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Transforming potential into performance: harnessing AI-driven competency frameworks to nurture talent in higher education
The Indian Higher Education System is the backbone for economic expansion, creativity and innovation resulting in the progress of the nation. With a huge network of universities, colleges, and institutes, India's higher education system is one of the world's largest, serving millions of students. Despite its rapid growth the Higher Education System in India is witnessing issues like lower research output in comparison with global benchmarks, shortage of qualified academic staff and inadequate quality. Considering the available store house of literature and need gap analysis this paper aims to investigate how competence frameworks powered by artificial intelligence might help to turn promise into performance, to examine the changing talent needs in Higher Education by looking at how the demands of academia, industry, and research are always changing and also to investigate AI's function in Competency-Based Talent Management. Based on the discussions of multiple spectrums namely, Academic excellence, Students employability and Industry readiness, Research Innovation, Teaching effectiveness, Global and National Rankings and Faculty Satisfaction, the paper has suggested the implementation of AI to all pervasively enable the academic Institutions to grow, diversify and enrich the talent acquisition and retention. The paper also focussed on the significance of the inclusion of AI driven automation process to tide over the mundane difficulties of administrative work, to enrich the collaborative research initiatives, to benefit the teaching community to concentrate on modern andragogies, to upscale the Global and National Rankings, to gain academic excellence, to strengthen the employability skills among the students and ultimately to promote the overall satisfaction of the educators and educates. The paper concluded that well trained Higher Education Institutions can optimize their workload judiciously by incorporating AI tools and in turn can contribute to a better learning environment. 2026 Elsevier Inc. All rights reserved. -
Collaborative security approaches for IoT ecosystems
The surge in Internet of Things (IoT) devices has transformed numerous industries by enabling unparalleled connectivity and data sharing. Yet, this rapid growth has also exposed critical security vulnerabilities. This chapter delves into collaborative security strategies aimed at improving the safety and reliability of IoT ecosystems. A major vulnerability is weak authentication and authorization, often stemming from poor password practices or insufficient authentication mechanisms. Such flaws can result in unauthorized access, data breaches, and serious cyber threats, including Distributed Denial of Service (DDoS) attacks and Man-in-the-Middle (MITM) attacks. DDoS attacks can overwhelm essential IoT systems, like those in smart cities or healthcare, while MITM attacks can jeopardize data integrity during communication between devices and cloud services. Given that IoT devices frequently handle sensitive information, including personal and health data, ensuring their security is vital to avoid detrimental outcomes for users. Physical security risks also present a challenge, as the physical compromise of IoT devices can disrupt systems or pose risks to individuals. To address these threats, this chapter recommends several cybersecurity measures, such as secure design and development practices, robust authentication methods, and advanced encryption techniques. Secure device design should include mechanisms for safe firmware updates and employ Trusted Platform Modules for secure key storage. Effective authentication can be enhanced with multifactor methods, role-based access controls, and digital certificates. Data protection should involve encrypting data both in transit and at rest, as well as employing techniques like data anonymization and differential privacy. 2026 Elsevier Inc. All rights reserved.. -
Identity and access management for IOT devices
Identity and Access Management (IAM) for Internet of Things (IoT) devices is crucial in safeguarding the security and integrity of interconnected systems. This critical analysis explores the unique challenges and solutions associated with IAM in the context of IoT environments. IoT devices, characterized by their diverse types and widespread deployment, present significant complexities in managing identities and controlling access. Traditional IAM frameworks often fall short when applied to the dynamic and distributed nature of IoT networks, where devices frequently interact autonomously and may lack standardization. The analysis identifies key issues such as scalability, interoperability, and real-time authentication. It highlights the need for advanced IAM solutions that can handle the vast number of devices, support diverse protocols, and ensure robust security measures. Emerging technologies, including blockchain and AI-driven authentication, offer promising avenues for enhancing IAM in IoT contexts. Blockchain can provide decentralized, tamper-proof identity management, while AI can enhance real-time threat detection and adaptive access control. The review underscores the importance of developing IAM frameworks that are both flexible and scalable to address the evolving security requirements of IoT environments. By addressing these challenges, organizations can better secure their IoT infrastructure and mitigate risks associated with unauthorized access and identity breaches. 2026 Elsevier Inc. All rights reserved.. -
