## 1 Introduction  
As global life expectancy increases, the demographic shift towards aging populations has precipitated a surge in age-related health conditions, with neurodegenerative diseases, particularly Alzheimer's disease (AD), emerging as a significant public health challenge. AD, characterized by complex pathology and progressive cognitive decline, is the leading cause of dementia, accounting for 60% to 70% of the nearly 10 million new dementia cases reported annually worldwide, according to the latest report from the World Health Organization (WHO) [1]. The socioeconomic burden is equally alarming, with the global cost of dementia surpassing USD 1.3 trillion in 2019, nearly half of which falls on informal caregivers [2]. These staggering figures underscore the urgent need for advancements in early diagnosis and disease management. Despite decades of research, AD remains incurable, with existing treatments addressing only symptomatic relief while failing to target underlying disease mechanisms. Moreover, the diagnostic landscape is similarly limited. AD is notoriously difficult to diagnose in its preclinical stages, during which pathological changes--such as A\(\beta\) plaque deposition and tau protein tangles--begin years before clinical symptoms manifest [3; 4; 5]. These early biomarkers are crucial for intervention yet notoriously difficult to detect. Nonetheless, they have become a staple in the defining criteria for the diagnosis of AD [6]. While these biomarkers are pivotal for early intervention, their detection remains a significant challenge due to the limitations of current diagnostic tools. Traditional diagnostic methods--relying on clinical observations and post mortem analyses--have evolved into new diagnostic modalities, including neuroimaging and biochemical testing, which have significantly enhanced our ability to detect these biomarkers in vivo. These advancements represent significant progress, but they are often limited by high costs, low sensitivity and specificity, and barriers to widespread clinical adoption, which hampers a wide spread to clinical applications [7; 8; 9]. Therefore, to attain these challenges, there is an urgent need for the creation of innovative diagnostic platforms that combine real-time, sensitive biomarker detection with scalability and accessibility. Biosensors, particularly optical biosensors, have emerged as promising solutions for addressing these diagnostic gaps [10]. Optical biosensors utilize light-matter interactions to detect biomolecular signatures, with high sensitivity and specificity. They can detect biomolecular interactions originating from several intertanglements of light such as absorbance, transmittance, and fluorescence levels. These biosensors have the ability to provide non-invasive, real-time molecular insights, which makes them particularly suitable for applications in preclinical and clinical Alzheimer's research [11]. Particularly, fluorescence-based biosensors offer a powerful approach for the visualization and quantification of biomarkers, providing high sensitivity and real-time detection capabilities [12]. These advantages stem from the unique optical properties of fluorescence, which enable highly specific signals, and offer rapid and non-destructive analysis, making them well suited for continuous monitoring in biological systems, which further enhances their applicability in multiplexed assays for Alzheimer's biomarker detection [13; 14; 15]. In the context of AD, optical sensors enable the detection of A\(\beta\) by identifying the binding of a fluorescent probe to the target biomarker. Undoubtedly, biosensors attain one aspect of this research framework. However, it is also essential to recognize the recent advancements in microfluidics and sensor miniaturization, which have enabled the development of complex integrated platforms, such as the brain-on-a-chip (BoC) [16; 17].