eChapters

Green composites are emerging as promising alternatives to conventional synthetic composites in response to climate change, resource scarcity, environmental degradation, and increasing demand for sustainable materials. This chapter reviews the current landscape of green composites and examines the challenges associated with their broader adoption. It discusses technological innovations, smart sustainable materials, bio-based nanocomposites, hybrid systems, environmental and economic implications, and the relationship between green composites and sustainable development. Processing limitations, high costs, regulatory uncertainties, and commercialization challenges are considered alongside opportunities for technological advancement. Life-cycle assessment and supportive policies are important for evaluating environmental impacts and accelerating the transition toward circular and sustainable material systems.

Circular economy principles provide a framework for developing materials that minimize resource consumption and waste while maintaining functional performance throughout their life cycle. This chapter examines the development of green nanocomposites through sustainable sourcing, circular material design, appropriate processing technologies, and functional enhancement. It discusses strategies for selecting renewable raw materials, designing composites for circularity, applying nanotechnology to improve performance, and evaluating environmental impacts through life-cycle assessment. Applications of sustainable green nanocomposites and challenges in implementing circular economy principles are also considered. The chapter concludes by highlighting future innovations in synthesis, design, processing, and evaluation needed to expand the contribution of green nanocomposites to global sustainability.

Additive manufacturing is increasingly important for developing resource-efficient and customized products while reducing material waste associated with conventional manufacturing. This chapter examines the role of additive manufacturing in supporting green and sustainable production systems. It covers environmental benefits, economic and operational advantages, additive manufacturing technologies, sustainable applications, life-cycle assessment, environmental impact analysis, challenges, and design strategies. Industry trends, case studies, sustainability standards, and future research directions are also considered. In the context of green composites, additive manufacturing can support flexible fabrication and potentially improve material utilization and product customization. The chapter emphasizes the need to evaluate manufacturing technologies across their complete environmental and economic life cycle.

Green nanocomposites offer promising possibilities for biomedical applications because biodegradable matrices can be combined with nanoscale fillers to obtain specific functional properties. This chapter explores their applications in tissue engineering and drug delivery, with attention to materials such as chitosan, alginate, cellulose, and polylactic acid. It discusses the fundamentals of green nanocomposites, tissue-engineering applications, controlled drug delivery, toxicity, biocompatibility, regulatory considerations, and current limitations. Nanocomposite systems can provide useful mechanical, thermal, antimicrobial, antioxidant, and therapeutic characteristics while supporting more sustainable material development. The chapter highlights the importance of balancing biomedical functionality, safety, environmental responsibility, processing requirements, and long-term biological compatibility.

The demand for lightweight, high-performance, and environmentally responsible materials is particularly strong in automotive, aerospace, and defence applications. This chapter examines biomass-derived green nanocomposites as potential materials for lightweight systems and energy-storage-related applications. It discusses material fundamentals, design requirements, sector-specific performance expectations, material systems, and performance benchmarks. Green processing and circular economy integration are considered alongside current technological challenges and strategic research gaps. Biomass-derived resources provide opportunities to reduce dependence on non-renewable feedstocks while developing functional composite materials. The chapter emphasizes the need to balance lightweight design, mechanical and functional performance, manufacturing feasibility, sustainability, and end-of-life considerations for demanding engineering applications.

Graphene and related carbon nanomaterials have attracted considerable attention as reinforcements for high-performance green composites. This chapter examines graphene, graphene oxide, reduced graphene oxide, carbon nanotubes, and graphene nanoplatelets and their integration with bio-based polymers and natural fibres. It discusses synthesis and fabrication methods, characterization techniques, mechanical properties, electrical and thermal performance, barrier properties, and applications. The chapter also considers dispersion and compatibility challenges associated with incorporating nanomaterials into green matrices. Graphene-based composites are explored for energy storage, environmental treatment, biomedical applications, sensors, packaging, automotive, and aerospace uses. Emerging trends focus on multifunctionality, scalable production, cost reduction, and sustainable processing.

