Meysam Madadi – författare

Lignin-driven Advanced Materials: The State-of-the-Art offers a pioneering approach to address the multifaceted challenges in the production of lignin-derived materials. This comprehensive book covers the creation of value-added products such as carbon materials, nanoparticles, energy storage materials, and bioplastics. It promotes collaboration between academia and industry, bridging the gap between theoretical knowledge and practical expertise. Featuring advanced lignin isolation techniques, tools for lignin characterization, and pragmatic aspects of lignin-based materials, the book incorporates the latest research and industrial advancements.Additionally, the book addresses the contentious questions surrounding lignin isolation techniques, providing advanced strategies that have the potential to replace conventional methods in the industry. It serves as a comprehensive resource for analytical tools for lignin characterization and presents solutions for more in-depth knowledge. Furthermore, it aims to improve economic viability and sustainability in lignocellulosic biomass utilization, and guides sustainable decision-making with environmental and economic assessments. This book is a vital resource for those exploring advancements in lignin-based materials.Gives access to comprehensive insights and cutting-edge knowledge in the field of lignin research, characterization tools, and sustainable lignin-driven materials production processesProvides practical solutions and applications that bridge the gap between theoretical knowledge and real-world industrial practicesOffers tools and methodologies for conducting environmental and economic assessments, empowering researchers and professionals to make informed, sustainable decisions in lignin-driven materials production processes

This volume starts with a wide review on image denoising, retracing and comparing various methods from the pioneer signal processing methods, to machine learning approaches with sparse and low-rank models, and recent deep learning architectures with autoencoders and variants.