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Polyurethane nanocomposites for supercapacitor applications
Polymer nanocomposites have received a lot of interest recently in materials research as they display a variety of distinct properties compared to those of their counterpart polymer micro-composites, whose matrices include the same inorganic components. The flexible features of polyurethane (PU) nanocomposites, which may be easily adjusted to fulfill the specific needs in energy storage, have led to the rapid development of these materials in recent years. Numerous types of functional nanofiller integration have led to the advancement of PU-based nanocomposites. Details on PU nanocomposites' synthesis, characteristics, and uses in supercapacitors are covered in this chapter. There have been several approaches explored for the synthesis of various PU nanocomposites, including electrospinning, dip-coating, spray coating, and one-step carbonization. Recent advancements in the use of PU nanocomposites as supercapacitors, along with their challenges and possibilities in the future, are also discussed herein. This chapter also reviews recent developments in smart supercapacitors, including their various properties such as long-lasting cycling stability, excellent specific capacitance, high energy density, and good capacitance retention. Functions of supercapacitors include self-healing, shape memory, shape editing, and photodetection, along with specific emphasis on their recyclability and recoverability. 2026 Elsevier Ltd. All rights reserved. -
Polyurethane nanocomposites for electromagnetic interference shielding applications
Polyurethane (PU) is composed of polyisocyanate and polyol units joined through urethane linkages. The isocyanate and polyol units constitute different domains in PU, which are, in turn, responsible for its properties such as softness, flexibility, and hardness. Shielding of electromagnetic (EM) radiation is generally attained by reflecting EM waves from the surface, absorbing the signals, or by multiple internal reflections. Pristine PU is not an efficient electromagnetic interference (EMI) shielding material because of its nonconducting nature. But the EMI shielding can be improved by incorporating conducting polymers into the PU-based nanocomposite. Significant factors that influence the shielding effect of PU nanocomposites are the thickness and conductivity of the film. Features that predominantly influence the EMI shielding performance of nanocomposites are identified as the nanofillers used, the dispersion state, and the interaction between the filler and the polymer. This book chapter attempts to explain the EMI shielding of PU nanocomposites, synthesis of PU-based EMI shielding materials, different nanofillers used along with PU nanocomposites, and their efficiency in EMI shielding. 2026 Elsevier Ltd. All rights reserved. -
Physical aging of polymers and polymer nanocomposites
This chapter offers an illustration of physical aging in polymers and their nanocomposites. Physical aging, explained as time-dependent property changes taking place at fixed temperature and less stress without external forces or influences, is crucial for recognizing long-term performance and strength of polymer materials. The text discovers how physical aging establishes differently in amorphous polymers and semicrystalline polymers along with their nanocomposite, with specific consideration to polymer blends. Molecular mechanisms focusing on physical aging are addressed, indicating how polymers in nonequilibrium states undertake structural relaxations to equilibrium when stored below their glass transition temperature (Tg). The chapter scrutinizes factors affecting aging rates, including quench depth (?TA=Tage ? Tg), nanofillers, and the effect of molecular mobility. Additionally, it evaluates theoretical models and molecular simulation methodologies increasingly assisted to overcome limits of conventional experimental methods in determining intrinsic aging mechanisms. The work completes by classifying future research directions essential for evolving understanding of polymer aging behavior, with suggestions for refining service life and storage constancy of polymer-based materials and equipment. 2026 Elsevier Ltd. All rights reserved. -
Physical aging of biopolymers and their nanocomposites
With growing environmental concerns, particularly around the widespread use of conventional plastics, there is an urgent need to develop sustainable alternatives like bioplastics and biocomposites. These eco-friendly materials offer the potential to significantly reduce the carbon footprint while enhancing product performance and durability in various applications. This chapter aims to expand scientific understanding of biocomposites, focusing on their behavior under different aging conditions. A comprehensive analysis is provided on aging processes, aging mechanisms, and strategies to improve the longevity and performance of biocomposites. Special emphasis is placed on future research directions and the adoption of innovative aging techniques to optimize the performance of biopolymers. This review explores both the advantages and challenges of using biocomposites as replacements for traditional petroleum-based plastics, with a particular focus on their degradation behavior over time. The insights presented here are essential for driving further research and development in bio-based and biodegradable polymers, highlighting their potential for both academic inquiry and industrial application. By addressing key aspects of biocomposite aging, this chapter aims to guide researchers in overcoming existing challenges and advancing the field toward a more sustainable future. 2026 Elsevier Ltd. All rights reserved. -
