Single-molecule detection (SMD) has reformed analytical science by enabling the direct observation of individual molecular events, thus overcoming the limitations of ensemble-averaged measurements. This review presents a comprehensive analysis of the principles, devices, and emerging materials that have shaped the current landscape of SMD. We explore a wide range of sensing mechanisms, including surface plasmon resonance, mechanochemical transduction, transistor-based sensing, optical microfiber platforms, fluorescence-based techniques, Raman scattering, and recognition tunneling, which offer distinct advantages in terms of label-free operation, ultrasensitivity, and real-time responsiveness. Each technique is critically examined through representative case studies, revealing how innovations in device architecture and signal amplification strategies have collectively pushed the detection limits into the femtomolar to attomolar range. Beyond the sensing principles, this review highlights the transformative role of advanced nanomaterials such as graphene, carbon nanotubes, quantum dots, MnO₂ nanosheets, upconversion nanocrystals, and magnetic nanoparticles. These materials enable new transduction pathways and augment the signal strength, specificity, and integration into compact and wearable biosensing platforms. We also detail the multifaceted applications of SMD across biomedical diagnostics, environmental monitoring, food safety, neuroscience, materials science, and quantum technologies, underscoring its relevance to global health, safety, and sustainability. Despite significant progress, the field faces several critical challenges, including signal reproducibility, biocompatibility, fabrication scalability, and data interpretation complexity. To address these barriers, we propose future research directions involving multimodal transduction, AI-assisted signal analytics, surface passivation techniques, and modular system design for field-deployable diagnostics. By providing a cross-disciplinary synthesis of device physics, materials science, and real-world applications, this review offers a comprehensive roadmap for the next generation of SMD technologies, poised to impact both fundamental research and translational healthcare.

Artificial intelligence (AI) and machine learning (ML) are transforming medical diagnostics, but human nail, an easily accessible and rich biological substrate, is still not fully exploited in the digital health field. Nail pathologies are easily diagnosed, non-invasive disease biomarkers, including systemic diseases such as anemia, diabetes, psoriasis, melanoma, and fungal diseases. This review presents the first big synthesis of image analysis for nail lesions incorporating AI/ML for diagnostic purposes. Where dermatological reviews to date have been more wide-ranging in scope, our review will focus specifically on diagnosis and screening related to nails. The various technological modalities involved (smartphone imaging, dermoscopy, Optical Coherence Tomography) will be presented, together with the different processing techniques for images (color corrections, segmentation, cropping of regions of interest), and models that range from classical methods to deep learning, with annotated descriptions of each. There will also be additional descriptions of AI applications related to some diseases, together with analytical discussions regarding real-world impediments to clinical application, including scarcity of data, variations in skin type, annotation errors, and other laws of clinical adoption. Some emerging solutions will also be emphasized: explainable AI (XAI), federated learning, and platform diagnostics allied with smartphones. Bridging the gap between clinical dermatology, artificial intelligence and mobile health, this review consolidates our existing knowledge and charts a path through yet others to scalable, equitable, and trustworthy nail based medically diagnostic techniques. Our findings advocate for interdisciplinary innovation to bring AI-enabled nail analysis from lab prototypes to routine healthcare and global screening initiatives.

This study pioneers the optimization of Friction Stir Welding (FSW) for C71500 copper-nickel alloys, delivering significant enhancements in weld quality and corrosion resistance while bridging a critical gap in integrated mechanical, microstructural, and corrosion analyses essential for demanding marine and offshore applications. A robust tungsten carbide tool was employed on a vertical milling machine with refined parameters (1130 rpm rotational speed, 30 mm min ⁻¹ traverse speed, 5 KN axial force) which yielded defect-free welds with unprecedented performance. The evaluated mechanical properties, demonstrated superior retention of around 90% of the tensile strength (342 MPa after welding versus 380 MPa before welding). The Vickers hardness tester showed a value between 198–262 HV, indicating profound grain refinement. Microstructural assessments per ASTM E112 confirmed dynamic recrystallization, reducing weld zone grain size to 12.4 μm from the parent metal’s 35.2 μm, enhancing the structural integrity. ASTM G48 corrosion testing of the specimen in 6% FeCl ₃ revealed negligible weight loss (0.5566 g) and zero pitting, surpassing conventional welds by up to 75%. The corrosion rate was calculated using the Tafel extrapolation curves and was found to be 1260 mpy. Further the Taguchi optimization revealed the highest S/N ratio for spindle rotation, i.e. 1350 rpm and H13 tool material demonstrated the highest corrosion resistance among all. These results establish FSW’s dominance in preserving alloy properties and enabling cost-effective solutions in harsh marine environments and high-stake industries.