Journal of Engineering in Industrial Research

Abstract The successful synthesis of undoped and doped CrTe was achieved through electrochemical deposition. The film exhibited a 2θ value of 46.51o and a hexagonal phase with orientations (111), (112), (121), and (200). The CrTe image exhibits a surface with compacted particles, indicating a favorable shaving surface. The absorption of photons is evident on the surface of the substrate with a well-packed grain size. The surface micrograph of the CrTe precursor was altererd by the addition of zirconium. The doped films exhibited a stone-like micro-grain in their surface morphology. As the dopant concentration increased, specific grains showed an enlargement and thickening of the stone-like nano-grains. The doped CrTe material successfully achieved a uniform deposition of nanoparticles across the entire solar substrate. There was a decrease in the material's thickness from 104.02 to 103.23 nm, accompanied by an increase in film resistivity from 5.49 to 6.03Ω.m. As a result, conductivity decreased from 1.82 to 1.65 S/m. The undoped exhibited an energy bandgap of 1.62 eV. The molar concentration of zirconium has an inverse relationship with the energy bandgap of doped CrTe, with the range decreasing from 1.68 eV at 0.1 mol, 1.42 eV 0.2 mol, and 1.41 eV 0.3 mol. Incorporating zirconium into the crystal lattice of chromium telluride (CrTe) introduces novel and distinct physical properties, primarily by modifying its optical and electrical characteristics. The key novelty is demonstrating a non-linear relationship between Zr concentration and surface morphology. The study identifies that 0.02 mol of Zr is the ideal amount for achieving superior dispersion and the finest grain structure. It also provides a clear mechanistic explanation for why a higher concentration (0.03 mol) fails, due to particle agglomeration.

Abstract Photocatalysis is a fast-advancing pillar of green chemistry with strong promise for sustainable, energy-efficient environmental remediation. Drawing on studies from 2018–2024, this review surveys progress, challenges, and future directions in semiconductor photocatalysts—emphasizing titanium dioxide (TiO₂) and its modified forms alongside g-C₃N₄ composites and high-entropy oxides—which are valued for low energy demand, operational simplicity, and effective degradation of persistent organic pollutants. Recent design strategies—metal/non-metal doping, heterojunction engineering, and composite construction—have expanded visible-light response, improved light harvesting, and suppressed charge recombination, yielding degradation efficiencies of ~94–96% for model dyes such as methylene blue and methyl orange and delivering ~15–25% gains in quantum efficiency versus conventional systems. Key practical barriers remain, including carrier recombination, photostability over long use, and limits in solar utilization, but converging advances in material architecture and reactor engineering are steadily translating lab performance into scalable, eco-friendly, and cost-effective technologies for industrial wastewater treatment and sustainable chemical manufacturing—pointing to a clear path toward cleaner chemistry.

Abstract In light of growing global challenges like climate change, population growth, environmental pollution, and inefficient use and depletion of natural resources, it is essential for countries to adopt technologies and approaches that promote environmentally responsible economic activity. These measures aim to reduce environmental harm and preserve natural resources for future generations. Sustainable development, which prioritizes minimal environmental damage, relies on comprehensive and all-encompassing policies. These policies, both international and national, recognize the long-term needs of humanity and emphasize balancing economic growth with ecological preservation. A key component of these policies is the employment of green technologies, which are designed to minimize environmental impact. Countries that adopt green technologies are better positioned to mitigate the effects of climate change, reduce pollution, and ensure the sustainability of resources, which is crucial for the well-being of future generations. Through policy support, investment in innovation, and collaboration at the global level, green technologies can facilitate the transition to more sustainable economic models.

Abstract This article presents an overview of multibit ferroelectric memory utilizing hafnium oxide (HfO₂)-based ferroelectric field-effect transistors (FeFETs) within the MirrorBit architecture. As the demand for high-density, non-volatile memory solutions increases, HfO₂'s ferroelectric properties emerge as a promising candidate due to its compatibility with existing CMOS technology and its ability to retain data at lower power consumption levels. The MirrorBit architecture, which allows for multiple bits to be stored in a single memory cell, enhances data storage efficiency while maintaining robust performance. This study explores the technological advancements and mechanisms that enable multibit storage through FeFETs, highlighting benefits such as improved scalability, reduced area footprint, and enhanced speed compared to traditional memory technologies. Furthermore, this article discusses the implications of these advantages for next-generation memory applications, addressing challenges and future research directions in this rapidly evolving field.

Abstract The growing need for cleaner energy sources has positioned bioethanol as a viable alternative to gasoline in spark-ignition engines. This overview examines how blending gasoline with bioethanol from diverse feedstocks like sugarcane, corn, lignocellulosic materials, and algae impacts engine performance and emissions. Bioethanol's high oxygen content and octane rating enhance combustion, reducing carbon monoxide and hydrocarbon emissions. However, its lower energy density may compromise power output and efficiency. The effect on nitrogen oxides (NOₓ) is condition-dependent: while certain blends (E10–E20) may lower NOₓ due to cooler combustion, higher blends or specific load conditions can increase NOₓ formation. Blends containing 10–30% ethanol typically improve thermal efficiency and reduce emissions without requiring significant engine modifications. Higher ethanol blends might increase fuel consumption and cold-start difficulties. While second-generation bioethanol offers substantial environmental advantages, its production poses challenges. This review identifies the key research gap: the need to optimize higher ethanol blends and clarify NOₓ behavior under different operating conditions. Blends with up to 30% ethanol strike a balance between performance and sustainability, making them a practical choice.

Abstract This study conducted a comparative conceptual analysis of risk management approaches in banking and chemical processing industries, concentrating on credit risk management and process safety risk management, respectively. The research investigated underlying frameworks, including Basel II/III and IFRS 9 in banking, and HAZOP, LOPA, and Quantitative Risk Assessment (QRA) in chemical safety, revealing significant similarities in risk identification, assessment, mitigation, and monitoring. Parallels in quantitative tools were identified between credit risk indicators (Probability of Default, Loss Given Default, and Exposure at Default) and safety metrics (frequency, consequence analysis, risk matrices). The study examined artificial intelligence applications in predictive modeling for both credit defaults and process safety incidents, uncovering methodological overlap that enhances foresight and decision-making. Human and organisational influences on risk perception and control were investigated. Results produced a conceptual Integrated Risk Management Framework demonstrating the viability of applying unified risk principles across highly regulated but diverse industrial contexts. The framework integrates three pillars: predictive intelligence (leveraging advanced analytics, AI, and historical data), quantitative evaluation (systematic measurement of likelihood, severity, and exposure), and human systems management (addressing behavioural biases and fostering risk-aware cultures). This cross-sectoral approach enables knowledge transfer, establishes unified risk language, and promotes AI development synergies. The framework requires empirical validation through case studies and pilot implementations for practical application across financial and industrial sectors.