Journal of Engineering in Industrial Research

Abstract This review presents a timely and comprehensive synthesis of nano-engineered quantum dots and low-dimensional semiconductors for optoelectronic applications, uniquely integrating recent breakthrough developments from 2023 to 2025 with practical device implementation strategies. Unlike previous reviews, this work bridges fundamental physics, advanced materials engineering, and systems-level integration perspectives, while critically addressing sustainability and scalability challenges often overlooked in existing literature. The burgeoning field of nano-engineered quantum dots (QDs) and low-dimensional semiconductors has emerged as a transformative technology platform for next-generation optoelectronic applications. This comprehensive review examines the latest advances in materials synthesis, fundamental mechanisms, and device integration strategies that have revolutionised the performance of light-emitting diodes, solar cells, photodetectors, and quantum photonic devices. A critical analysis of size-dependent quantum confinement effects, surface engineering approaches, and heterostructure design principles is porposed that enables precise control over optical and electronic properties. The review encompasses colloidal QDs, epitaxially grown nanostructures, two-dimensional materials, and hybrid organic-inorganic systems, highlighting breakthrough achievements in efficiency, stability, and spectral tunability. Particular emphasis is placed on emerging applications in flexible electronics, bioimaging, quantum information processing, and smart sensor networks for IoT and smart city infrastructures. The integration pathways are investigated for quantum dots in next-generation photovoltaic architectures and coupling strategies with advanced energy storage systems. Key challenges are identified in scalable synthesis, long-term stability, and environmental impact while outlining promising research directions for the next decade. This work provides researchers and engineers with a comprehensive roadmap for leveraging nano-engineered quantum systems in practical optoelectronic technologies.

Abstract This study investigates multiannual variations in land surface temperature (LST) across four representative permafrost regions in northern Alaska—Prudhoe Bay, Barrow, the Brooks Range, and the Yukon Flats—to identify short-term surface warming trends. MODIS Terra MOD11A2 V6.1 data were processed using the Google Earth Engine (GEE) platform to compute annual mean LST values for the period 2020–2025. Warming rates (°C/year) were quantified through linear regression analysis. The results reveal a consistent regional warming pattern, with positive temperature trends observed at all sites. Among the studied regions, the Yukon Flats exhibited the most pronounced warming, reaching positive LST values of approximately +1 °C by 2025. Gradual warming was also detected in the coastal regions of Barrow (approximately −8 °C in 2025) and Prudhoe Bay (approximately −6 °C in 2025). These findings demonstrate that the integration of MODIS data with GEE provides an accurate and efficient framework for monitoring regional permafrost thermal dynamics and underscores the importance of remote sensing in evaluating climate change impacts on ground stability in Alaska.

Abstract Aerospace structural health monitoring (SHM) has evolved significantly with the integration of artificial intelligence (AI) technologies, transforming traditional maintenance paradigms from reactive to predictive approaches. The complexity of modern aerospace structures necessitates advanced monitoring systems capable of real-time damage detection, prognosis, and decision-making. This systematic review examines the current state-of-the-art applications of AI in aerospace SHM, evaluates existing methodologies, identifies research gaps, and proposes future directions for enhanced structural integrity assessment. A comprehensive literature search was conducted across multiple databases (Web of Science, Scopus, IEEE Xplore, and PubMed) covering publications from 2018 to 2024. Studies were selected based on predefined inclusion criteria focusing on AI applications in aerospace SHM, including machine learning, deep learning, and hybrid approaches. The review analyzed 127 relevant publications, revealing significant advancements in AI-driven SHM technologies. Machine learning algorithms demonstrated 85-95% accuracy in damage detection, while deep learning approaches achieved up to 98% accuracy in complex pattern recognition tasks. Hybrid AI systems showed superior performance in real-time monitoring applications with reduced false alarm rates. The integration of AI in aerospace SHM has shown tremendous potential for improving safety, reducing maintenance costs, and extending aircraft service life. However, challenges remain in data standardization, model interpretability, and regulatory compliance. Future research should focus on developing explainable AI models, enhancing edge computing capabilities, and establishing industry-wide standards.

