A unified computational-experimental paradigm for rational design of next-generation electrochemical biosensors: the IECM framework

Abstract Electrochemical sensors face critical challenges in achieving ultra-high sensitivity and specificity within complex biological matrices. To address these limitations, this study proposes an integrated electrochemical computational model (IECM), a novel end-to-end framework that couples kinetics, electrochemistry, and signal amplification sub-modules to predict and optimize sensor performance. Unlike previous descriptive reviews, this work validates the IECM through rigorous experimentation using reduced graphene oxide MXene/ anchors gold nanoparticles nanocomposite interfaces. The limit of detection was determined based on the standard definition (3sigma/slope, where sigma is the standard deviation of the blank signal), yielding exceptionally low values of 0.12 fg ml −1 for prostate-specific antigen and 5.0 × 10 −18 M for microRNA-21, with corresponding calibration curve slopes confirming high signal-to-noise ratios. In complex matrices such as serum and saliva, the sensor demonstrated robust anti-interference capabilities, achieving recovery rates of 92.0% ± 3.5% and cross-reactivity rates below 3.5%. Long-term stability tests indicated a signal retention of 68.7% after 8 weeks under moderate storage conditions. Furthermore, double-blind clinical trials against gold-standard assays revealed a high concordance rate (κ = 0.93), confirming the model’s predictive accuracy and the sensor’s clinical potential. This research establishes a unified computational-experimental paradigm for the rational design of next-generation biosensors.