The role of IoT in optimizing quick commerce operations: a comprehensive analysis of micro-fulfillment centers and consumer satisfaction
This research focuses on integrating Internet of Things (IoT) and AI-driven machine learning algorithms to optimize operations within micro-fulfillment centers for quick commerce in India. The study addresses the growing demand for faster delivery times, driven by the rise of e-commerce, and the challenges associated with managing inventory, logistics, and last-mile delivery. By implementing IoT-enabled devices to monitor real-time data and deploying machine learning algorithms for demand forecasting and route optimization, the study aimed to enhance operational efficiency and reduce delivery times. The research utilized a dataset comprising various operational metrics from micro-fulfillment centers across major Indian cities. A comparative analysis revealed that the AI and IoT integration led to a 22% reduction in delivery time and a 15% improvement in order accuracy. Additionally, the predictive maintenance system, powered by IoT sensors, resulted in a 30% decrease in equipment downtime. The results demonstrate that combining IoT with machine learning optimizes supply chain operation and significantly contributes to meeting consumer expectations in the quick commerce sector. This research provides a comprehensive framework for leveraging technology to address the unique challenges of the Indian market, offering valuable insights for industry stakeholders. 2026 Elsevier Inc. All rights reserved.. -
Emerging trends and security in IoT devices
The Internet of Things has rapidly evolved, connecting billions of devices and transforming various industries. This chapter explores the latest emerging trends in IoT devices, focusing on advancements in connectivity, edge computing, and artificial intelligence (AI) integration. As IoT adoption grows, so does the importance of security, given the increasing vulnerabilities and potential threats. This chapter delves into key security challenges, including data privacy, secure communication protocols, and device authentication. It also discusses cutting-edge security solutions, such as blockchain technology and AI-driven anomaly detection, to safeguard IoT ecosystems. By examining both technological advancements and security considerations, this chapter provides a comprehensive overview of the current landscape and future directions for IoT devices. 2026 Elsevier Inc. All rights reserved.. -
Modern privacy preserving strategies for IoT security
The proliferation of Internet of Things (IoT) devices has brought about a revolution in various industries and everyday life, enhancing connectivity and efficiency. Nevertheless, this rapid adoption has also given rise to notable security and privacy challenges, leading to the need for robust solutions to safeguard sensitive data. This study delves into contemporary strategies for preserving privacy specifically designed for IoT security, with a focus on the most recent trends and technologies. By the year 2024, it is projected that the global count of IoT devices will exceed 30 billion, exhibiting a compound annual growth rate (CAGR) of 26.7% from 2021 to 2024. This exponential growth has led to a significant increase in the amount of data produced and transmitted by IoT devices, thereby creating fresh opportunities as well as vulnerabilities. Privacy apprehensions are crucial, given that these devices frequently amass sensitive personal and organizational data. The research scrutinizes cutting-edge privacy-preserving techniques, such as federated learning, homomorphic encryption, and differential privacy, which present promising resolutions for safeguarding data while upholding functionality. Federated learning has garnered attention as a decentralized approach that permits data processing to occur locally on devices rather than being sent to central servers, thereby reducing data exposure. Homomorphic encryption facilitates data processing while encrypted, ensuring a high level of security without disclosing the underlying information. Conversely, differential privacy introduces statistical noise to data, guaranteeing that individual data points are not easily discernible, thus preserving user privacy. This section also accentuates the significance of adhering to regulations and the impact of frameworks like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) on shaping the advancement and acceptance of privacy-preserving technologies. Moreover, an exploration is made into the incorporation of blockchain for immutable and transparent data management in IoT environments. This manuscript furnishes an exhaustive overview of the prevalent trends and technologies within this realm, providing insights into the future trajectory of IoT security and privacy. 2026 Elsevier Inc. All rights reserved.. -
