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An incisive guide to diamond-like carbon (DLC) coatings and their contemporary applications In Applications of Diamond-like Carbon Coatings, distinguished researcher Dr. Abdul Wasy Zia delivers an insightful and up-to-date discussion of the latest advancements in new and non-conventional applications of diamond-like carbon (DLC) coatings. The editor explains the transformation of typical topics into advanced applications of DLC, including tribology for future transportation solutions, green lubrication, invasive implants, MEMS, optical devices, and more. The book also details advanced and contemporary trends in DLC coatings, like material informatics involving artificial intelligence and machine learning, and new, net-zero applications, including energy storage batteries. Readers will also find: A thorough introduction to applications of DLC coatings in mechanics, transportation, medicine, and electrical and optical device manufacture Comprehensive explorations of emerging trends in DLC coatings, including green energy, data-centric approaches, textile and plastics, and carbon circularity from DLC coated productsPractical discussions of how small and medium industries can design and develop DLC coatings for broad engineering applications Complete treatments of the benefits and opportunities presented by DLC coating applicationsPerfect for postgraduate students and researchers with an interest in DLC coatings, Applications of Diamond-like Carbon Coatings will also benefit scholars and instructors in academia, technical managers, scientists, engineers, and corporate research and development professionals with backgrounds in chemistry, materials science, polymer chemistry, and physical chemistry.
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Apply artificial intelligence and machine learning techniques across drug development and manufacturing Pharmaceutical professionals face mounting pressure to accelerate drug development while maintaining quality and regulatory compliance. Data-Driven Pharmaceutical Processing and Drug Development provides a thorough overview of data-driven methodologies from drug discovery to manufacture. This reference examines how artificial intelligence, machine learning, and big data analytics transform every stage of pharmaceutical production, covering data sources and advanced collection methods, AI and machine learning applications in liquid and solid drug formulation, statistical analysis techniques, and real-world data impacts on development decisions. Case studies demonstrate successful implementation, including COVID-19 vaccine development. Detailed coverage of FDA and EMA regulatory guidelines ensures readers understand compliance requirements for data-driven processes. The book also includes: Coverage of nanotechnology applications in drug formulation and manufacturing processes, with specific guidance on implementation and quality controlExamination of digital twins, IoT devices, and real-time monitoring systems representing the future of pharmaceutical manufacturing operationsAnalysis of computational chemistry and molecular modeling techniques that enhance accuracy in drug design and personalized medicine approachesPharmaceutical industry professionals, academic researchers, and scientists applying data-driven methodologies in drug development and manufacturing will all benefit from this reference. The book connects advanced technologies with practical implementation strategies, enabling readers to optimize processes, ensure regulatory compliance, and accelerate innovation in pharmaceutical production.