Evidence map›Paper›PMID 42658572›Full record

ReviewAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026

From Sensing Mechanisms to System Co-Design: Graphene-Based Sensors and Readout Circuits.

Jinduo Zhang, Meng Chen, Guanchen Li, Ruifeng Liu, Yingxin Wang, Ziran Zhao

Abstract readReview
In one paragraph

Review in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Jinduo ZhangDepartment of Engineering Physics, Tsinghua University, Beijing, China.ORCID https://orcid.org/0009-0009-8737-5377
Meng ChenNational Engineering Research Center of Dangerous Articles and Explosives Detection Technologies, Beijing, China.ORCID https://orcid.org/0000-0001-9595-7806
Guanchen LiDepartment of Engineering Physics, Tsinghua University, Beijing, China.ORCID https://orcid.org/0009-0007-9883-8539
Ruifeng LiuDepartment of Engineering Physics, Tsinghua University, Beijing, China.
Yingxin WangDepartment of Engineering Physics, Tsinghua University, Beijing, China.
Ziran ZhaoDepartment of Engineering Physics, Tsinghua University, Beijing, China.ORCID https://orcid.org/0000-0003-2158-2639

Funding

Beijing Nova Program 20240484648Beijing Nova Program 20240484671National Key R&D Program of China 2023YFF0715000National Natural Science Foundation of China 62327804National Natural Science Foundation of China 62375149
6 · The paper itself

Abstract

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.

Indexed as

graphenePS–EQ–RF frameworkreadout circuitssensors

Identifiers

PMID42658572
PMCPMC13521197

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.