PE teachers across India (159 females and 332 males). Qualitative data were collected through 20 semistructured interviews conducted via the Zoom platform. Quantitative data were analyzed using descriptive statistics, and chi-square or Fisher's exact tests were applied where appropriate. Qualitative data were examined through reflective thematic analysis. Results: Findings reveal widespread understanding of PL; however, significant conceptual confusion persists, particularly regarding PE teachers' perception of its distinction from PE and its implementation strategies. Thematic analysis revealed three themes: (a) clarifying the meaning of PL, (b) teachers' perspectives on PL, and (c) challenges in implementing PL. Conclusion: Indian PE teachers show enthusiasm for PL but often lack conceptual clarity, limiting its classroom implementation. Strengthened professional development and contextually relevant resources are essential.

Every country’s ability to grow economically primarily depends on its agricultural production. Rapid population expansion, frequent climate change, and resource shortages make it difficult to meet the food needs of the current population. Precision agriculture has rapidly evolved with the integration of deep learning (DL) techniques, addressing key challenges such as crop selection, yield prediction, disease detection, pest management, and weed control. This systematic review provides a comprehensive analysis of recent advances in DL applications for agriculture, highlighting methodologies, models, and their impact on farm efficiency and sustainability. By reviewing 117 studies, we evaluate how DL models, including convolution neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), Transformers, and other pre-trained models, are employed in tasks ranging from image-based disease identification to time series crop yield forecasting. Recent developments in large vision models (LVMs) and the rise of multi-models further enhance precision agriculture by automating complex tasks such as disease detection and crop health monitoring. These approaches address challenges like limited labeled data and dataset imbalances, providing scalable and reliable solutions. Despite their potential, DL models face challenges such as computational resource demands, generalization difficulties in diverse environments, and real-time scalability. Future research should prioritize hybrid DL models, integration with internet of things (IoT) systems, and transfer learning to overcome these limitations. Additionally, incorporating LVMs and multi-models with advanced data fusion techniques can further improve precision agriculture practices, enabling real-time decision-making, adaptability, and sustainability. This review offers insights into the current state and future directions of DL in precision agriculture, emphasizing its transformative potential in modern agriculture.

Solid-state hydrogen storage materials with tunable properties are crucial for developing sustainable energy systems. In this work, we have investigated the structural, electrical, thermal, and adsorption–desorption properties of scandium (Sc)- and hafnium (Hf)-decorated MoS2 monolayers using density functional theory (DFT) and molecular dynamics (MD) simulations to evaluate their potential as a hydrogen storing medium. Both dopants preferentially occupy the hollow sites with strong binding energy of 3.56 eV and 4.21 eV; however, the high diffusion energy barrier of Sc and Hf atoms suppresses the clustering effect. The positive phonon spectrum and stable MD results at 300, 500, and 700 K confirm the dynamic and thermal stability of the decorated systems. Sc and Hf decoration substantially modify the electronic property of MoS2 by introducing a new energy state near the Fermi level, enabling stronger interaction with hydrogen. Sc-MoS2 exhibits a semiconductor-to-metallic transition at low H2 coverage, whereas Hf-decorated MoS2 retains its semiconducting nature even after adsorption. The decorated systems can hold up to eight H2 molecules (Sc-MoS2) with adsorption energy ranging from −0.48 eV to −0.24 eV per H2 molecule, while Hf-MoS2 can bind up to seven molecules, having adsorption energy in the range of −0.84 eV to −0.28 eV/H2. Although the resulting gravimetric density is still below the DOE target, these adsorption energies are within the range for reversible hydrogen adsorption. MD simulations further verify the thermal stability of maximum H2-loaded systems performed at different temperatures. Overall, the results highlight the effectiveness of transition metal decoration in tailoring the hydrogen adsorption and desorption behaviour of MoS2, guiding the design of next-generation 2D hydrogen storage materials.