Laser synthesis emerges as a suitable technique to produce ligand-free nanoparticles, alloys and functionalized nanomaterials for catalysis, imaging, biomedicine, energy and environmental applications. In the last decade, laser ablation and nanoparticle generation in liquids has proven to be a unique and efficient technique to generate, excite, fragment and conjugate a large variety of nanostructures in a scalable and clean way. In this work, we give an overview on the fundamentals of pulsed laser synthesis of nanocolloids and new information about its scalability towards selected applications. Biomedicine, catalysis and sensing are the application areas mainly discussed in this review, highlighting advantages of laser-synthesized nanoparticles for these types of applications and, once partially resolved, the limitations to the technique for large-scale applications.

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.

Intraocular lenses (IOLs) are commonly implanted after surgical removal of a cataractous lens. A variety of IOL materials are currently available, including collamer, hydrophobic acrylic, hydrophilic acrylic, PHEMA copolymer, polymethylmethacrylate (PMMA), and silicone. High-quality polymers with distinct physical and optical properties for IOL manufacturing and in line with the highest quality standards on the market have evolved to encompass medical needs. Each of them and their packaging show unique advantages and disadvantages. Here, we highlight the evolution of polymeric materials and mainly the current state of the art of the unique properties of some polymeric systems used for IOL design, identifying current limitations for future improvements. We investigate the characteristics of the next generation of IOL materials, which must satisfy biocompatibility requirements and have tuneable refractive index to create patient-specific eye power, preventing formation of posterior capsular opacification.

The development of MnO₂-graphene (MnO₂-GR) composite by microwave irradiation method and its application as an electrode material for the selective determination of serotonin (SE), popularly known as “happy chemical”, is reported. Anchoring MnO₂ nanoparticles on graphene, yielded MnO₂-GR composite with a large surface area, improved electron transport, high conductivity and numerous channels for rapid diffusion of electrolyte ions. The composite was characterized by X-ray photoelectron spectroscopy (XPS), Raman spectroscopy and scanning electron microscopy (SEM) for assessing the actual composition, structure and morphology. The MnO₂-GR composite modified glassy carbon electrode (GCE) exhibited an excellent electrochemical activity towards the detection of SE in phosphate buffer saline (PBS) at physiological pH of 7.0. Under optimum conditions, the modified electrode could be applied to the quantification of serotonin by square wave voltammetry over a wide linear range of 0.1 to 800 µM with the lowest detection limit of 10 nM (S/N = 3). The newly fabricated sensor also exhibited attractive features such as good anti-interference ability, high reproducibility and long-term stability.

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.