Metallurgical and Materials Engineering (E-Journal)
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Electrochemical Synthesis of Metal Nanoparticles for Catalytic Applications
Get PDFAlternative catalysts emerged through metal nanoparticles (MNPs) which obtain their effectiveness from their distinctive electronic properties and optical functions and surface characteristics. Synthesizing nanoparticles through electrochemical approaches stands as an ideal synthesis method since these techniques enable thorough control over nanoparticle size distribution and composition shapes. The research investigates electrochemical MNP synthesis while evaluating major synthesis variables which include electrolyte solutions alongside voltage inputs together with electrode selection. The research evaluates catalytic behavior of made nanoparticles throughout reactions involving H2 production and O2 reduction combined with organic transformation
Analysis of Factors Influencing Students' Academic Challenges and their Impact on Outcomes
Get PDFThe primary goal of educational institutions is to offer a favorable learning environment and impart valuable knowledge to their students. The student’s achievement relies on academic performance and it is affected by the psychological problems encountered during the studies. In this paper, we conducted an analysis of the psychological issues that characterize students' experiences, including heavy workloads, insufficient or excessive sleep, mental stress, depression, feelings of pressure, diversity-related issues, negative emotions, and other study-related problems. We then performed a classification to determine the level of stress experienced by the students and examined its impact on academic performance. The dataset used in current work is collected using research methodology, and performed preprocessing. A model is developed to classify/predict the stress level. Classification and prediction techniques employed are Random Forest, K-nearest neighbors, Naïve Bayes, SVM, and ANN. We compared the performance using the metrics accuracy, precision, and recall. According to our extensive experiments, the accuracy of the Random Forest model is 98.16%, which demonstrates superior performance compared to Naïve Bayes (96.78%) and k-NN (95.41%). The ANN model accuracy is 98.16%. The Random Forest model performance is best as compared to other. Statistical method used to find the impact of students’ stress level on academic achievement
Fusion of Opportunistic Networks with Machine Learning: Present and Future
Get PDFOpportunistic Networks (OppNets) are mobile ad hoc networks characterized by intermittent connectivity and the lack of a guaranteed end-to-end path between source and destination. Nodes in an OppNet employ a store-carry-forward strategy – messages are stored and carried by mobile nodes until a communication opportunity arises, at which point they are forwarded. This paradigm enables data delivery in challenging environments (disaster areas, remote regions, etc.) where conventional infrastructure is absent, but it also introduces high delays and uncertainty. Machine Learning (ML) has emerged as a powerful tool to improve OppNet performance by exploiting patterns in node mobility, contact frequency, and context. This paper provides an extensive survey of the state-of-the-art in merging ML with OppNets and discusses future developments. In this paper, we analyze how ML algorithms have enhanced message delivery rates, reduced delays, and improved decision-making in OppNets (often outperforming traditional protocols by significant margins), as illustrated by recent results in the literature. Key challenges at this fusion include data sparsity, computational constraints on mobile devices, privacy/security concerns, and the need for realistic testing
Deep Learning Approaches for Autonomous Driving a Comprehensive Survey
Get PDFInvestments into autonomous driving have created a revolutionary technology which is changing the way people traverse through space. The paper summarizes modern deep learning methods used in autonomous vehicles by exploring fundamental elements which include sensing objects and segmentation and path planning as well as sensor unification. We review multiple deep learning structures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs) and transformers which find practical use in modern driving operations. We examine the deployment difficulties of DL-based autonomous systems which include difficulties in generalization and safety concerns as well as interpretability issues. The conclusion introduces potential advancements and new research paths which aim to boost the reliability together with robustness of autonomous driving systems that use deep learning techniques
Interfacial Design of Advanced 2D Nanomaterials for Sustainable Electrochemical Energy Storage
Get PDFBackground: Advanced 2D nanomaterials are of great interest in electrochemical energy storage as they exhibit outstanding conductivity, high surface area, and tunable interfacial properties. Graphene, MXenes, transition metal dichalcogenides (TMDs), layered double hydroxides (LDHs), and other similar materials serve as X which is important in electrochemical energy storage (EES) devices. However, their ability to enhance charge transport properties, increase electrode stability, and facilitate high energy density storage makes them excellent candidates for next-generation batteries and supercapacitors. Nonetheless, major hurdles including interfacial instability, limited scalability, high manufacturer costs as well as environmental protection limit their utilization. Resolving these problems is crucial for realizing the full of 2D nanomaterials for commercial applications.