Abstract The semiconductor manufacturing industry represents one of the most complex and globally integrated supply chains in modern manufacturing. This review examines the evolution of semiconductor supply chains from basic silicon processing to sophisticated global manufacturing networks serving diverse markets through a systematic literature review methodology. Industry reports were analysed from SEMI and Semiconductor Industry Association, peer-reviewed academic publications, and financial data from leading semiconductor companies covering the period 2020-2024, with particular emphasis on post-pandemic supply chain developments. The analysis employs a multi-dimensional framework examining structural characteristics, dynamic trends, strategic responses, and risk factors. Key quantitative findings include: global wafer fabrication capacity reached 42 million wafers quarterly in Q4 2024; silicon wafer shipments declined 2% to 12,174 million square inches in 2024, with a projected 10% recovery to 13,328 million square inches in 2025; and the semiconductor supply chain is projected to reach $600 billion by 2024, driven by artificial intelligence demand. Geographic analysis reveals that Taiwan, South Korea, and China account for 75% of global capacity, with advanced manufacturing nodes under 7 nm showing even higher concentration. The industry demonstrated operational resilience in 2024 despite disruptions, including 40% reduction in Suez Canal traffic and geopolitical tensions. However, emerging challenges, including AI-driven demand shifts, geopolitical fragmentation, and sustainability requirements, are driving fundamental restructuring from efficiency-optimised models toward balanced approaches incorporating resilience and strategic autonomy. The review synthesises developments in manufacturing capacity, regional diversification efforts, and technological advances reshaping global semiconductor supply networks. Findings have significant implications for industry stakeholders pursuing strategic diversification, policymakers implementing regional capacity initiatives, and researchers developing semiconductor-specific supply chain frameworks.

Abstract Wastewater that is polluted with synthetic dyes presents a major environmental problem and treatment by conventional methods usually does not lead to full elimination of the dyes. The industry desperately looks for eco-friendly solutions that involve the use of photocatalysts that can efficiently remove toxic organic dyes from water. In recent years, polymer nanocomposites have become a significant focus in both academic and industrial fields. Nanoparticles are materials that are smaller than 100 nanometers in size. Adding a small amount of nanoparticles to polymers can give the composite materials new properties. This research describes how to make titanium oxide (TiO₂) nanoparticles using the sol-gel method. The nanoparticles were characterized using FTIR, X-ray diffraction (XRD), and UV-Visible spectroscopy. From the UV-Visible results and using a Tauc plot, the band gap energy of polyaniline/TiO2 nanoparticles was calculated. The X-ray diffraction data also showed the size of the crystalline particles. The UV radiation caused a total of 98% ortho nitroaniline orange dye degradation under the action of the Mg²⁺-doped PANI/TiO₂ nanocomposite, which had a 3.0 eV band gap and 38.7 nm crystalline size. The isotherm and kinetics studies verified the adsorption to be effective and the photocatalytic activity to be rapid. This indicates that the composite can be used to develop an eco-friendly dye remediation process for water treatment. Future research needs to address the photocatalytic activity of Mg²⁺-doped TiO₂ nanocomposites associated with visible light and real industrial wastewater situations.

Abstract Dye-sensitized solar cells (DSSCs) have emerged as a promising renewable energy technology over the past two decades; yet, their widespread application is hindered by issues of efficiency and stability. As the primary electron transport medium, the photoanode is a critical component that dictates the overall power conversion efficiency (PCE) of a DSSC. To overcome the limitations of conventional titanium dioxide (TiO₂​) photoanodes, researchers have explored incorporating various nanostructures. This review highlights recent advancements in using iron oxide-graphene-TiO₂​ nanocomposites as a multifunctional photoanode materials. It explores the synergistic effects among the components: iron oxide extends the photoanode’s light absorption into the visible spectrum, graphene enhances electron transport, and TiO₂ provides structural integrity and primary photoactivity. Various synthesis and characterization techniques are reviewed, which were used to create these composites and discuss their direct impact on the composites’ properties and performance. Despite significant progress, challenges remain in optimizing synthesis strategies, improving scalability, and ensuring long-term stability. Ultimately, this review demonstrates the immense potential of iron oxide-graphene-TiO₂​ nanocomposites for creating high-efficiency DSSCs, highlighting both their promise and the key challenges that must be addressed for future development.

Abstract This study evaluated the performance of a multipurpose wet sieving machine developed for processing maize slurry, cassava, and coconut milk. Wet sieving, a critical operation in food and agricultural industries, enables separating materials based on particle size. The machine was tested at operational speeds of 4.75 m/s and 5.76 m/s to assess throughput capacity, sieving efficiency, and overall performance. Results showed that increasing speed significantly improved throughput and efficiency, with cassava slurry exhibiting the most pronounced relative gain. At higher speeds, the machine achieved efficiencies of 98% for maize, 93% for cassava, and 94% for coconut. Mechanical wet sieving also reduced operator strain compared to manual methods. However, challenges such as higher energy demand and occasional sieve clogging were identified, emphasizing the need for design refinements and optimized operating parameters. The findings demonstrate the machine’s potential to enhance agricultural processing by reducing manual labor, minimizing product losses, and improving product quality. Future work should focus on the effects of sieve size, slurry concentration, and speed control to establish optimal conditions and facilitate large-scale applications in food processing industries.