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52 articles for “Chemical machining”
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Impact of Lubricant Additives on Friction Reduction and Wear Prevention in Machinery
Abstract: Lubrication is essential for ensuring the lifespan and operating efficiency of machinery because it minimizes wear and reduces friction. Modern lubricants are made up of several chemical compounds called lubricant additives, which are essential to boost the lubricant's ability to reduce wear and friction. This study thoroughly examines how lubricant additives affect wear prevention and friction reduction in equipment. This work attempts to break down the principles behind the efficacy …
Published in International Journal of Fracture Mechanics and Damage Science · Vol. 1, Issue 2, 2023 · pp. 1–6 Read article
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Role of Quantum Chemistry in Catalysis: A Comprehensive Review
Abstract: Catalysis plays a crucial role in modern chemical manufacturing, energy conversion, and environmental protection by enabling chemical reactions to occur more rapidly, selectively, and with reduced energy consumption. A fundamental understanding of catalytic processes at the atomic and electronic levels is essential for the rational design and optimization of catalysts. Quantum chemistry has emerged as a powerful theoretical and computational framework that enables detailed investigation of electronic structure, reaction energetics, …
Published in Journal of Catalyst & Catalysis · Vol. 13, Issue 1, 2026 · pp. 01–16 Read article
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Laser Beam Machining Techniques and Applications: A Review
Abstract: Laser beam machining (LBM) is the most common thermal energy-based non-contact, non-conventional machining process. The non-conventional manufacturing processes are used to remove extra material using a variety of mechanical, thermal, electrical, chemical, or combinations of these energies without the use of sharp cutting tools as is required for conventional manufacturing. With innovative approaches to manufacturing processes, it has transformed a number of industries. It is frequently used to machine a …
Published in Journal of Microwave Engineering and Technologies · Vol. 11, Issue 3, 2024 · pp. 28–35 Read article
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Using Machine Learning to Guess Photochemical Reaction Pathways
Abstract: Photochemical reactions are crucial to many activities in the fields of energy conversion, environmental cleanup, and synthetic chemistry. However, predicting their causes and results effectively is still very hard since they entail excited electronic states, nonadiabatic transitions, and complicated potential energy surfaces. Machine learning (ML) has been a powerful technique to go along with classic quantum chemistry methods in the last few years. It offers better prediction capability and lower …
Published in International Journal of Photochemistry and Photochemical Research · Vol. 3, Issue 2, 2025 · pp. 01–12 Read article
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Structure–Property Modeling of Cement-Based Multi-Component Composites Using Ensemble Machine Learning and Explainable Feature Attribution
Abstract: Accurate prediction of compressive strength is central to structure–property optimization, quality control, and sustainability-driven design in cement-based composite materials. Cementitious systems represent heterogeneous multi-phase composites composed of reactive binder matrices and dispersed aggregate phases, whose macroscopic mechanical performance emerges from complex nonlinear interactions among constituents and curing-dependent microstructural evolution. This study develops a data-driven structure–property modeling framework to quantify the nonlinear dependence of compressive strength on multi-component composite composition and …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 112–131 Read article
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Artificial Intelligence in Drug Repurposing: A Short Impact Assessment
Abstract: Artificial intelligence (AI) in pharmaceutical repurposing has become a game-changing tool that opens new avenues for the application of new drugs that have already been approved. Traditional drug discovery is a lengthy and expensive process, whereas AI can rapidly analyze vast datasets of biological, chemical, and clinical information to predict drug-disease interactions. AI-driven techniques, such as machine learning, natural language processing, and deep learning, enable the identification of potential repurposing …
Published in Trends in Drug Delivery · Vol. 11, Issue 3, 2024 · pp. 42–45 Read article
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ML-Enhanced Self-Healing Fiber-Reinforced Polymer Composites with Embedded IoT Sensors for Damage Prediction
Abstract: Fiber-reinforced polymer (FRP) composites are widely used in aerospace and structural systems; nevertheless, the potential for microcracking and fatigue-induced performance degradation remains an obstacle with respect to improved service life. Traditional self-healing methods, while performing well on a chemical level, often lack real-time diagnostic awareness and adaptive control. To circumvent this, we developed a machine-learning augmented self-healing FRP composite, in which a DCPD–Grubbs catalytic matrix was combined with IoT sensor …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 188–208 Read article
