The increasing demand for rapid, cost-effective, and reliable diagnostic tools in personalized and point-of-care medicine is driving scientists to enhance existing technology platforms and develop new methods for detecting and measuring clinically significant biomarkers. Humanity is confronted with growing risks from emerging and recurring infectious diseases, including the influenza virus, dengue virus (DENV), human immunodeficiency virus (HIV), Ebola virus, tuberculosis, cholera, and, most notably, SARS coronavirus-2 (SARS-CoV-2; COVID-19), among others. Timely diagnosis of infections and effective disease control have always been of paramount importance. Plasmonic-based biosensing holds the potential to address the threat posed by infectious diseases by enabling prompt disease monitoring. In recent years, numerous plasmonic platforms have risen to the challenge of offering on-site strategies to complement traditional diagnostic methods like polymerase chain reaction (PCR) and enzyme-linked immunosorbent assays (ELISA). Disease detection can be accomplished through the utilization of diverse plasmonic phenomena, such as propagating surface plasmon resonance (SPR), localized SPR (LSPR), surface-enhanced Raman scattering (SERS), surface-enhanced fluorescence (SEF), surface-enhanced infrared absorption spectroscopy, and plasmonic fluorescence sensors. This review focuses on diagnostic methods employing plasmonic fluorescence sensors, highlighting their pivotal role in swift disease detection with remarkable sensitivity. It underscores the necessity for continued research to expand the scope and capabilities of plasmonic fluorescence sensors in the field of diagnostics.

Sensitive, accurate, and early detection of biomarkers is essential for prompt response to medical decisions for saving lives. Some infectious diseases are deadly even in small quantities and require early detection for patients and public health. The scarcity of these biomarkers necessitates signal amplification before diagnosis. Recently, we demonstrated single-molecule-level detection of tuberculosis biomarker, lipoarabinomannan, from patient urine using silver plasmonic gratings with thin plasma-activated alumina. While powerful, biomarker binding density was limited by the surface density of plasma-activated carbonyl groups, that degraded quickly, resulting in immediate use requirement after plasma activation. Therefore, development of stable high density binding surfaces such as high binding polystyrene is essential to improving shelf-life, reducing binding protocol complexity, and expanding to a wider range of applications. However, any layers topping the plasmonic grating must be ultra-thin (<10 nm) for the plasmonic enhancement of adjacent signals. Furthermore, fabricating thin polystyrene layers over alumina is nontrivial because of poor adhesion between polystyrene and alumina. Herein, we present the development of a stable, ultra-thin polystyrene layer on the gratings, which demonstrated 63.8 times brighter fluorescence compared to commercial polystyrene wellplates. Spike protein was examined for COVID-19 demonstrating the single-molecule counting capability of the hybrid polystyrene-plasmonic gratings.

Accurate biomolecule quantification at ultralow concentrations remains a major challenge because conventional ensemble assays report population averaged signals and therefore lose sensitivity in low-abundance regimes. Single molecule fluorescence counting can overcome this limitation by converting emission into discrete digital events, but practical implementation is often hindered by manual inspection, limited reproducibility, and the complexity of machine learning based analysis. Here, we present a simple and general analytical framework for rapid single molecule detection based on a deterministic threshold algorithm that exploits the temporal signature of fluorescence blinking. The method operates directly on time resolved fluorescence image stacks, applies median filter-based noise suppression, and identifies candidate single molecule events from consecutive frame-to-frame intensity transitions without the need for training data or model fitting. Applied to Alexa Fluor 488, Alexa Fluor 647, and Rhodamine Red–X datasets, the approach reproduced the concentration dependent trends observed by manual counting, while providing more standardized detection under weak signal and high background conditions. Dye specific operating thresholds yielded robust counting behavior and preserved approximately linear concentration dependent response across the tested range. Compared with manual analysis, which required inspection of only selected grid regions, the automated workflow processed full movie stacks and reduced analysis time from ~3 h to ~20 min per concentration, corresponding to an approximately 9-fold gain in efficiency. These results establish an interpretable, computationally lightweight, and experimentally adaptable strategy for fluorescence single molecule counting that can support rapid diagnostics and provide a practical foundation for future extensions in automated localization, clustering, and real time molecular analysis.