Human factors and social engineering in IoT attacks
This chapter explores how social engineering and human factors contribute to IoT assaults, emphasizing how human mistake and psychological manipulation weaken linked systems. Cybercriminals are increasingly using social engineering techniques to trick people into disclosing private information or jeopardizing security measures as IoT devices become more and more integrated into everyday life and vital infrastructure. This study examines important strategies like baiting, pretexting, and phishing, highlighting their unique uses in Internet of Things settings. It also looks into how human behavior affects security procedures, showing how dangers are increased by insufficient awareness and training. The chapter offers important insights into how human factors and IoT security interact by examining current case studies and actual attack scenarios. It ends with tactical suggestions for enhancing security awareness, fortifying authentication procedures, and putting in place efficient defenses against social engineering risks in IoT ecosystems. Future research should focus on developing AI-driven security measures, flexible defense strategies, and strong policy frameworks to strengthen IoT security. Tackling these human-related vulnerabilities is essential to building a safer and more reliable IoT ecosystem. 2026 Elsevier Inc. All rights reserved.. -
Cementing the future from gray to green
This chapter explores the potential of green structures and nature-based solutions in the context of Bangalore, a city that is rapidly urbanizing and struggling with the environmental impacts of traditional building materials such as concrete. Concrete, while being a mainstay of modern construction, contributes significantly to global CO2 emissions (Fischetti etal., 2023). The problem is how we can maintain Bangalore's rapid growth without concrete's ill effects. What are the alternatives to concrete, how do the different materials compare with each other, and how will it affect Bangalore? In Bangalore, using materials like clay and mud blocks instead of bricks, simple Kota tiles and clay tiles for flooring, or Hempcrete would have a significant environmental benefit. This chapter also discusses nature solutions, which are nature-based strategies for creating sustainable and climate-resilient solutions to address infrastructure needs for Bangalore. These solutions often offer higher quality, lower cost, and more excellent resiliency than traditional infrastructure. Composition and Properties of Hempcrete is a biocomposite building material that combines the internal woody core of the industrial hemp plant with a lime-based binder. The result is a lightweight, insulating, and flame-retardant material with excellent thermal properties. The porous cellulosic structure of the hemp core provides exceptional bonding strength with lime, making foam concrete a versatile material that can be used in both new construction and renovation of existing buildings. One of the main thermal properties and advantages is its high thermal mass. This means they can store heat and keep buildings warmer in the winter and cooler in the summer (Hemp). This property reduces the need for energy-intensive heating and cooling systems, making buildings made from Hempcrete more energy-efficient and cost-effective in the long run. 2026 Elsevier Inc. All rights reserved. -
Impact of fine-tuning large language model in society: a comprehensive study
The fine-tuned large language models (LLMs) have revolutionized artificial intelligence (AI) and natural language processing (NLP) with key innovations in neural architectures, particularly with the transformer. The recent advancements of LLM have witnessed that models like bidirectional encoder representations from transformer (BERT), generative pretrained transformer (GPT)-2, GPT-3, and text-to-text transfer transformer (T5) show outstanding performance in understanding and generating human-like text at scale. Researchers use fine-tuned models to excel in their responses to specific tasks or domains. The purpose of fine-tuning the LLM models is to improve the performance of LLM in special fields such as education, research, literature summarization, contract analysis, and creative content generation. Fine-tuning LLM models also has issues like amplifying biases, ethical issues, and regulatory implications, remarkably when LLMs are fine-tuned for emerging domains that may hold harmful stereotypes or misinformation. Fine-tuned LLMs also provide substantial societal benefits, including expert-level knowledge to underserved regions and personalizing educational resources