Surface-modified carbon nanomaterials
Surface-modified or engineered nanostructures have become an essential aspect of surface modification in various domains. They inherit promising properties that can be tailored based on specific requirements enabled by design and fabrication essentials. Among them, carbon-based materials are potential candidates for various applications in drug delivery, energy storage, environmental profiling, and disease diagnosis. The biomedical applications of carbon-based nanomaterials have recently boomed for disease-specific prevention, diagnosis, treatment, and recovery. They can be extended for precise, specific, and sophisticated approaches to yield long-lasting and favourable outcomes. In this book chapter, we will be discussing about the interesting properties of carbon, such as good mechanical strength, high electrical conductivity, and desirable morphological features. In continuation, the history of carbon surfaces and nanomaterials will be discussed to provide a background knowledge of the element. We will also discuss the relevant reports on emerging carbon-based nanomaterials, their attributes, and applications in distinct arenas involving electrochemical-based approaches. The protocols for surface modification will be summarised in the later section of the chapter. Finally, their downsides will be compared. These nanostructured carbon surfaces confer the advantages of small size, enabling advantages over bulk phase materials. Finally, the toxicity of surface-modified carbon materials has been studied in depth before the summation of the chapter. 2026 Elsevier Inc. All rights reserved. -
Journey toward Internet of Bio-Nano Things: evolution, trends, and future challenges
The Internet of Bio-Nano Things (IoBNT) is a pioneering vision of embedding biological components into the existing network of interconnected nanodevices to create sophisticated webs of nano-scale biological and artificial components. This framework extends the conventional Internet of Things architecture to include biological components to facilitate new relationships between artificial nano-scale devices and biological systems. IoBNT enables unprecedented functionalities in healthcare, environmental monitoring, agriculture, and food protection through multiple communication channels, including molecular communication, THz-band electromagnetic nanocommunication, acoustic nanocommunication, and FRET-based nanocommunication. Nonetheless, there are still many issues to be solved in the areas of nanonetwork implementation, bio-cyber interface development, big data analytics, security, energy harvesting, and biocompatibility. Some of the potential countermeasures to security risks include device hardening, cryptographic mechanisms, external device delegation, intrusion detection, and hardware-based solutions. Although IoBNT presents a great prospect for change across various sectors, there is a need for more research to resolve technical, biological, and security issues so that it can be adopted in real life. 2026 Elsevier Inc. All rights reserved.. -
Big data management and smart drug delivery system based on Internet of Bio-Nano Things
Integrating big data analytics and Internet of Bio-Nano Things (IoBNT) presents disruptive prospects in healthcare, especially in advancing intelligent medication delivery systems. This chapter examines the complex dynamics of big data management, emphasizing the collection, processing, and analysis of extensive biomedical data produced by IoBNT-enabled devices. IoBNT, an innovative network of bio-nanosensors and actuators, enables accurate and real-time monitoring of physiological states, hence advancing personalized and targeted drug delivery systems. Key components like data integration, predictive analytics, machine learning algorithms, and the way contemporary communication protocols enable smooth data flow between bio-nanodevices and healthcare infrastructure. The chapter also explores the challenges associated with data security, privacy, next scalability within the frameworks of IoBNT designs. Using big data analytics, IoBNT-based innovative drug delivery systems can improve therapeutic outcomes by optimizing dosage, timing, and administration paths. This chapter underscores the potential of the Internet of Things and big data to transform healthcare in the future through a more agile, responsive, and efficient method of disease management. The discussion is supported by analyzing emerging trends and concrete case studies, illustrating how these advancements can lead to significant improvements. 2026 Elsevier Inc. All rights reserved.. -
Challenges and opportunities in multimodal learning research
