Bio-derived smart nanostructures for efficient biosensors

While metal nanoparticles are being increasingly used in many sectors of the economy, there is growing interest in the biological and environmental safety of their production. The main methods for nanoparticle production are chemical and physical approaches that are often costly and potentially harmful to the environment. The present review is devoted to the possibility of metal nanoparticle synthesis using plant extracts. This approach has been actively pursued in recent years as an alternative, efficient, inexpensive, and environmentally safe method for producing nanoparticles with specified properties. This review provides a detailed analysis of the various factors affecting the morphology, size, and yield of metal nanoparticles. The main focus is on the role of the natural plant biomolecules involved in the bioreduction of metal salts during the nanoparticle synthesis. Examples of effective use of exogenous biomatrices (peptides, proteins, and viral particles) to obtain nanoparticles in plant extracts are discussed.

This review highlights the recent advances using deep eutectic solvents (DESs), deep eutectic ionic liquids (DEILs), low-melting mixtures (LMMs) or low transition temperature mixtures (LTTMs) as green media as well as catalysts in organic reactions. Deep eutectic solvents (DESs), also known as deep eutectic ionic liquids (DEILs) or low-melting mixtures (LMMs) or low transition temperature mixtures (LTTMs) in the literature, have become more and more attractive in recent years due to their interesting properties and benefits, such as low cost of components, easy to prepare, tunable physicochemical properties, negligible vapor pressure, non-toxicity, biorenewability and biodegradability. These eutectic mixtures have been widely used as green and sustainable media as well as catalysts in many chemical processes. This review focuses on recent advances using DESs in organic reactions including addition reactions, cyclization reactions, replacement reactions, multicomponent reactions, condensation reactions, oxidation reactions, and reducing reactions.

To manage the COVID-19 pandemic, development of rapid, selective, sensitive diagnostic systems for early stage β-coronavirus severe acute respiratory syndrome (SARS-CoV-2) virus protein detection is emerging as a necessary response to generate the bioinformatics needed for efficient smart diagnostics, optimization of therapy, and investigation of therapies of higher efficacy. The urgent need for such diagnostic systems is recommended by experts in order to achieve the mass and targeted SARS-CoV-2 detection required to manage the COVID-19 pandemic through the understanding of infection progression and timely therapy decisions. To achieve these tasks, there is a scope for developing smart sensors to rapidly and selectively detect SARS-CoV-2 protein at the picomolar level. COVID-19 infection, due to human-to-human transmission, demands diagnostics at the point-of-care (POC) without the need of experienced labor and sophisticated laboratories. Keeping the above-mentioned considerations, we propose to explore the compartmentalization approach by designing and developing nanoenabled miniaturized electrochemical biosensors to detect SARS-CoV-2 virus at the site of the epidemic as the best way to manage the pandemic. Such COVID-19 diagnostics approach based on a POC sensing technology can be interfaced with the Internet of things and artificial intelligence (AI) techniques (such as machine learning and deep learning for diagnostics) for investigating useful informatics via data storage, sharing, and analytics. Keeping COVID-19 management related challenges and aspects under consideration, our work in this review presents a collective approach involving electrochemical SARS-CoV-2 biosensing supported by AI to generate the bioinformatics needed for early stage COVID-19 diagnosis, correlation of viral load with pathogenesis, understanding of pandemic progression, therapy optimization, POC diagnostics, and diseases management in a personalized manner.