for self-directed learning. The study also discusses the technical aspects of fine-tuning LLMs by examining how general-purpose models are transformed into efficient models. The impact on society and the need for a framework that can shape the deployment of models, with ethical guardrails, transparency, and public engagement to ensure responsible development and use of fine-tuned LLMs. The current work explores the various steps that can be taken for bias mitigation and transparent documentation for different stakeholder engagements. The purpose of the chapter is to analyse the perspectives from technical foundations with ethical, cultural, and policy considerations and provides an integral view of the societal impact of fine-tuned LLMs. 2026 Elsevier Inc. All rights reserved. -
Applications and future directions in multimodal large language model: opportunities and challenges
Multimodal large language models (MLLMs) are an application of artificial intelligence that is rapidly growing by integrating numerous use cases. MLLMs have the capability to process data from several sources, including structured and unstructured. It enables large language models (LLMs) to give insights to the user by analyzing data from various formats. The traditional way of analyzing the data was done with a single data format. While using MLLMs, multiple data modalities are handled to manage complicated multimodal tasks, like generating content, multimodal perception, and augmenting human-computer interaction. This chapter discusses in detail the insights from data from multiple modalities and domains of the use cases of MLLMs. We also discuss the advantages of MLLMs and explain the transformative benefits from unimodal systems to multimodal systems in different sectors. We also focus on the ethical usage of MLLMs by addressing the challenges related to privacy, operational limits, bias, computational difficulties, and data scarcity. The scarce assessment metrics and trials in accomplishing robust explainability are also discussed. To train these MLLMs, acquiring and training the data utilizes more computation and power consumption, along with addressing the data security and privacy concerns. The chapter also discusses the sensible usage of AI through different problems and practices. Detailed analysis and strategies for addressing global challenges and promoting novelty in model development, outlining how these MLLMs shape the upcoming technological innovation focusing on ethical application of technology with an advantage on society. 2026 Elsevier Inc. All rights reserved. -
Human gut microbiota and regulation of human behavior
The human body is often referred to as a microbiome, embodying a multitude of microbes. Specifically, the gut microbiome constitutes a cluster of bacteria within the gastrointestinal (GI) system. In both healthy individuals and those facing health issues, these bacteria significantly impact human physiology. They form an integral part of the unconscious system that exerts influence over human behavior. Globally, researchers are actively investigating the components of a good gut microbiome to enhance our comprehension of the role gut microorganisms play in health and disease. The gut harbors a diverse array of bacteria, encompassing both hazardous and beneficial types. Furthermore, studies on specific health-improving parasitic species are yielding biological insights that may propel the development of novel drugs. The gut microbiome directly impacts an individual's health by secreting physiologically active compounds like vitamins, essential amino acids, and lipids. Moreover, it indirectly influences the immune system and metabolic functions, thereby affecting moods and cognitive functions. Recognition of the role played by gut microbiota extends to various diseases and disorders. Evidence demonstrates significant interactions between the microbiome and certain drugs, profoundly shaping their intended effects. The interplay between the immune system, gut microbiota, and psychiatric disorders such as eating disorders, as well as conditions like cancer, autoimmune diseases, and autism spectrum disorder, is increasingly evident through ongoing research. This is unsurprising given the pivotal roles played by daily caloric intake, eating habits, and food composition in regulating various biological systems. Recent research sheds light on the intricate gut-brain axis (GBA), a two-way communication system where the gut receives signals from the brain, and vice versa. Microbes in the GI tract produce neurotransmitters that influence crucial functions like learning, memory, attention, and emotions. Even a slight change in the gut microbiome can lead to inflammatory reactions, connecting to the hypothalamic-pituitary-adrenal (HPA) axis and influencing human behavior. Under stressed conditions, changes in the gut microbiome can harm helpful bacteria, reduce the variety of bacteria in our gut, and allow harmful bacteria to grow. This makes us more likely to get sick and causes inflammation in our gut. Some studies even suggest that chemicals produced by the gut during an infection can affect our brain, making us more prone to feeling anxious or depressed. Knowing that our gut microbiome influences our behavior, it's essential to pay attention to what we eat, avoid drugs that harm our gut bacteria, and consider taking probiotic supplements in our diet. 2025 Elsevier Inc. All rights reserved. -
