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Graphene Transistors for In Vitro Detection of Health Biomarkers

Advanced Functional Materials

May 2023
33(31)

DOI:10.1002/adfm.202301948

Authors:

Derong Kong

Fudan University

Yunqi Liu

Albemarle

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Biomarkers are primary indicators for precise diagnosis and treatment. The early identification of health biomarkers has been sustained by the evolutionary success in sensor technologies. Among them, graphene field‐effect transistor (GFET) biosensors have exhibited major advantages such as an ultrashort response time, high sensitivity, easy operation, capability of integration, and label‐free detection. Owing to the atomic thickness, graphene restricts charge carrier flow merely at the material surface and responds to foreign stimuli directly, leading to effective signal acquisition and transmission. Here, this review summarizes the latest advances in GFET biosensors in a comprehensive manner that contains the device design, working principle, surface functionalization, and proof‐of‐concept applications. It provides a comprehensive survey of GFET biosensors with regard to biomarker analysis at the single‐device level and integrated prototypes that include wearable sensors, biomimetic systems, healthcare electronics, and diagnostic platforms. Moreover, there is discussion on the long‐standing research efforts and outlook for the future development of GFET sensor systems from lab to fab.

Working principles of GFET biosensors. a) Schematic illustration of a liquid‐gate GFET sensor. b) The analytes include proteins, nucleic acids, viruses, bacteria, etc. The probes include aptamers, antibodies, enzymes, CRISPR/Cas, etc. c) Typical ambipolar transfer characteristics of graphene. d) Sensing principle on the graphene surface: the binding of negatively (positively) charged analytes induced negative (positive) shifts of VCNP. e) Potential diagram of the electrical double layer model. The arrow in the dipole shows the electric field generated by the positive charge of the protein on the gate surface. f) Relative change in the normalized signal at different analyte concentrations. (a) Adapted with permission. [¹⁵] Copyright 2021, American Chemical Society. (e) Reproduced under the terms of CC BY‐NC license.[⁴⁶] Copyright 2021, The Authors, published by the American Association for the Advancement of Science.

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Functionalization strategies of GFET biosensors. a,b) Channel functionalization via a) bifunctional linkers such as PASE and b) edge defects on patterned graphene. c–e) Gate functionalization via c) gold‐thiol binding, d) tetrahedral DNA nanostructures, and e) extended gate modification. (a) Reproduced under the terms of CC BY license. [⁶⁶] Copyright 2018, The Authors, published by American Chemical Society. (b) Reproduced under the terms of CC BY license. [⁶²] Copyright 2023, The Authors, published by MDPI. (c) Reproduced under the terms of CC BY‐NC license. [⁴⁶] Copyright 2021, The Authors, published by the American Association for the Advancement of Science. (d) Adapted with permission. [⁶³] Copyright 2021, American Chemical Society. (e) Reproduced under the terms of CC BY license. [⁶⁰] Copyright 2021, The Authors, published by Springer Nature.

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Antifouling strategies of GFET biosensors. a–c) Schematic illustration of nonspecific binding on a) bare graphene, b) graphene modified with a BSA layer, and c) a DNA‐TDN layer. d) Antifouling strategy in the COF/graphene FET biosensors. The red circle shows the chelating effect at the sensing surface. e) Scheme illustrating the hydrogel‐based encapsulation of sensor electrodes. (a–c) Reproduced under the terms of CC BY license.[¹⁴] Copyright 2022, The Authors, published by Springer Nature Limited. (d) Adapted with permission.[⁷²] Copyright 2020, Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim. (e) Reproduced under the terms of CC BY license.[⁷¹] Copyright 2022, published by Springer Nature Limited.

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GFET nucleic acid sensors. a,b) Crumpled GFET nucleic sensor: a) Schematic illustration of detecting target and nontarget dsDNA near the crumpled graphene surface. b) Response contrast for amplified and nonamplified primer DNA. c,d) CRISPR‐based GFET nucleic sensor: c) Working principle of the Cas9‐CS04 enzyme in the presence of CS04 (left panel) or WTC11 genomic DNA (right panel). d) Response contrast for DNA samples with a single‐base mismatch. e,f) DNA‐based Y dual‐probe GFET sensor: e) Device configuration and sensing mechanism at the sensing interface of GFETs. f) Signal response with different probes at different SARS‐CoV‐2 cDNA concentrations. (a, b) Adapted with permission. [⁴⁰] Copyright 2020, WILEY‐VCH Verlag GmbH & Co. KGa. (c, d) Reproduced under the terms of CC BY license. [⁸⁵] Copyright 2021, The Authors, published Springer Nature. (e, f) Adapted with permission. [⁹¹] Copyright 2021, American Chemical Society.

