Carbon-Based Polymer Nanocomposites for High-Performance Applications II

The Advancing Role of Nanocomposites in Cancer Diagnosis and Treatment

The relentless pursuit of effective cancer diagnosis and treatment strategies has led to the rapidly expanding field of nanotechnology, with a specific focus on nanocomposites. Nanocomposites, a combination of nanomaterials with diverse properties, have emerged as versatile tools in oncology, offering multifunctional platforms for targeted delivery, imaging, and therapeutic interventions. Nanocomposites exhibit great potential for early detection and accurate imaging in cancer diagnosis. Integrating various imaging modalities, such as magnetic resonance imaging (MRI), computed tomography (CT), and fluorescence imaging, into nanocomposites enables the development of contrast agents with enhanced sensitivity and specificity. Moreover, functionalizing nanocomposites with targeting ligands ensures selective accumulation in tumor tissues, facilitating precise imaging and diagnostic accuracy. On the therapeutic front, nanocomposites have revolutionized cancer treatment by overcoming traditional challenges associated with drug delivery. The controlled release of therapeutic agents from nanocomposite carriers enhances drug bioavailability, reduces systemic toxicity, and improves overall treatment efficacy. Additionally, the integration of stimuli-responsive components within nanocomposites enables site-specific drug release triggered by the unique microenvironment of the tumor. Despite the remarkable progress in the field, challenges such as biocompatibility, scalability, and long-term safety profiles remain. This article provides a comprehensive overview of recent developments, challenges, and prospects, emphasizing the transformative potential of nanocomposites in revolutionizing the landscape of cancer diagnostics and therapeutics. In Conclusion, integrating nanocomposites in cancer diagnosis and treatment heralds a new era for precision medicine.

Machine Learning for Property Prediction and Optimization of Polymeric Nanocomposites: A State-of-the-Art

Recently, the field of polymer nanocomposites has been an area of high scientific and industrial attention due to noteworthy improvements attained in these materials, arising from the synergetic combination of properties of a polymeric matrix and an organic or inorganic nanomaterial. The enhanced performance of those materials typically involves superior mechanical strength, toughness and stiffness, electrical and thermal conductivity, better flame retardancy and a higher barrier to moisture and gases. Nanocomposites can also display unique design possibilities, which provide exceptional advantages in developing multifunctional materials with desired properties for specific applications. On the other hand, machine learning (ML) has been recognized as a powerful predictive tool for data-driven multi-physical modelling, leading to unprecedented insights and an exploration of the system's properties beyond the capability of traditional computational and experimental analyses. This article aims to provide a brief overview of the most important findings related to the application of ML for the rational design of polymeric nanocomposites. Prediction, optimization, feature identification and uncertainty quantification are presented along with different ML algorithms used in the field of polymeric nanocomposites for property prediction, and selected examples are discussed. Finally, conclusions and future perspectives are highlighted.

Carbon-Based Nanomaterials 3.0

Carbon-based nanomaterials are currently attracting a lot of interest in many fields, ranging from medicine and biotechnology to electronics, energy storage, and sensing applications [...]