An Innovative 3D-IDE Design and Adaptive Signal Extraction Algorithm for Efficient Ovarian Cancer Detection

This work presents a facile, ultrasensitive, and selective chemiresistive biosensor assisted by an adaptive signal extraction algorithm (ASEA) for detecting vimentin, a potential biomarker for ovarian cancer detection. The low-cost device, fabricated on a PCB substrate through sacrificial copper etching, features a 3D-IDE design with interwoven comb-like structures mimicking the natural symmetry of a droplet. An unequal count of positive and negative concentric circle fingers ensures a uniform, higher electric field over the sensor's surface, as verified by COMSOL Multiphysics 3D simulation. This optimal electric field elegantly reflects changes in the IV characteristics, even with minor variations in surface charge density from probe-target interactions. Graphene oxide, functionalized with a heterobifunctional linker, serves as the sensing nanomaterial. A detailed study examines the device's response with interdigitated gaps from 30 to 150 μm. A wider interdigitated gap introduces greater variability in the response across different voltage levels. To address this, the Python-based ASEA meticulously scans the entire voltage range, isolating the segment of the signal that best balances both the intensity and extension for optimal expression. ASEA boosts the limit of detection (LOD) by five times for sensors with gaps of over 100 μm. The biosensor achieves a minimum LOD of 9.45 fg mL^-1 and a highest sensitivity of (Δ<i>R</i>/<i>R</i>) 477 ng mL^-1 cm². The biosensor exhibits excellent selectivity, strong resistance to interfering proteins, and commendable stability, showcasing its potential for on-field and point-of-care testing.