The trend of multimodal learning, which involves processing and interpreting data through multiple modes such as text, images, and audio, is one aspect that highlights a great frontier in the artificial intelligence (AI) and machine learning (ML) domains. This project explores the technical, practical, and ethical considerations of research studies on multimodality. It starts with preliminary ethical considerations that should drive progress in AI and ML technologies, which include ideas of transparency, accountability, equality, and privacy. Analysis of this paper holds prime importance for moral concerns, as it discusses the issues of bias in AI algorithms and gives strategies that may reduce the level of bias in multimodal patterns. This technology part of the research focuses on technical challenges concerning accountability and transparency in multimodal machine decision-making methodologies. Privacy concerns regarding extensive use of AI and ML have been brought forward, along with the strategy for defensive personal statistics. At each step, an opportunity for innovation and development will be sought and mapped through the complex ethical landscapes of multimodal knowledge research. Through these considerations, this observation attempts to provide a close analysis in which future recommendations and discussions in the realm of AI and multimodal learning are addressed. 2026 Elsevier Inc. All rights reserved. -
Multimodal data analytics for climate and water resources management
The incorporation of multimodal data analytics into climate and water resource management has become a groundbreaking strategy for tackling intricate environmental issues. This chapter examines the importance of integrating various data sourcesincluding satellite imagery, weather sensors, textual reports, and social media feedsto develop a comprehensive perspective on climate and water systems. It addresses key challenges such as data heterogeneity, computational demands, and potential biases while showcasing the significant benefits of multimodal data in enhancing predictive modeling and decision-making. The discussion extends to advanced methodologies for data acquisition, integration, and feature extraction, with a focus on machine learning and deep learning techniques. Additionally, real-world applications in climate prediction, drought and flood forecasting, and water quality assessment are explored. The chapter also considers ethical concerns and future advancements in multimodal analytics, emphasizing the importance of responsible data utilization and innovative research to strengthen climate adaptation and water resource management efforts. 2026 Elsevier Inc. All rights reserved. -
Surveillance and security: integrating video, audio, and sensor data
This period of rapid technological development offers enormous prospects for enhancement in security and surveillance systems through integration of sensor, audio, and video data. Conventional surveillance systems, that rely primarily on video feeds, have been undergoing the process of incorporating audio and sensor data to provide monitoring solutions that are more comprehensive and accurate. In addition to this the research provides an overview of the possibility of strengthening security in a number of settings. It also deals with the strategy involved in the integration of multimodal data as well as challenges associated with this process. This expansion is crucial for the deployment of these integrated systems. Moreover, in the application of machine learning (ML) and artificial intelligence (AI) technologies in making sense of such vast volumes of data as output by the respective systems, the detection of threats with higher precision and speed has improved. Complex sensor, audio, and video pattern analysis are theoretically possible using models of AI and ML. This study culminates in the presentation of a list of prospective future research subjects. It is meant to integrate technology innovation with social aspects such as privacy and security systems. Among them are the development of lightweight algorithms, ethical frameworks, and integration solutions that are efficient. 2026 Elsevier Inc. All rights reserved. -
Multimodal data analytics for social media and user behavior
The introduction of social media has prompted an explosion of diverse data types, such as textual content, pix, videos, and audio. Traditional unimodal analysis techniques do not effectively depict the difficult interactions between exceptional fact sorts and consumer sports. Multimodal data analytics addresses this difficulty through fusing one-of-a-kind modalities to unlock deeper insights, improving the accuracy and scope of social media analysis. This bankruptcy looks into the significance of multimodal facts in understanding consumer behavior, sentiment analysis, content material engagement assessment, and trend prediction. The bankruptcy starts off with the exploration of various sources of statistics in social media analytics, together with textual content posts, visual content, and consumer interactions. It then explores preprocessing and function extraction strategies utilized to prepare raw multimodal data for the usage of gadget gaining knowledge of. In-intensity methodologies, inclusive of natural language processing for text evaluation, computer vision for photo and video interpretation, and speech recognition for audio processing, are expounded in extraordinary detail. Integration of these modalities via fusion techniquesearly fusion, past due fusion, and hybrid modelsis also explored. 2026 Elsevier Inc. All rights reserved. -
