Pure, mixed and doped metal oxides (MOX) have attracted great interest for the development of electrical and electrochemical sensors since they are cheaper, faster, easier to operate and capable of online analysis and real-time identification. This review focuses on highly sensitive chemoresistive type sensors based on doped-SnO₂, RhO, ZnO-Ca, Sm_x-CoFe_2−xO₄ semiconductors used to detect toxic gases (H₂, CO, NO₂) and volatile organic compounds (VOCs) (e.g., acetone, ethanol) in monitoring of gaseous markers in the breath of patients with specific pathologies and for environmental pollution control. Interesting results about the monitoring of biochemical substances as dopamine, epinephrine, serotonin and glucose have been also reported using electrochemical sensors based on hybrid MOX nanocomposite modified glassy carbon and screen-printed carbon electrodes. The fundamental sensing mechanisms and commercial limitations of the MOX-based electrical and electrochemical sensors are discussed providing research directions to bridge the existing gap between new sensing concepts and real-world analytical applications.

Traditional pharmacotherapy suffers from multiple drawbacks that hamper patient treatment such as antibiotic resistances or low drug selectivity and toxicity during systemic applications. Some functional hybrid nanomaterials are designed to handle the drug release process under remote-control. More attention has recently been paid to synthetic polyelectrolytes for their intrinsic properties which allow them to rearrange into compact structures, ideal to be used as drug carriers or probes influencing biochemical processes. The presence of Ag nanoparticles (NPs) in the Poly methyl acrylate (PMA) matrix leads to an enhancement of drug release efficiency, even using a low-power laser whose wavelength is far from the Ag Surface Plasmon Resonance (SPR) peak. Further, compared to the colloids, the nanofiber-based drug delivery system has shown shorter response time and more precise control over the release rate. The efficiency and timing of involved drug release mechanisms has been estimated by the Weibull distribution function, whose parameters indicate that the release mechanism of nanofibers obeys Fick’s first law while a non-Fickian character controlled by diffusion and relaxation of polymer chains occurs in the colloidal phase.

Graphene-based materials are intriguing nanomaterials with applications ranging from nanotechnology-related devices to drug delivery systems and biosensing. Multifunctional graphene platforms were proposed for the detection of several typical biomarkers (i.e., circulating tumor cells, exosomes, circulating nucleic acids, etc.) in liquid biopsy, and numerous methods, including optical, electrochemical, surface-enhanced Raman scattering (SERS), etc., have been developed for their detection. Due to the massive advancements in biology, material chemistry, and analytical technology, it is necessary to review the progress in this field from both medical and chemical sides. Liquid biopsy is considered a revolutionary technique that is opening unexpected perspectives in the early diagnosis and, in therapy monitoring, severe diseases, including cancer, metabolic syndrome, autoimmune, and neurodegenerative disorders. Although nanotechnology based on graphene has been poorly applied for the rapid diagnosis of viral diseases, the extraordinary properties of graphene (i.e., high electronic conductivity, large specific area, and surface functionalization) can be also exploited for the diagnosis of emerging viral diseases, such as the coronavirus disease 2019 (COVID-19). This review aimed to provide a comprehensive and in-depth summarization of the contribution of graphene-based nanomaterials in liquid biopsy, discussing the remaining challenges and the future trend; moreover, the paper gave the first look at the potentiality of graphene in COVID-19 diagnosis.

Polymers are widely employed in several fields thanks to their wide versatility and the easy derivatization routes. However, a wide range of commercial polymers suffer from limited use on a large scale due to their inert nature. Nowadays, acrylate and methacrylate polymers, which are respectively derivatives of acrylic or methacrylic acid, are among the most proposed materials for their useful characteristics like good biocompatibility, capping ability toward metal clusters, low price, potentially recyclability and reusability. Here, we discuss the advantages and challenges of this class of smart polymers focusing our attention on their current technological applications in medical, electronic, food packaging and environmental remediation fields. Furthermore, we deal with the main issue of their recyclability, considering that the current commercial bioplastics are not yet able to meet the global needs as much as to totally replace fossil-fuel-based products. Finally, the most accredited strategies to reach recyclable composites based on acrylic polymers are described.

NMR measurements combined with chemometrics allow achieving a great amount of information for the identification of potential biomarkers responsible for a precise metabolic pathway. These kinds of data are useful in different fields, ranging from food to biomedical fields, including health science. The investigation of the whole set of metabolites in a sample, representing its fingerprint in the considered condition, is known as metabolomics and may take advantage of different statistical tools. The new frontier is to adopt self-learning techniques to enhance clustering or classification actions that can improve the predictive power over large amounts of data. Although machine learning is already employed in metabolomics, deep learning and artificial neural networks approaches were only recently successfully applied. In this work, we give an overview of the statistical approaches underlying the wide range of opportunities that machine learning and neural networks allow to perform with accurate metabolites assignment and quantification.Various actual challenges are discussed, such as proper metabolomics, deep learning architectures and model accuracy.