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GFET protein sensors. a,b) Multiantibody GFET sensor: a) Schematic illustration of the sensing principle on the graphene surface, where the three antibodies bind to different sites of the SARS‐CoV‐2 virus. b) Comparison of the detection of the diluted clinical sample using multiantibodies and a single antibody. c) Crumpled GFET sensor with a schematic illustration of the binding process. d) Anti‐foiling mechanism of the GFET sensor: Electrostatic actuation enables probes to capture target analytes, while rigid DNA bases prevent nonspecific absorption on the graphene. (a, b) Adapted with permission. [¹⁵] Copyright 2021 American Chemical Society. (c) Adapted with permission. [¹¹⁵] Copyright 2021, Wiley‐VCH GmbH. (d) Reproduced under the terms of CC BY license. [¹⁴] Copyright 2022, The Authors, published by Springer Nature Limited.

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REVIEW

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Graphene Transistors for In Vitro Detection of Health

Biomarkers

Changhao Dai, Derong Kong, Chang Chen, Yunqi Liu, and Dacheng Wei*

Biomarkers are primary indicators for precise diagnosis and treatment. The

early identiﬁcation of health biomarkers has been sustained by the

evolutionary success in sensor technologies. Among them, graphene

ﬁeld-eﬀect transistor (GFET) biosensors have exhibited major advantages

such as an ultrashort response time, high sensitivity, easy operation,

capability of integration, and label-free detection. Owing to the atomic

thickness, graphene restricts charge carrier ﬂow merely at the material surface

and responds to foreign stimuli directly, leading to eﬀective signal acquisition

and transmission. Here, this review summarizes the latest advances in GFET

biosensors in a comprehensive manner that contains the device design,

working principle, surface functionalization, and proof-of-concept

applications. It provides a comprehensive survey of GFET biosensors with

regard to biomarker analysis at the single-device level and integrated

prototypes that include wearable sensors, biomimetic systems, healthcare

electronics, and diagnostic platforms. Moreover, there is discussion on the

long-standing research eﬀorts and outlook for the future development of

GFET sensor systems from lab to fab.

1. Introduction

Health biomarkers are essential biological indicators in clini-

cal diagnostics.[, ] They are associated with human tissue bio-

logical characteristics and can provide valuable information for

early diagnosis, treatment, and prevention of diseases.[– ] Thus,

identifying a health-related biomarker at an early stage is of vi-

tal importance in healthcare applications. Conventional analyt-

ical techniques suer from temporal heterogeneities, invasive

procedures, insucient sensitivity, and time-consuming and so-

phisticated operation.[– ] By comparison, electrical sensing tech-

nology has attracted extensive interest due to its noninvasive,

C. Dai, D. Kong, C. Chen, D. Wei

State Key Laboratory of Molecular Engineering of Polymers

Department of Macromolecular Science

Fudan University

Shanghai , China

E-mail: weidc@fudan.edu.cn

C. Dai, D. Kong, C. Chen, Y. Liu, D. Wei

Laboratory of Molecular Materials and Devices

Fudan Un iver sity

Shanghai , China

The ORCID identiﬁcation number(s) for the author(s) of this article

can be found under https://doi.org/./adfm.

DOI: 10.1002/adfm.202301948

label-free, real-time, and easily-operated de-

tection capability.[,] Over the past few

decades, research eorts have been devoted

to developing advanced electric sensors

such as lab-on-a-chip platforms,[] lateral

ﬂow immune assays,[] electrochemilu-

minescence sensors,[] ﬁeld-eect transis-

tor (FET) sensors,[, ] enzyme-linked im-

munosorbent assays,[] surface plasmon

resonance,[, ] and others.[,, ] Among

them, FET-based sensors exhibit major ad-

vantages such as high integrability, inher-

ent signal ampliﬁcation ability, low energy

expenditure, and ultrashort turn-around

time.[, ] However, traditional FET sensors

are limited by low signal conversion e-

ciency and poor sensor performance be-

cause their bulk channel (−nm) re-

stricts the carrier modulation at the solid-

electrolyte surface.[, ] To address this is-

sue, advances in FET sensors have been

made in replacing the bulk channel by D

materials with atomic thickness (< nm), which in-

clude graphene family materials,[,, ] transition metal

dichalcogenide,[] black phosphorus,[,] and D transition-

metal carbides (MXenes),[–] just to name a few.[– ]