Multimodal data generation and synthesis
Multimodal data generation and synthesis have become new promising directions in artificial intelligence research, making possible the combination and transformation of the different data modalities: text, images, audio, and video. In this chapter a look will be made about the principles, methodologies, applications, and challenges linked with multimodal data, bringing attention to the current trends and needs regarding multimodal systems and systems approaches to tackle complex real-world challenges across the medical and health care, autonomous systems, entertainment, and extended reality (XR) fields. The chapter introduces multimodal data and discusses how the approach differs from unimodal methods, considering the merits of working with multiple data forms. Multimodal systems present richer and more comprehensive representations that lead to better decision-making and provide a better interaction with users. The complexity due to alignment, synchronization, and representation of diverse modes is inherently difficult. This section further discusses state-of-the-art techniques in multimodal synthesis, especially focusing on generative approaches like generative adversarial networks (GANs), variational autoencoders (VAEs), and diffusion models. These methods are shown to facilitate cross-modal transformations, such as text-to-image or audio-to-video synthesis, driving innovation in artificial intelligence and beyond. Applications of multimodal data synthesis are discussed in detail, underscoring its transformative impact. In health care, for instance, synthesizing medical images paired with textual annotations enhances diagnostic accuracy and medical training. Autonomous vehicles benefit from the integration of LiDAR, visual, and auditory data, enabling robust decision-making in real-time environments. Similarly, in entertainment and XR, multimodal synthesis is redefining content creation, making immersive experiences more personalized and dynamic. The chapter also delves into novel applications such as multimodal translation, exemplified by systems that translate sign language into spoken text, fostering inclusivity and accessibility. Despite its potential, multimodal synthesis faces critical challenges, including bias in data and models, privacy concerns, and the ethical implications of creating hyperrealistic synthetic data, such as deepfakes. All these raise pressing concerns, and addressing these requires robust privacy-preserving techniques, bias-mitigation strategies, and stringent ethical guidelines. 2026 Elsevier Inc. All rights reserved. -
Multimodal sentiment analysis: integrating text, image, and audio
Multimodal sentiment analysis aims to integrate text, images, and audio information to provide a more comprehensive understanding of human emotions and opinions. This chapter reviews key aspects of multimodal sentiment analysis, including feature extraction techniques, fusion methods, modeling approaches, and applications. For feature extraction the chapter discusses lexical, syntactic, and semantic features for text; visual attributes and facial expressions for images; and acoustic properties for audio. Three primary fusion techniques are examined: early fusion, which combines features before classification; late fusion, which integrates outputs from unimodal models; and model-based fusion, which learns joint representations across modalities. The chapter explores traditional machine learning and deep learning modeling approaches, highlighting the effectiveness of neural architectures like CNNs and RNNs. Key application areas discussed include social media analysis, emotion recognition, intelligent transportation, and education. The chapter also outlines future research directions, such as crossmodal learning, multimodal pretraining, and explainable AI. As multimodal data increases, sentiment analysis techniques that can effectively integrate information across modalities will become increasingly crucial for understanding human emotions and opinions in diverse contexts. This review provides a comprehensive overview of current approaches and emerging trends in this rapidly evolving field. 2026 Elsevier Inc. All rights reserved. -
Challenges in preprocessing and normalization of heterogenous data
