Electroanalytical Transduction and Robustness Trade‐Offs in Graphene‐Based microRNA Biosensors: A System‐Level Review

Graphene‐based electrochemical biosensors have demonstrated exceptional sensitivity for microRNA detection, frequently achieving femtomolar to attomolar limits of detection under controlled conditions. However, their translation into clinically reliable devices remains limited by unresolved trade‐offs between analytical sensitivity and operational robustness. This study hypothesizes that biosensor performance is governed not by the intrinsic properties of graphene alone, but by a system‐level interplay between electroanalytical transduction, interfacial chemistry, signal amplification strategies, and device architecture. To investigate this, a structured literature review (2014–2025) was conducted using Methodi Ordinatio and ProcKnow‐C, resulting in a curated portfolio of 13 experimental studies. The results show that ultra‐low detection limits (10 −15 –10 −18 M) are typically achieved through multilayer architectures and enzymatic amplification, which introduce instability, signal drift, and reduced reproducibility. In contrast, simpler configurations such as label‐free or nonenzymatic systems exhibit improved stability and reproducibility, albeit at the expense of sensitivity. A key contribution of this work is the identification of a systematic sensitivity–robustness trade‐off, driven by factors including charge–transfer resistance modulation, probe immobilization density, matrix interference, and fabrication complexity. Notably, robustness metrics such as reusability, long‐term stability, and performance in biological matrices are inconsistently reported, limiting cross‐study comparability and hindering clinical translation. This review advances a system‐level electroanalytical framework that shifts the design paradigm from sensitivity maximization toward integrated performance optimization. The proposed perspective provides actionable guidelines for developing graphene‐based biosensors with improved reliability, scalability, and applicability in point‐of‐care diagnostics.