D material-based FETs, especially graphene-based FETs

(GFETs), have demonstrated unique potential in biomarker anal-

ysis and healthcare applications.[,,, ] Due to an ideal van der

Waals surface without dangling bonds, all carriers of graphene

sheets ﬂow merely on the surface and are directly exposed to

foreign stimuli/perturbations, leading to their ultrasensitive re-

sponse to target analytes.[– ] Additionally, the large surface-to-

volume ratio endows graphene with abundant modiﬁcation sites,

which is primary to building integrated and multifunctional sen-

sor platforms.[, ] From these standpoints, GFET-based sensor

technology is quite attractive for sensitive biomarker detection

in healthcare ﬁelds.[– ] Recently, substantial research eorts in

GFET biosensors have been devoted to the following aspects: i)

how to build high-performance sensor units with standardized

methods; ii) how to develop integrated and multifunctional pro-

totypes with the balance of cost, user interface and device reli-

ability; and iii) how to understand the relevancy between sensor

information and clinical characteristics in human testing.[,,, ]

In this review, we systematically discuss recent progress in

GFET biosensors, from fundamental investigation at the single-

device level to integrated systems for health monitoring appli-

cations. This review provides a brief description of the device

conﬁguration, working principle, surface functionalization, and

performance characterization. A comprehensive report of GFET

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Figure 1. Working principles of GFET biosensors. a) Schematic illustration of a liquid-gate GFET sensor. b) The analytes include proteins, nucleic acids,

viruses, bacteria, etc. The probes include aptamers, antibodies, enzymes, CRISPR/Cas, etc. c) Typical ambipolar transfer characteristics of graphene. d)

Sensing principle on the graphene surface: the binding of negatively (positively) charged analytes induced negative (positive) shifts of VCNP. e) Potential

diagram of the electrical double layer model. The arrow in the dipole shows the electric ﬁeld generated by the positive charge of the protein on the

gate surface. f) Relative change in the normalized signal at dierent analyte concentrations. (a) Adapted with permission. [] Copyright , American

Chemical Society. (e) Reproduced under the terms of CC BY-NC license.[] Copyright , The Authors, published by the American Association for the

Advancement of Science.

biosensors with regard to their conceptual applications in non-

invasive biomarker detection is provided. The following sec-

tion summarizes sensing prototypes, including wearable sen-

sors, biomimetic systems, healthcare electronics, and diagnostic

platforms. Finally, we discuss future challenges and put forward

suggestions to build sensor systems that are suitable for home-

care monitoring and healthcare applications.

2. Design and Operation

2.1. Working Principle

A GFET sensor works by monitoring the channel current modu-

lation at the interface, where current modulation can be induced

by external perturbations such as charged adsorption, biological

binding, and chemical reactions.[, ] Under an external gate bias

(Vgs), the gate electrode and the semiconductor act as either plat

of a capacitor. Accordingly, an induced electric ﬁeld perpendic-

ular to the gate electrode forms a conducting channel, in which

the carriers ﬂow from the source to drain.[, ] Figure 1a schemat-

ically shows a liquid-gate GFET sensor with a reference electrode

providing gate voltage through the electrolyte. Composed of all

sp-hybridized carbon atoms, graphene can promote in-plane car-

rier transport at the surface, which makes it extremely sensitive

to the external environment.[, ] To develop GFET biosensors,

biological probes are immobilized on the channel via dierent

linkers to capture target analytes and generate measurable sig-

nals (Figure b). Although many factors, such as the operational

mode, electrode encapsulation and choice of channel materials,

aect the sensing performance, the heart of GFET biosensors

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depends largely on the interface between the graphene chan-

nel and the electrolyte.[,, ] For example, the Debye length (𝜆D)

is one of the key parameters that demonstrates the charge car-

rier’s net electrostatic eect in electrolyte solutions.[, ] Within

𝜆D, the charged analytes will introduce mobility modulation in

the graphene channel. If the distance between the charged ana-

lytes and the graphene channel is larger than 𝜆D, a notable de-

terioration of the sensor response is expected due to the Debye

screening layer.[,] For physiological solutions at room temper-

ature, the Debye length can be calculated via Equation :[]

𝜆D=𝜀𝜀rkBT

NAeI()

where 𝜖is the permittivity of free space; 𝜖ris the dielectric con-

stant; kBis Boltzmann’s constant; Tis the temperature; NAis

Avogadro’s number; eis the elementary charge; and Iis the ionic

strength of the solution.