In todays information-driven landscape, the exposure to information from a wide range of sources, such as social media, financial transactions, healthcare records, and Internet of Things (IoT) sensors. This variety, while providing valuable insights, also brings significant issues. Heterogeneous information, which varies in formats, structures, and scales, requires careful management to ensure it can be effectively used in analytics and machine learning. Key steps in this process include prehandling and standardization. Prehandling involves cleaning and preparing the information by tackling issues like missing values, identifying and eliminating noise (inaccurate or irrelevant information), and addressing inconsistencies. Normalization, in contrast, converts the information into a uniform format and scale, facilitating easier comparison and analysis. However, managing diverse information effectively comes with several issues. Missing information is a frequent problem, and accurately filling in these gaps can be complicated, especially for complex information types. Noise and inconsistencies can greatly affect the accuracy and reliability of any analysis that follows. Additionally, merging information from various sources with differing formats and structures can be a challenging and time-consuming task. This chapter explores the specific issues faced when prehandling and normalizing different information types, including numerical, categorical, textual, and image information. Real-world examples from India, such as the Aadhaar information base and IoT-enabled smart cities, highlight the practical implications of these issues. By grasping best practices and emerging AI-driven trends, organizations can improve information reliability and enhance decision-making. 2026 Elsevier Inc. All rights reserved. -
Transfer learning in multimodal settings
A powerful machine learning technique in the multimodal environment allows the transmission learning model to adapt to information from one domain to another, which promotes more effective learning in different types of data, including lessons, images, speeches, speeches, and sensor data. This method increases the model's adaptability, reduces the requirement for large marked datasets, and increases performance across domains. It has been used in several domains where multimodal integration is important, such as healthcare, autonomous systems, and natural language processing. Despite the benefits, transmission learning has disadvantages, including high data costs, data shortages, and domain changes. To meet these challenges, model architecture, adaptation strategy, and improvement in dataset growth techniques are necessary. This study examines basic ideas, procedures, and transfer of transfer to multimodal references, and provides insight. Practical use and new development. We show the developing role to learn transfer in improving artificial intelligence (AI) applications by looking at current studies and case studies. As the area develops, a combination of knowledge from many methods will be necessary to create scalable, reliable, and effective AI systems that can handle the problems in the real world. 2026 Elsevier Inc. All rights reserved. -
Feature extraction and fusion techniques for multimodal data
Integrating multimodal information has become crucial in the big data era for developing a thorough knowledge of complex systems and enhancing decision-making in a variety of fields. The importance of feature extraction and fusion strategies in multimodal learning is examined in this chapter, with particular attention paid to the difficulties and approaches involved in merging several data modalities, including text, pictures, audio, and sensor data. It talks about how conventional feature extraction approaches have evolved into more sophisticated ones like deep learning models for picture and audio data and neural embeddings for text. The chapter also explores several fusion tactics, such as early, late, and intermediate fusion, and focuses on how they are used in domains including sentiment analysis, autonomous cars, healthcare, and multimodal search engines. The chapter highlights future directions, such as lightweight architectures and privacy-preserving techniques, while also addressing contemporary issues, such as managing missing data, scalability, and privacy concerns. The chapter provides a thorough grasp of how feature extraction and fusion aid in the creation of multimodal systems that are more precise, effective, and interpretable by looking at these factors. 2026 Elsevier Inc. All rights reserved. -
Modalities in data: understanding text, images, and audio
Data modalities, encompassing diverse forms such as text, audio, image, and video, play a pivotal role in shaping modern data analysis and machine learning applications. Each modality represents information in a unique format, requiring specific processing and interpretation methods. The integration of multiple modalities, known as multimodal data, enhances decision-making and predictive accuracy, particularly in complex systems like sentiment analysis, speech recognition, and medical diagnostics. Deep learning techniques have facilitated the seamless fusion of multimodal data, enabling a more comprehensive understanding across various fields, from healthcare to social media analytics. For example, combining text with images improves sentiment analysis, while integrating audio and video aids in more accurate speech recognition. However, the incorporation of multimodal data presents challenges, including data heterogeneity, synchronization issues, and dimensionality concerns. Data formats differ across modalities, and aligning them for cohesive analysis requires sophisticated algorithms and computational power. Despite these obstacles, multimodal data offers significant benefits, such as enhanced customer experience in business and increased diagnostic accuracy in health care. Furthermore, the rise of large datasets and artificial intelligence (AI) technologies has fueled innovation, enabling the development of more efficient models capable of uncovering intricate relationships within data. This chapter discusses various modalities, their applications, and the technological advancements driving their integration. It also highlights the challenges in multimodal data processing and the solutions being developed to address these complexities, offering valuable insights for businesses, researchers, and AI practitioners. 2026 Elsevier Inc. All rights reserved. -
