9th International Nanoscience and Nanotechnology Conference, Ankara, Türkiye, 27 - 29 Ağustos 2025, ss.351, (Özet Bildiri)
This thesis presents the design and development of a compact, cost-effective, modular, and portable spectrophotometric device tailored for the
simultaneous detection and quantification of multiple metabolites associated with metabolic disorders, particularly diabetes mellitus and chronic kidney
disease. Although considerable progress has been achieved in point-of-care diagnostic technologies and Lab-on-a-Chip platforms, the sensitivity,
specificity, and reproducibility of existing analytical methods remain inadequate for reliable clinical deployment. The proposed device leverages
optoelectronic technologies to address these limitations by enabling rapid and accurate analysis of eight clinically relevant metabolites: glucose, urea,
creatinine, sodium, chloride, calcium, bilirubin, and lactate, using minimal blood volume.
The analytical principle of the device is based on absorption spectroscopy, wherein the wavelength-dependent absorption characteristics of target analytes
are measured. Central to the system is the Hamamatsu C12666MA spectral sensor, a CMOS-based linear photodiode array capable of acquiring absorption
data across the 340–780 nm spectral range with a resolution of 15 nm. Illumination is provided by an array of high-intensity LEDs with discrete emission
peaks at 405 nm, 460 nm, 505 nm, 515 nm, 530 nm, 565 nm, 580 nm, and 610 nm. The optical and electronic components are integrated into a sealed
enclosure with dimensions of 40×40×20 cm, ensuring controlled environmental conditions and minimizing ambient light interference. Custom electronic
circuitry has been designed and simulated to optimize LED driving and signal acquisition performance.
A bespoke graphical user interface, developed in Python, facilitates real-time data acquisition, calibration, noise reduction, and spectral visualization.
Initial validation studies will be conducted using phosphate-buffered saline (PBS) solutions spiked with known metabolite concentrations, followed by
tests with synthetic blood matrices to emulate physiological conditions. Analytical performance parameters, including accuracy, precision, linearity,
detection limit, and interference susceptibility—will be rigorously assessed to establish the system’s reliability.
The resulting data will be benchmarked against conventional laboratory reference methods to evaluate clinical compatibility. By integrating miniaturized
optoelectronic components with intelligent software, the proposed spectrophotometric system represents a novel diagnostic platform with potential
applications in both clinical and home-based monitoring of metabolic disorders. This work was supported by the TÜBİTAK 1004 - Center of Excellence
Support Program (Project No 22AG017).