Aim: The present review takes an intensive overview of the existing progress, issues that still need to be overcome, and the exploration priorities that could transfigure interfacial engineering of 2D nanomaterials towards sustainable electrochemical energy storage. Understanding the effectiveness of various nanomaterials in nanocomposite storage devices relies on knowledge of their interfacial cross-correlation and their role in energy storage capacity; therefore, this study compiles the most widely used nanomaterials, their interfacial properties, energy storage performance, and identifies critical gaps in the research that need to be overcome to make nanocomposite storage devices more ubiquitous.
Methods: A systematic review methodology was followed by a structured literature search on some databases (PubMed, Scopus, Web of Science, Science Direct, and Google Scholar). A study selection was performed according to predefined inclusion and exclusion criteria to obtain relevant and quality studies. Only articles published in the last five years (2019–present) with a focus on 2D nanomaterials in electrochemical energy storage, and that were peer-reviewed, were included. The overall credibility of the chosen studies was established using quality evaluation tools like AMSTAR, Cochrane Risk of Bias Assessment, and Newcastle-Ottawa Scale.
Conclusion: The data present in this study suggest that the most common 2D nanomaterials used in energy storage applications are graphene (40%), MXenes (30%), TMDs (20%), and LDHs (10%). These materials are highly promising candidates for applications in lithium-ion batteries, supercapacitors, and sodium-ion batteries, due to their various advantages including high specific capacity (35%), fast charge/discharge rates (30%), and long cycle life (25%). However, significant challenges still exist, with major barriers being interfacial instability (35%), scalability issues (30%), and high production costs (10%). Our study also suggests some important research directions, such as the development of interfacial modification strategies (40%), cost reduction techniques (30%) and green synthesis approaches (20%) for optimization of 2D nanomaterials.
Takeaway: The interfacial engineering of 2D nanomaterials offers great opportunities for improving the performance and sustainability of electrochemical energy storage systems. Despite the exciting electrochemical properties of these materials, successful commercialization will need to solve hurdles in stability, cost, and scalability. Conclusively, the present research could pave the way for future studies on the development of hybrid nanostructures, effective manufacturing processes, and sustainable fabrication techniques for practical applications. This work illuminates both the promise and challenge of 2D nanomaterials and guides future energy storage LI-Ion initiatives
The Role of IoT and Cybersecurity in Sustainable Mining and Materials Processing: A Pathway to Climate Change Mitigation
Get PDFThe study examined the role of the Internet of Things (IoT) and cybersecurity in sustainable mining and materials processing as a pathway to climate change mitigation. IoT-enabled technologies we re identified as essential tools for enhancing operational efficiency, optimizing resource utilization, and minimizing environmental degradation through real-time monitoring and predictive maintenance. Cybersecurity was emphasized as a critical factor in protecting industrial infrastructure from cyber threats, ensuring the stability and reliability of digital systems. Sustainable mining and materials processing require the adoption of intelligent and secure systems that support data-driven decision-making and regulatory compliance. The discussion highlighted implementation challenges, the need for strengthened cybersecurity measures, and the importance of policy support in fostering sustainable practices. The study contributed to knowledge by identifying challenges associated with technological adoption, highlighting the necessity for secure digital transformation, and proposing strategies for achieving climate change mitigation through smart and sustainable practices. The future of sustainable mining and materials processing depends on continuous investment in IoT advancements and robust cybersecurity frameworks. Policymakers and industry stakeholders should collaborate to develop regulatory policies that promote secure and environmentally responsible technological integration. Encouraging research, enhancing digital infrastructure, and strengthening cybersecurity strategies will be vital in ensuring a resilient and sustainable mining industry
Surface Engineering of MXene-Based Materials for Next-Generation Rechargeable Batteries