Ethical considerations in multimodal data collection and analysis
Nowadays, research and industry rely more on collecting and analyzing multimodal data that integrates a host of formats like text, images, audio, and video. Such integration increases the decision-making capabilities and builds insightful information, but equally raises serious ethical issues to be followed up with caution. In multifarious and interrelated datasets, questions about ownership of the data, informed consent, and privacy become much more complex. This has further worsened by the advent of social media and big data analytics, exposing participants to possible harms. This paper discusses the ethical issues arising in such scenarios and calls for strong mechanisms that ensure responsible and just conduct in multimodal research. 2026 Elsevier Inc. All rights reserved. -
Fusion Techniques for medical imaging and clinical data towards precision diagnostics and personalized care
The combination of clinical data with medical imaging has created a revolution in modern healthcare, which provides a clear insight into patient's health sometimes in an earlier stage itself. Magnetic resonance imaging, computed tomography scans, ultrasounds, and X-rays are a few of the medical imaging techniques that offer high-resolution representations of the body's structure and physiological processes. Clinical data, such as physical examinations, test findings, and medical histories, help to contextualize these photographs, which enhances treatment planning and broadens diagnostic discoveries. Fusion approaches combine data from multiple sources to create a unified dataset, making diagnoses more accurate and treatments more personalized. This chapter emphasizes the importance of the fusion of heterogeneous medical data along with various fusion techniques, including deep learning and attention mechanisms, to align medical images with clinical data for meaningful insights. By using fusion techniques, healthcare professionals can make real-time decisions, identify diseases with better accuracy, and derive insights that can lead to actionable treatment or management strategies. Advanced fusion methods enable healthcare providers to obtain a holistic view of a patient's health, allowing advancements in precision medicine and customized treatment plans. 2026 Elsevier Inc. All rights reserved. -
Future trends in multimodal learning: from theory to practical applications
The human ability to seamlessly integrate information from various sensory channelssight, sound, touchhas long inspired researchers in artificial intelligence. This ability to learn from and reason with multimodal datatext, speech, vision, and moreforms the core of multimodal learning. This abstract delves into the theoretical foundations of multimodal learning, explores its cutting-edge advancements, and critically examines the path toward practical applications in diverse fields. At its heart, multimodal learning seeks to exploit the inherent complementarity between different data modalities. Text, for instance, provides rich semantic meaning, while visual data offers valuable context. Speech captures the nuances of emotion and prosody often absent in text. By learning from these combined modalities, models can achieve a more comprehensive understanding of the world around them. Recent years have witnessed significant progress in multimodal learning architectures. Deep learning approaches, particularly convolutional neural networks and recurrent neural networks, have proven adept at capturing complex relationships within individual modalities. New architectures like multimodal transformers further bridge the gap by allowing models to learn joint representations across different modalities. These advancements pave the way for a paradigm shift in areas like computer vision, natural language processing, and robotics. In computer vision, multimodal learning allows models to not only recognize objects in images but also understand the context and actions depicted. By incorporating textual descriptions or speech narratives alongside visual data, models can achieve better scene understanding, image captioning, and action recognition. This has applications in autonomous vehicles, where understanding traffic signs, pedestrians, and road conditions is crucial, and in video surveillance systems, where interpreting visual cues alongside spoken dialogue improves anomaly detection. 2026 Elsevier Inc. All rights reserved.