Perovskite Nanoparticles as an Electrochemical Sensing Platform for Detection of Warfarin

Advancements in electrochemical sensor technology for warfarin detection: a comprehensive review

Warfarin (WA), the most prescribed oral anticoagulant in patients with atrial fibrillation, is widely utilized for the treatment of various diseases, such as vascular disorders, venous thrombosis, and atrial fibrillation. However, its abnormal concentration is linked to a variety of disorders and diseases, namely bleeding while brushing teeth, skin tissue issues, hair loss, and chest pain. Therefore, WA monitoring in blood serum is vital due to its narrow therapeutic window. Accordingly, WA determination has been conducted using various methods, such as high-performance liquid chromatography, fluorescent, surface-enhanced Raman scattering, and electrochemical methods. Electrochemical methods have received considerable attention due to their outstanding selectivity, remarkable sensitivity, great time efficiency, and cost-effectiveness. Herein, a comprehensive literature survey on electrochemical methods for determining WA is presented. This review discusses the development of various chemically modified electrodes (CMEs). These CMEs, such as multi-wall carbon nanotubes, molecularly imprinted polymers, metal oxide nanoparticles, and polymer nanocomposites, owing to their morphology and structure, high selectivity, high conductivity, and high volume/area ratio, are designed to overcome the limitations of bare electrodes, which include reduced electrocatalytic activity, slower electron transfer rates, and poor sensitivity. Also, this review presents the advantages and disadvantages of various modified electrodes applied in WA detection.

Machine learning in nanozymes: from design to application

Nanozymes, a distinctive class of nanomaterials endowed with enzyme-like activity and kinetics akin to enzyme-catalysed reactions, present several advantages over natural enzymes, including cost-effectiveness, heightened stability, and adjustable activity. However, the conventional trial-and-error methodology for developing novel nanozymes encounters growing challenges as research progresses. The advent of artificial intelligence (AI), particularly machine learning (ML), has ushered in innovative design approaches for researchers in this domain. This review delves into the burgeoning role of ML in nanozyme research, elucidating the advancements achieved through ML applications. The review explores successful instances of ML in nanozyme design and implementation, providing a comprehensive overview of the evolving landscape. A roadmap for ML-assisted nanozyme research is outlined, offering a universal guideline for research in this field. In the end, the review concludes with an analysis of challenges encountered and anticipates future directions for ML in nanozyme research. The synthesis of knowledge in this review aims to foster a cross-disciplinary study, propelling the revolutionary field forward.