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Post-Treatment Methods in Additive Manufacturing: A Review of Mechanical, Thermal, Chemical, and Hybrid Approaches
Abstract: Additive manufacturing, particularly Fused Deposition Modeling (FDM), has become a cornerstone of rapid prototyping and functional part production due to its accessibility and material versatility. However, the inherent layer-by-layer nature of FDM introduces surface roughness, reduced mechanical strength, and anisotropic behavior, which limit its industrial applications. This literature review investigates the impact of chemical post-processing treatments such as solvent vapor smoothing, immersion, and hybrid chemical-thermal methods on the surface quality …
Published in Journal of Experimental & Applied Mechanics · Vol. 16, Issue 3, 2025 · pp. 25–34 Read article
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Integrated Frameworks for Artifical Intelligence in Radioactive Waste Characterization and Nuclear Lifecycle Safety
Abstract: The management and characterization of radioactive waste represent a pivotal challenge for the global energy sector, requiring the convergence of advanced physics, material science, and computational intelligence. As the nuclear industry undergoes a paradigm shift toward decommissioning legacy facilities and establishing deep geological repositories, the limitations of traditional, manually-intensive waste management processes have become increasingly apparent. Rigid separation from the biosphere is required for radioactive waste, which is defined by …
Published in Journal of Nuclear Engineering & Technology · Vol. 16, Issue 2, 2026 Read article
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From Molecular Mechanics to Nanocomposites: Engineering Polytetrafluoroethylene (PTFE) for High-performance Coating Applications
Abstract: Polytetrafluoroethylene (PTFE) exhibits remarkable chemical inertness, hydrophobicity, antifriction, self-lubrication, and high-temperature resilience, making it an ideal polymer for various industrial applications, including coatings for medical implants, machinery parts, and corrosion-resistant structures. This study presents a comprehensive analysis of PTFE’s structural properties, focusing on helix reversals within its helical carbon-fluorine chains and their effects on mechanical behavior. The phase transitions of PTFE at temperatures from ambient to high (198°C and above) …
Published in International Journal of Advance in Molecular Engineering · Vol. 2, Issue 2, 2024 · pp. 14–19 Read article
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Predicting Dielectric Constants of Polymers Using Molecular Structural Descriptors and Explainable Machine Learning: A Data-Driven Approach
Abstract: Accurate prediction of dielectric constants in polymeric materials is fundamental to the rational design of advanced electronic components, energy storage capacitors, flexible substrates, and high-frequency communication circuits. Conventional approaches to identifying suitable polymer dielectrics rely on extensive experimental synthesis and characterisation, which are both time-consuming and resource-intensive. In this work, an explainable machine learning framework is developed to predict the dielectric constant of polymers directly from molecular structural descriptors derived …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 297–304 Read article
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A Data-Driven Analysis of Machine Learning Classification Models for Reliable Crop Yield Prediction
Abstract: The adoption of ML technologies in agriculture is reshaping farming practices, empowering producers to make informed, data-oriented decisions that improve yields, sustainability, and long-term resilience. In mango cultivation, ML analyzes data from weather, soil, and pests to optimize irrigation, fertilization, and pest control. Predictive analytics help forecast ideal farming practices, minimizing resource wastage and improving yield. Real-time monitoring and image-based disease detection allow timely interventions to maintain plant health and …
Published in Research and Reviews : Journal of Crop science and Technology · Vol. 15, Issue 1, 2026 · pp. 12–17 Read article
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Selective Recovery of Valuable Chemicals from Tire Pyrolysis Oil: A Sustainable Approach
Abstract: The generation of waste/scrap/end-of-life tires (ELTs) poses severe environmental challenges globally. For the disposal of huge numbers of ELTs (generated every year) using conventional recycling methods, like as landfill or incineration, are not preferred due to their adverse environmental impacts. To overcome this problem, an alternative and promising technology is developed -the pyrolysis of waste tires. Pyrolysis is a thermochemical process that decomposes organic materials (Rubber Hydrocarbons) in the absence …
Published in Journal of Catalyst & Catalysis · Vol. 12, Issue 3, 2025 · pp. 31–43 Read article