Two-dimensional nanomaterials have expanded the possibilities for designing advanced green composites with tailored multifunctional properties. This chapter focuses on MXenes and nanosilicates, including laponite, montmorillonite, and halloysite, as important alternatives and complements to graphene-based nanomaterials. Their integration into green composite matrices can influence mechanical strength, thermal stability, barrier characteristics, surface functionality, and other performance parameters. The chapter examines the development of advanced green composites based on these nanomaterials and considers their potential for sustainable material design. Particular attention is given to the relationship between nanoscale structure, matrix interaction, and composite performance, highlighting opportunities for multifunctional and environmentally responsible material systems.

Green composites provide opportunities to reduce dependence on petroleum-derived materials while maintaining useful mechanical and functional performance. This chapter examines the fundamentals of green composites and the major challenges limiting their wider adoption. Important issues include poor interfacial bonding, nanoparticle dispersion, variability of natural fibres, mechanical and thermal limitations, processing difficulties, cost, scalability, and commercialization barriers. Nanoparticle reinforcement is explored as a means of improving tensile strength, stiffness, thermal stability, electrical conductivity, and barrier properties. The chapter also discusses green processing, advanced characterization, emerging applications, sustainability assessment, life-cycle assessment, and future research directions for developing high-performance and commercially viable green composite systems.

The development of green nanocomposites requires more than replacing conventional materials with renewable alternatives; it also requires responsible sourcing, efficient resource utilization, and consideration of the complete material life cycle. This chapter examines green and eco-friendly nanocomposites from the perspective of sustainable material selection and circular economy principles. It discusses strategies for obtaining renewable raw materials, reducing environmental impacts, improving resource efficiency, and designing materials for reuse, recycling, and recovery. Applications of green nanocomposites are also considered in relation to sustainability objectives. By connecting material development with circular economy strategies, the chapter provides a framework for developing nanocomposites that combine functional performance with reduced environmental burden.

Natural-fibre-reinforced green composites represent an important alternative to conventional synthetic-fibre composites. This chapter examines renewable fibres such as hemp, jute, flax, and sisal and their integration with bio-based polymer matrices. The discussion covers material composition, processing methods, environmental considerations, sustainability, applications, advantages, limitations, and current research developments. Natural fibres offer low density, renewability, biodegradability, and cost advantages, although hydrophilicity, variability, and processing limitations can affect composite performance. Surface treatments and appropriate fabrication methods are therefore important for improving mechanical, thermal, and durability characteristics. Applications in automotive, construction, packaging, and biomedical sectors demonstrate the growing potential of these sustainable composite materials.

Bipolar plates (BPPs) are essential components of proton exchange membrane fuel cells, performing functions related to gas distribution, current collection, thermal management, water management, and cell separation. This chapter examines conventional and advanced materials used for BPPs, with particular emphasis on nanoparticle-enhanced composite systems. The incorporation of nanoparticles can improve electrical and thermal conductivity while reducing weight and supporting environmental sustainability. Materials, synthesis and fabrication techniques, and additive manufacturing approaches are discussed. The chapter also considers cost and scalability because these factors influence the wider commercialization of fuel-cell technologies. Nanoparticle-reinforced composites are presented as promising lightweight alternatives for sustainable energy applications.

The growing demand for sustainable materials has encouraged the development of green composites based on bio-based polymers and renewable reinforcements. This chapter explores advanced carbon nanomaterials, including graphene, graphene oxide, carbon nanotubes, carbon nanofibers, and carbon quantum dots, as functional reinforcements for green composites. It discusses their synthesis, functionalization, incorporation into biopolymer and bio-based matrices, and influence on mechanical, electrical, thermal, and barrier properties. Particular attention is given to green synthesis approaches using biomass-derived resources, surface modification, dispersion, environmental considerations, scalability, cost, and life-cycle assessment. The chapter also considers future opportunities for multifunctional and commercially viable sustainable nanocomposites.

Computational crop-disease detection uses machine learning, neural networks and image-processing techniques to provide timely and accurate diagnoses. This chapter presents a complete framework covering agricultural intelligence, disease categories, image acquisition, preprocessing, model training and classification. It examines datasets collected in fields or through drones and considers deployment through mobile devices, cloud platforms and other digital systems. Evaluation measures are introduced for assessing model accuracy and reliability. The chapter also considers limitations caused by insufficient or poor-quality data and variation in field conditions. Future directions include multimodal data integration and explainable models that provide transparent recommendations for sustainable crop protection and improved food security.

