An\nInnovative 3D-IDE Design and Adaptive Signal Extraction\nAlgorithm for Efficient Ovarian Cancer Detection

This\nwork presents a facile, ultrasensitive, and selective chemiresistive\nbiosensor assisted by an adaptive signal extraction algorithm (ASEA)\nfor detecting vimentin, a potential biomarker for ovarian cancer detection.\nThe low-cost device, fabricated on a PCB substrate through sacrificial\ncopper etching, features a 3D-IDE design with interwoven comb-like\nstructures mimicking the natural symmetry of a droplet. An unequal\ncount of positive and negative concentric circle fingers ensures a\nuniform, higher electric field over the sensor’s surface, as\nverified by COMSOL Multiphysics 3D simulation. This optimal electric\nfield elegantly reflects changes in the IV characteristics, even with\nminor variations in surface charge density from probe-target interactions.\nGraphene oxide, functionalized with a heterobifunctional linker, serves\nas the sensing nanomaterial. A detailed study examines the device’s\nresponse with interdigitated gaps from 30 to 150 μm. A wider\ninterdigitated gap introduces greater variability in the response\nacross different voltage levels. To address this, the Python-based\nASEA meticulously scans the entire voltage range, isolating the segment\nof the signal that best balances both the intensity and extension\nfor optimal expression. ASEA boosts the limit of detection (LOD) by\nfive times for sensors with gaps of over 100 μm. The biosensor\nachieves a minimum LOD of 9.45 fg mL<sup>–1</sup> and a highest\nsensitivity of (Δ<i>R</i>/<i>R</i>) 477\nng mL<sup>–1</sup> cm<sup>2</sup>. The biosensor exhibits excellent\nselectivity, strong resistance to interfering proteins, and commendable\nstability, showcasing its potential for on-field and point-of-care\ntesting.