Get PDFNext-generation rechargeable batteries are being developed to address challenges such as low cost, high stability, high energy density, and safe energy storage materials. MXene-based mate-rials have attracted wide attention due to their unique properties, large surface area, high elec-trical conductivity, and easy dispersion in solvents compared to graphene. MXene derived from carbide and nitrides of transition metals (Ti3C2TX) have unique properties compared to other two-dimensional materials (2D) for use in rechargeable batteries. MXene electrodes delivered excellent performance and cyclic stability in various rechargeable secondary batteries. This re-view highlights the role of MXene in next-generation rechargeable batteries of lithium ion bat-teries (LIBs), lithium sulfur batteries (LISBs), sodium ion batteries (SIBs), zinc ion batteries (ZIBs), aluminium ion batteries (AlIBs), potassium ion batteries (PIBs) and magnesium ion bat-teries (MIBs). Moreover, in this review, we discussed the current research developments to im-prove the efficiency of energy storage devices and present the future research direction to im-prove the scalability, stability, and overall performance of MXene-coated electrodes in recharge-able batteries to overcome the energy storage challenges. 
Generative AI-Powered Framework for Audio Analysis and Conversational Exploration
Get PDFThis paper introduces a hybrid deep learning system for complex audio interpretation and post time communication utilizing associated hidden Convolutional Neural Networks (CNNs) with transformer based Large Language Models (LLMs) over spectrogram. The system inputs raw audio input in the form of audio signals, and maps them into spectrograms, extracts high level features using CNNs, and asks for fusion of LLM-produced embeddings with it, for adding semantic understanding, and contextual discussions. The multimodal attention technique helps in crossing the audio-linguistic gap and therefore, it is possible that they can have meaningful and context-aware response. The release offers the apps for intelligent assistant, education, intelligent monitoring, and other. Github repository, experimental evaluation presents increase in performance over the state-of-the-art in both experiments, with accuracy at 93.8%, latency at 420 ms and high semantic coherence (BLEU score of 0.74 is obtained). This result proves that the proposed system is usable to offer both user-friendly and intelligent audio exploration
Navigating the AI Landscape in Talent Acquisition: Examining Managerial Awareness and Perceived Talent Management Impact
Get PDFThis study investigates the awareness, adoption factors, and perceived impact of Artificial Intelligence (AI) within the talent acquisition (TA) process among Human Resource (HR) and Talent Acquisition managers. Amidst an evolving hiring landscape characterized by competition for skilled labor, AI has emerged as a transformative force in TA. This research employs a deductive and descriptive approach, utilizing a self-administered questionnaire distributed to 280 HR and TA professionals, complemented by a comprehensive literature review and semi-structured interviews. The quantitative data, collected from 116 valid responses, was analyzed using descriptive statistics, Chi-Square tests, and One-Way ANOVA to address three key research questions: the level of AI awareness, the factors influencing AI adoption and usage, and the perceived impact of AI on broader talent management practices. Descriptive analysis revealed a general awareness of AI tools among respondents. However, Chi-Square test results indicated no statistically significant relationship between AI training and actual AI usage in TA. Furthermore, the One-Way ANOVA demonstrated a statistically significant difference in perceived AI impact scores across varying frequencies of AI usage in different HR domains (Retention, Learning, Performance, and Potential). These findings provide empirical insights into the current state of AI integration in TA from the perspective of HR and TA managers, highlighting the nuances of awareness, adoption drivers, and perceived consequences for talent management
The Role of Microstructure in the Fracture Toughness of Advanced Ceramics
Get PDFThe superior mechanical alongside thermal alongside chemical characteristics of advanced ceramics make them suitable for demanding applications. At present their natural brittleness stands as the main restriction to their use. This research examines the mechanisms of enhanced toughening through microstructural design by reviewing published works and reviewing experimental outcomes. The research demonstrates the need for exact microstructural manipulations when developing ceramic materials for modern engineering needs