Climate change and resource scarcity require farming systems that anticipate risks and make proactive decisions. This chapter explains how predictive modelling, artificial intelligence and IoT technologies can strengthen climate-smart agriculture. Statistical techniques, Convolutional Neural Networks, Long Short-Term Memory networks and Transformer models are examined for forecasting weather, crop performance and resource requirements. These tools can improve irrigation and fertilizer management, raise productivity and reduce environmental impact. Adoption remains constrained by poor-quality data, infrastructure deficiencies, cost, limited interpretability and unequal access to digital technology. Farmer-centred design, federated learning, gradual deployment and scalable systems are recommended for developing fair, practical and climate-resilient agricultural solutions.

Soil health determines agricultural productivity, resource efficiency and long-term sustainability. Conventional laboratory and field-assessment methods provide valuable information but can be time-consuming and difficult to scale. This chapter presents a computational approach combining soil sensors, remote sensing and historical agronomic records. Indicators such as moisture, nutrients, pH and organic matter are processed using data-cleaning and feature-extraction techniques. Random Forest, Support Vector Machine and neural-network models estimate soil-quality parameters and identify degradation patterns. Explainable artificial intelligence improves the transparency and usefulness of predictions. Integration with edge computing enables real-time soil evaluation and supports targeted fertilization, efficient irrigation and sustainable soil-management decisions.

Early and accurate crop-disease diagnosis is essential for preventing production losses and reducing unnecessary pesticide use. Manual diagnosis can be slow, subjective and difficult to apply across large agricultural areas. This chapter introduces computational approaches using artificial neural networks, fuzzy logic, support vector machines, deep learning and hybrid optimization models. It discusses image acquisition, preprocessing, feature extraction, pattern recognition and classification of disease symptoms from leaves, crop canopies and sensor data. Integration with precision-agriculture systems, mobile advisory applications and real-time monitoring enables timely intervention. Challenges involving data quality, field variability, model interpretation and large-scale deployment are examined through practical examples and case studies.

Agricultural robotics addresses labour shortages, production demands, environmental concerns and the need for greater operational precision. This chapter examines unmanned aerial vehicles, autonomous ground machines, robotic manipulators, greenhouse robots and livestock systems. Enabling technologies include multimodal sensors, computer vision, navigation, control systems and artificial intelligence. Applications extend from land preparation, sowing and crop monitoring to weeding, spraying, irrigation, harvesting and post-harvest processing. Emerging approaches such as reinforcement learning, digital twins, swarm coordination and soft robotics are also explored. Technical reliability, high investment, connectivity, labour displacement and data ownership remain important barriers. Future development must prioritize accessible, affordable and farmer-oriented automation platforms.

Remote sensing and Geographic Information Systems provide spatial information for monitoring crops, land, soil and water resources. This chapter introduces their fundamental principles and explains developments involving satellites, UAVs, hyperspectral sensors, thermal imaging and cloud-based geospatial platforms. Analytical techniques include vegetation indices, time-series analysis, spatial modelling and artificial intelligence. Applications cover crop-health monitoring, yield forecasting, irrigation planning, drought assessment, pest and disease identification and soil-resource management. Integration with IoT, automation and other digital technologies improves the timeliness and resolution of agricultural information. These tools support evidence-based decisions responding to food-security, climate-change and natural-resource sustainability challenges.

The Internet of Things supports continuous monitoring of soil, crops, weather and other field conditions through distributed sensors and connected devices. This chapter proposes an edge-cloud architecture designed to address scalability, heterogeneous data, communication latency and energy requirements. Edge nodes conduct local processing and anomaly detection, while cloud platforms provide storage and run advanced machine-learning models. Applications include crop-status evaluation, irrigation scheduling, pest and disease detection and yield forecasting. Explainable artificial intelligence improves transparency by helping users understand model recommendations. The integrated architecture can reduce response time compared with conventional centralized systems and provides a foundation for scalable, efficient and responsive next-generation smart-farming operations.