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Copper Sulfide Semiconducting Nanoparticles for Antibacterial Applications: Synthesis Strategies, Mechanisms and Performance – A Review
Abstract: Copper sulfide nanoparticles (CuS NPs) have drawn growing attention as a next-generation antibacterial platform, owing to their tunable structural, optical, and catalytic properties. This review consolidates current evidence on CuS NP synthesis, mechanisms, and antibacterial performance, comparing chemical and green synthesis routes alongside the effects of morphology, doping, and surface modification. Mechanistically, CuS NPs act through several overlapping pathways, including reactive oxygen species generation, bacterial membrane disruption, ion release, and …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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3D Printing for Polymer Science Visualization
Abstract: The burgeoning field of 3D printing offers exciting possibilities for various scientific disciplines. This paper explores the potential integration of 3D printing technology within the realm of polymer analysis. While the core focus of memory forensics investigations lies in digital forensics, the concept of 3D printing complex data structures presents intriguing possibilities for the visualization and communication of findings in polymer science. Here, we propose a future research avenue where …
Published in Journal of Polymer & Composites · Vol. 12, Issue 4, 2024 · pp. 96–101 Read article
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Advanced Fiber-Reinforced Polymer Composites: Processing, Nano-Modified Adhesive Technologies, Interfacial Engineering, Durability, and Structural Applications—A Comprehensive Review
Abstract: Fiber reinforced polymer (FRP) composites are considered to be one of the most universal categories of advanced engineering materials owing to their unique properties such as outstanding strength-to-weight ratio, corrosion resistance, fatigue durability, and wide opportunities for design. Innovations in the fields of fiber reinforcement, polymer matrix system, composite manufacturing technologies, and nano-modified adhesives technology have greatly extended the range of applications of this type of materials in aerospace, automotive, …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 980–990 Read article
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Recent Applications and Influences of Artificial Intelligence (AI) In Chemical and Allied Sciences
Abstract: Artificial Intelligence (AI), the future tool of mankind that can revolutionise scientific research by making it faster, add more efficiently and accurately. During the pandemic situation, the scientific community was parted into two distinct groups, the computation-dependent community could easily continue their research work from their resident, while the works of the other group of researchers with lab-oriented research stopped entirely. In this connection use of AI becomes important and …
Published in International Journal of Cheminformatics · Vol. 1, Issue 2, 2023 · pp. 22–48 Read article
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AI-based Drug Discovery-Revolutionizing Pharmaceutical Research
Abstract: The traditional drug discovery process is often costly, time-consuming, and prone to high failure rates. The advent of Artificial Intelligence (AI) has revolutionized this field by significantly enhancing efficiency, reducing costs, and improving success rates. AI-driven approaches, including machine learning (ML), deep learning (DL), and natural language processing (NLP), have transformed key areas such as drug target identification, molecular screening, lead optimization, and clinical trial design. AI models can analyze …
Published in Research & Reviews: A Journal of Drug Design & Discovery · Vol. 12, Issue 2, 2025 · pp. 30–44 Read article
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Taguchi Method: A New Approach for Evaluating and Optimizing Parameters of TIG Welding
Abstract: The construction and industrial sectors must improve the quality of their welds. An experimental plate fabricated of SS304L was to be tungsten inert gas (TIG) welded in this experiment, and an effort was made to enhance the mechanical properties. The Taguchi L9 orthogonal array was used to organize the experiment, and the mechanism of advancement was used to simulate the performance. The tensile strength of the welded junction rises in …
Published in Journal of Polymer & Composites · Vol. 11, Issue 2, 2023 · pp. 121–129 Read article
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Debris Flow Kinetics in Planetary Environments: A Systems Perspective
Abstract: Debris flow kinetics in planetary environments represent a critical intersection of geomorphology, fluid mechanics, and planetary science. These gravity-driven flow mixtures of solids, liquids, and gases play a key role in shaping planetary surfaces and recording environmental histories. This study adopts a systems perspective to analyze debris flow behavior across different planetary contexts, emphasizing the interconnected roles of material properties, energy transformations, and environmental forcing. By integrating rheological models with …
Published in International Journal of Universe · Vol. 1, Issue 2, 2025 · pp. 08–17 Read article