## 3 Results and Discussion
### Microfluidic-Sensing Studies for Amyloid-Beta Analysis
#### 3.2.3 Optimizing Binding Studies of CRANAD-2 with Amyloid-Beta in a Microfluidic Device
Furthermore, the fluorescence characteristics of the fibrils, including both intensity and the linear relationship with A\(\beta\) concentration, remain consistent. This indicates reliability across platforms and confirms that quantification is possible. As a result, fibrils were established as the baseline for subsequent studies and further analysis. This provides a preliminary set for the use of microfluidic systems in detecting AD and lays the groundwork for further exploration of this optical sensing tool in advanced microfluidic devices. Comparative Analysis of Fluorescence Intensity of Bonded CRANAD-2 in Macroscale and Microscale Experiments  
Following the confirmation of CRANAD-2's binding specificity and consistent fluorescence response to A\(\beta\) fibrils, the transition from macroscale to microfluidic platforms marks a pivotal advancement in biomarker detection strategies for Alzheimer's disease. By establishing fibrils as the primary analyte, the upcoming comparative analysis highlights fluorescence intensity trends, sensitivity, and the inherent advantages of microfluidic systems. To quantitatively assess these variations, three linear regression analyses were performed, characterizing the relationship between A\(\beta\) concentration and CRANAD-2 fluorescence intensity across both experimental platforms. The resulting plots are presented in Figure 6. The corresponding regression equations are presented in Table 2. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**ID** & **Setup** & **Concentration** & **Equation** & \(\mathbf{R^{2}}\) \\ \hline (a) & Macroscale & 100–1000 & Fluorescence\({}_{\text{macro}}=0.32\cdot[A\beta]+1057.93\) & 0.996 \\ (b) & Microscale & 100–1000 & Fluorescence\({}_{\text{micro}}=0.22\cdot[A\beta]+329.20\) & 0.998 \\ (c) & Microscale & 5–100 & Fluorescence\({}_{\text{micro}}=1.62\cdot[A\beta]+200.71\) & 0.925 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Linear regression equations and coefficients of determination for fluorescence measurements under macroscale and microscale setups. Figure 5: Bar graphs of normalized fluorescence intensity difference for oligomeric (orange) and fibrillar (blue) forms of A\(\beta\) in presence of 25 uM CRANAD-2, measured at emission peak wavelength of 736 nm to compare fluorescence behavior across different experimental setups and concentration ranges: (**a**) macroscale experiments with A\(\beta\) concentrations ranging from 100 nM to 1000 nM; (**b**) microscale experiments with A\(\beta\) concentrations ranging from 100 nM to 1000 nM; (**c**) microscale experiments with A\(\beta\) concentrations ranging from 5 nM to 100 nM. A comparison of the slopes between the same concentration ranges but in different setup scales reveals distinct differences in sensitivity. The macroscale setup demonstrated higher sensitivity (0.32) in the 100-1000 nM range, likely due to its ability to maximize fluorescence capture through larger interaction volumes. Conversely, the microfluidic platform exhibited slightly lower sensitivity (0.22) in the same range. The relative reduction in sensitivity by 31.25% in the microfluidic system suggests that miniaturization influences fluorescence signal detection due to optical constraints and increased noise, as mentioned previously. However, despite these factors, the system retained a clear and quantifiable fluorescence response, demonstrating its feasibility for biomarker detection in microfluidic platforms. The regression results also highlight differences in baseline fluorescence intensities between platforms. Additionally, the fluorescence response in the lower microfluidic concentration range (5-100 nM) exhibited a markedly higher slope (1.62). The \(R^{2}\) values obtained for these regressions provide further insights into the linearity and reliability of fluorescence intensity measurements. For the macroscale experiments, the \(R^{2}\) value of 0.996 indicates an excellent fit, reflecting strong consistency and reproducibility.