Explainable AI and computational intelligence in healthcare: Application to clinical decision support and personalized medicine
Human intelligence system simulation has made significant strides in several areas, including clinical decision-making using medical imaging and electronic health records, health referral systems, discovering recommended medications and vaccines, recognizing prescribed errors, and real-time data analysis. Therefore it is essential to discover patterns and transfer knowledge in the medical domain. The obstacles at the level of data collection, data analysis, model development, decision-making, and ethical concerns need to be addressed. It is vital that clinical interpretation tools associated with both hardware and software employed by medical professionals be precisely examined when rendering decisions regarding diagnoses and therapies related to the diagnosis. Computer scientists generally lack training in medical concepts specific to their field. Another crucial aspect is that black box algorithms based on artificial and computational intelligence are opaque and devoid of logical justification. Owing to these limitations, the technique of eXplainable Artificial Intelligence (XAI) models is explored in this chapter, primarily focusing on improving the interpretability of computational models. Specific objectives of this chapter are to: a) discuss the role that CI techniques and methods in the construction of an intelligent health prediction system; b) demonstrate the multiple CI paradigms utilized in medical prediction; and c) present recent case studies to showcase the performance of the computational intelligent models. 2026 Elsevier Inc. All rights reserved. -
Air quality index improvement through machine learning and quantum computing: a framework for advancing air quality prediction using quantum-inspired metaheuristics on climate change to achieve positive health
Climate change significantly exacerbates air quality deterioration, intensifying health risks and environmental instability. Air pollution poses significant challenges to public health and environmental sustainability. Accurate prediction of the Air Quality Index (AQI) is crucial for timely interventions and policy-making. As urbanization and industrial activities intensify, there is an urgent need for accurate and real-time air quality monitoring systems. Advanced machine learning (ML) techniques have shown promise in air quality forecasting and classification. Recently, quantum-inspired computational paradigms have emerged as innovative tools to overcome the limitations of traditional models, particularly in areas like feature selection, optimization, and spatial-temporal pattern recognition. This study presents a comprehensive analysis of various machine learning and deep learning models for AQI prediction, utilizing pollutant concentration data. It also explores quantum computing-inspired approaches. We explore the efficacy of different algorithms, datasets, and preprocessing techniques. This paper critically reviews high-impact research that explores the intersection of climate-induced changes and air quality prediction using ML. It identifies trends, gaps, and emerging methodologies. We conduct a comparative analysis of datasets, prediction models, and performance metrics. The paper focuses on three case studies. The first case study focuses on the Indian aspect using an Indian dataset and the global aspects with different global datasets, and the second case study uses quantum-inspired approaches. We further evaluate the performance of 10 state-of-the-art ML models, offering a roadmap for future research and deployment. Effective air quality forecasting is vital in urban planning decisions. This also plays an essential role need in environmental management and the protection of public health. This issue directly deals with Sustainable Development Goal (SDG) 3 and SDG 13. SDG 3 is related to positive health and SDG 13 is related to climate action. Conventional predictive models in ML face challenges due to multiple reasons. Effective feature selection is one such challenge as well as effective hyperparameter tuning. These challenges limit the effectiveness of artificial intelligence models. In the proposed framework, searching is enhanced using quantum jump- and quantum mechanics-related principles. This approach leads to the development of a quantum-inspired particle swarm optimization called QPSO. QPSO is able to provide more promising results by bridging the gaps of traditional optimization techniques. Model convergence is accelerated by using quantum-inspired feature selection techniques. 2026 Elsevier Inc. All rights reserved. -