Three characteristic curves are widely used for operating GFET

biosensors, which include output curves, transfer curves and

time-dependent response curves. First, Figure c indicates a rep-

resentative transfer curve (Ids versus Vgs) of GFETs. The trans-

fer curve is obtained by sweeping Vgs and a constant drain volt-

age, which oers FET performance parameters such as the ﬁeld-

eect mobility, threshold voltage, and charge neutrality point

(CNP).[, ] VCNP (also known as VDirac) is the gate voltage at the

minimum current, where the electron transport almost equals

the hole transport.[,, ] After recognizing positively charged

analytes, VCNP shifted toward a positive potential (right panel

in Figure d), and the device exhibited a p-doping eect in the

electrolyte, and vice versa (left panel in Figure d). In such dop-

ing eects, induced charges move from charged analytes to the

graphene surface directly, thus modulating the Fermi level of

graphene as well as the carrier mobility of GFETs.[] Second, the

output curves (Ids versus Vds) demonstrate the change in drain-

to-source current under sweeping drain voltage at certain Vgs.

The current shifts correspond to the analyte concentrations, and

the linear behavior of the output curves indicates the contact

quality of the electrodes. Third, the time-dependent tests moni-

tor the drain-to-source current Ids, which is aected by the sur-

face potential of the gate electrode.[, ] More speciﬁcally, the

charged analytes induce a potential barrier and an electric dipole

across the electric double layer at the electrolyte/gate interface

(Figure e).[] When |Vds |≪|Vp−Vg,e|, the channel current Ids

of GFETs is given by Equation :[,, ]

Ids =𝜇W

LCiVp−Vg,e +Vds

Vds ∝N()

where μis the hole mobility; Wand Lare the channel width and

length, respectively; Ciis the eective capacitance per unit area

of the GFET; Vpis the pinch-o voltage; Vg,e is the eective gate

voltage; and Nis the number of charged analytes. Accordingly,

the change in Ids is proportional to the number of charged an-

alytes adsorbing on the graphene channel, contributing to the

quantitative detection of targets. However, the FET testing quan-

tity is nontrivial because challenges remain in two aspects: i) the

diculty in obtaining the precise charge number of each binding

process; and ii) the inherent staturation state (Figure f) when all

bioreceptors are occupied by target analytes.[,,, ] Experimen-

tally, the anity between probes and analytes can be obtained via

the Hill-Langmuir equation:[, ]

ΔVDirac

ΔVDirac,max

=Acsensing∕KDn

+csensing∕KDn()

where Ais the saturation response coecient of GFET sensors;

ΔVDirac,max is the largest ΔVDirac, denoted ΔVDirac,max =VDirac,max −

VDirac, (VDirac,max and VDirac, relate to the analyte solution with

maximum and no analytes, respectively); nis the Hill coecient

corresponding to the binding cooperativity; and KDof bioreceptor

probes is extracted from the ﬁtted curve (see the linear region in

Figure f).

2.2. Device Fabrication

A GFET is a three-terminal electronic device that includes source,

drain and gate electrodes, with a semiconductor channel bridg-

ing the source and drain.[] The typical fabrication process of

GFET biosensors includes four steps: i) electrode fabrication; ii)

graphene synthesis, transfer and patterning; iii) surface func-

tionalization; and iv) electrode packaging/encapsulation.[,, ]

For GFET biosensors, the key to fabrication is to construct an

active layer that consists of high-quality graphene and speciﬁc

recognition probes.[, ] Generally, current approaches for syn-

thesizing monolayer graphene can be categorized by top-down

fabrication (e.g., mechanical/liquid exfoliation), bottom-up fab-

rication (e.g., chemical/physical vapor deposition), and organic

synthesis.[,, ] Due to the low eciency of exfoliation and

poor quality of organic synthesis, the chemical vapor deposition

method has been widely applied in producing high-quality, large-

area, and highly uniform graphene sheets for various sensing

applications.[, ]

