From Sensing Mechanisms to System Co‐Design: Graphene‐Based Sensors and Readout Circuits
Graphene-based materials combining outstanding electrical, thermal, optical and mechanical properties have demonstrated remarkable potential for high-performance sensing across optoelectronic, molecular, mechanical, magnetic and thermal applications. However, practical graphene sensing often involves relatively small stimulus-induced changes in diverse electrical quantities, requiring dedicated readout electronics for accurate detection and conditioning. Existing reviews rarely treat sensing mechanisms and readout architectures in a unified system co-design context, which leaves a gap between device demonstrations and deployable systems. To bridge device transduction and circuit interfacing, a three-level PS-EQ-RF framework is proposed, which maps physical stimuli (PS) to electrical quantities (EQ) and to readout features (RF). Guided by the framework, this review organizes graphene-based sensors according to electrical output characteristics into four categories: voltage-source, current-source, resistive (RS), and capacitive (CS) sensors. Key circuit modules, representative readout topologies, and practical implementations are analyzed for each category. We further provide system-level co-design guidelines, including metric mapping, architecture selection, low-noise design, and mitigation of nonidealities, and discuss the evolution, remaining bottlenecks, and future directions of graphene-based sensing systems. This review aims to provide a systematic view of graphene-based sensing systems and to accelerate the transition of graphene-based sensing technologies from laboratory prototypes to practical applications.
Authors
- Ziran Zhao (ORCID: https://orcid.org/0000-0003-2158-2639)
- G. Q. Li
- Meng Chen (ORCID: https://orcid.org/0000-0001-9595-7806)
- Jinduo Zhang
- Ruifeng Liu
- Yingxin Wang
Institutions
- National Archives (GB)
- Tsinghua University (CN)
Publication Details
- Journal
- Advanced Science
- Published
- 2026-08-27
- DOI
- https://doi.org/10.1002/advs.77374
- Primary Topic
- Graphene research and applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Beijing Nova Program