Multiobjective portfolio optimization using multilevel quantum inspired optimization algorithms: a comparative study
The study of portfolio optimization has been a significant focus for computer science and finance researchers, with frequent publication of innovative methods. Numerous works have illustrated that conventional approaches like quadratic programming struggle with nonlinear constraints. This chapter compares ant colony optimization and particle swarm intelligence optimization within classical and quantum inspired frameworks, utilizing qubits and qutrits. This study analyzes benchmark datasets from the NASDAQ, Dow Jones, and BSE spanning over a decade. A pioneering effort has been made to develop a multiobjective portfolio optimization technique through a multilevel quantum inspired optimization algorithm. The experimental results demonstrate that the quantum inspired metaheuristic technique that utilizes qutrits slightly outperforms classical and qubit based quantum inspired methods. 2026 Elsevier Inc. All rights reserved. -
Ethical considerations in nanomedicine research involving women
This chapter elaborates social impact and perspectives of advanced technologies as well as critically discuss the ethical issues of womens participation in nano-medicine research. It starts by addressing the issue that women have been under sampled in clinical research but it is moral to take their samples farther into research most notably with certain special health conditions and unique reactions to drug treatments that are likely to be unique to women. One of the most emphasized concepts is informed consent, for the purpose of precisions and clarity in explaining its purpose and usefulness, as well as the probable consequences in the form of some narrowly speculative treatments with particular relevance to certain probabilities remaining beyond the range of perception on the female body after some or other nano-scale procedures. The chapter also talks about the ethical implications in the present strategies to increase the efficacy and safety for women only therapies like nanomedicine; the author urges for stringent preclinical and clinical guidelines which should have examined the following gender variations. It also examines the regulation with guidance and suggests contingency structures that are employed in the management of the ethical use of nanomedicine in women health care. Therefore this chapter tries to develop ethics together with science, incorporate these met ethical considerations into methods and guidelines in research, to contribute to enhancement of beneficial impact of the nanomedicine for womens rights and well-being. 2026 Elsevier Inc. All rights reserved.. -
Water purification membranes: state of the art, fundamentals, challenges, and opportunities
The rising global demand for clean and safe water has intensified the necessity for effective and sustainable purification technologies. This chapter thoroughly overviews membrane-based water purification systems, highlighting their basic principles, material types, operational mechanisms, and evolving roles in addressing water scarcity. It begins with the historical progression of membrane technology, discussing the various membrane types alongside essential separation processes including ultrafiltration, nanofiltration, reverse osmosis, and membrane distillation. The text also covers recent innovations in nanocomposite membranes, cutting-edge material design, and membrane module configurations, focusing on enhancing performance and energy efficiency. Special focus is placed on membrane fouling, its causes, effects, and strategies for mitigation, backed by computational modeling and machine learning insights. The chapter begins by exploring emerging trends, such as the development of fit-for-purpose membranes, their integration into zero liquid discharge systems, and scalable fabrication methods. Together, this information highlights the transformative capacity of membrane technology in tackling global water issues. 2026 Elsevier Inc. All rights reserved.. -
Detection and management of sucralose pollution: innovations in sustainable monitoring, mitigation, and energy utilization
Sucralose, a widely utilized artificial sweetener, has emerged as a potential environmental contaminant, persisting in aquatic environments and causing ecological risks. The use of suitable methods and sustainable materials can be applied for the detection of sucralose in wastewater, sludge, and other waste matrices Using sustainable materials and novel techniques for discerning sucralose. Leveraging waste-derived components, particularly in electrochemical sensing systems with competent nanomaterials can be a promising avenue for sucralose detection. Moreover, integrating waste-derived carbon materials like activated carbon or carbon black as electrodes enhances sensor sensitivity by augmenting organic molecule adsorption capacities, thus amplifying detection capabilities. Furthermore, waste-derived substrates such as cellulose fibers or agricultural residues can be engineered to serve as substrates or immobilization matrices in enzyme-based sensors for sucralose detection. While the electrochemical systems current output may not directly serve energy applications, its potential utilization for energy generation or storage, through technologies like electrochemical capacitors or batteries, underscores the broader sustainability objectives by repurposing energy from the detection process for additional beneficial applications. The amalgamation of sustainable methodologies in sucralose pollution detection and management not only contributes to environmental preservation but also aligns with the broader trajectory toward sustainable resource utilization and conservation. 2026 Elsevier Inc. All rights reserved. -
Electrochemical synthesis of nanoparticles
Nanomaterials possess diverse applications across environmental, medical, and energy sectors, owing to their unique properties dictated by structure, size, composition, and morphology. These characteristics of nanoparticles are pivotal in materials science. Their distinctive properties made an interest in specific applications, prompting the exploration of synthesis methods tailored for various purposes, particularly in electrochemistry. This chapter examines a nanoparticle synthesis technique capable of producing nanoparticles suitable for diverse applications, emphasizing electrochemical synthesis. This method offers efficient fabrication of nanoparticles at low temperatures with high purity, presenting an environmentally friendly preparation approach. Electrochemical synthesis, particularly through electrodeposition, involves the controlled passage of an electric current between the electrodes immersed in an electrolyte solution. This method yields stable nanoparticles with robust electrical contact, poised for utilization in batteries, fuel cells, supercapacitors, catalysis, optoelectronics, and beyond. The chapter elucidates the electrochemical synthesis process, underscoring its potential for advancing nanoparticle-based technologies across multiple disciplines. 2025 Elsevier Inc. All rights reserved. -
Graphene-based nanocomposites for energy conversion and storage
Graphene, consisting of a single layer of carbon atoms organized in a hexagonal pattern, exhibits exceptional electrical, mechanical, and thermal characteristics. The combined effects arising from the synergy of graphene with other materials assume a pivotal role in enhancing the overall performance of energy devices. The initial section of this chapter focuses on the utilization of graphene composites in energy conversion technologies, spanning solar cells, water-splitting devices, and fuel cells. Subsequently, this chapter explores the application of graphene composites in energy storage systems such as lithium-ion batteries and supercapacitors. Graphene provides a significant surface area, facilitates rapid electron transport, and offers mechanical stability, all positively impacting the energy storage capacity and cyclic stability of these devices. Furthermore, the chapter discusses the current research trends, challenges, and future prospects concerning graphene composites for energy conversion and storage applications. 2025 Elsevier Inc. All rights reserved. -
Polyurethane nanocomposites for food packaging applications
Progress in food packaging technology improves modern food trade by simplifying transportation and sales, while offering enhanced protection. Traditional food packaging uses petroleum-based and nonbiodegradable packaging materials, leading to severe environmental and health-related issues. Polymer nanocomposites can reduce traditional plastic consumption and achieve a high recycling efficiency while maintaining the desired barrier and mechanical properties. Polyurethane, a polymer with good mechanical properties, is characterized by low pollution, easy modification, and environmental protection. Innovations in its structure and functionality through the incorporation of nanoscale fillers have led to enhanced barrier properties, thermal stability, and biodegradability. Polyurethane nanocomposites (PUNCs) exhibit multifunctional properties owing to the synergistic effect of polymers and nanofillers, and are used to increase the shelf life of food products, reducing food waste and foodborne illness, thereby contributing to sustainable development goals. This chapter begins by elucidating the fundamental properties of PU and the significance of various nanocomposites in tailoring these properties to fit packaging requirements. This chapter also describes the role of PUNCs in extending the shelf life of food, providing ultraviolet light protection, barrier, antimicrobial, and antioxidant properties, and enhancing the mechanical strength of packaging materials. The recent advancements in food packaging applications of PUNCs and the challenges and future perspectives related to large-scale production, consumer acceptability, recyclability, and potential health implication aspects are discussed in this chapter. 2026 Elsevier Ltd. All rights reserved.