2.2.1. Surface Functionalization

Surface functionalization is of vital importance to build high-

performance GFET biosensors.[, ] To date, a large number of

publications have reported various functionalization strategies,

including channel functionalization (e.g., bifunctional linkers via

N-hydroxysuccinimide chemistry,[,] carbon quantum dots,[]

drop-casting strategy,[,] nanoparticles,[ ,] etc.[])andgate

functionalization (e.g., Staphylococcus aureus protein A,[]

sulfhydryl group or gold-thiol binding,[] etc.[,] ). Figure 2a,b

demonstrates two representative modiﬁcation approaches on

the graphene channel. As one of the most widely-used bifunc-

tional linkers, -pyrenebutyric acid N-hydroxysuccinimide ester

(PASE) contains a pyrene group absorbed on graphene via 𝜋-𝜋

stacking and a carbonyl group activated with biological probes

(Figure a).[,,,] Noticeably, the excess PASE molecules on the

sensing area will lead to nonspeciﬁc absorption and thereby false

diagnosis in clinical tests. Thus, research eorts have been de-

voted to precise functionalization to achieve controllable implace-

ment of recognition probes on graphene.[,, ] In , Kwon

et al. proposed a linker-free functionalization strategy based on

the edge eects of patterned graphene, indicating a – fold

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Figure 2. Functionalization strategies of GFET biosensors. a,b) Channel functionalization via a) bifunctional linkers such as PASE and b) edge defects

on patterned graphene. c–e) Gate functionalization via c) gold-thiol binding, d) tetrahedral DNA nanostructures, and e) extended gate modiﬁcation.

(a) Reproduced under the terms of CC BY license. [] Copyright , The Authors, published by American Chemical Society. (b) Reproduced under

the terms of CC BY license. [] Copyright , The Authors, published by MDPI. (c) Reproduced under the terms of CC BY-NC license. [] Copyright

, The Authors, published by the American Association for the Advancement of Science. (d) Adapted with permission. [] Copyright , American

Chemical Society. (e) Reproduced under the terms of CC BY license. [] Copyright , The Authors, published by Springer Nature.

enhanced response compared to pristine GFETs modiﬁed with

succinimidyl ester (Figure b).[] More recently, Wang and

coworkers applied tetrahedral DNA nanostructures to build

artiﬁcial electromechanical devices with higher precision and

more functionalities on the graphene channel.[] They also

found that DNA nanostructures helped control the distribution

of probes in the sensing area, thus leading to enhanced per-

formance of GFET biosensors and other bioelectronics. Apart

from channel modiﬁcation methods, valuable progress has also

been made in gate functionalization to develop advanced FET

sensors.[,, ] Liu et al. linked mercaptoacetic acid-modiﬁed

SARS-CoV- antibodies onto a Au gate electrode via gold-thiol

binding, which enables gate voltage pulses and minute-level

detection (Figure c).[] By comparison, Wang et al. used a

DNA-functionalized graphene gate to enrich the detection signal

caused by target analytes, which achieved .-fold improved sen-

sitivity compared with conventional channel functionalization

methods (Figure d).[] Recently, Sheibani et al. developed a

wearable sensory electronic chip based on platinum/graphene

aptamer extended gate GFETs (Figure e).[] In this conﬁgura-

tion, the gate electrode is attached through metal vias above the

aptamer-modiﬁed graphene, which allows the complementary

metal–oxide–semiconductor (CMOS) process for high-level inte-

gration. Furthermore, the extended-gate structure demonstrates

major advantages such as less drift and temperature dependence

in sensing applications.[, ]

2.2.2. Antifouling Strategies

Antifouling strategies are also crucial for maintaining high sen-

sor performance.[, ] Generally, electrode fouling means non-

speciﬁc binding that blocks the active sites of the sensing sur-

face (Figure 3a).[ ,,] The issue of fouling causes rapid loss of

sensitivity and speciﬁcity, especially when GFETs are exposed to

complex detection solutions such as saliva, serum, blood, and

plasma.[, ] To address this issue, research eorts have been

devoted to immobilizing block layers on graphene sensing sur-

faces to prevent unwanted nonspeciﬁc adsorption, which in-

cludes polyethylene glycol (PEG),[– ] bovine serum albumin

(BSA),[] DNA nanostructures,[] ethanolamine,[] etc.[,] As

an example, Wang et al. compared two antifouling strategies

by using BSA and a DNA tetrahedral (DNA-TDN) layer as the

block layer of the sensing area (Figure b,c).[] Both of these

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Figure 3. Antifouling strategies of GFET biosensors. a–c) Schematic illustration of nonspeciﬁc binding on a) bare graphene, b) graphene modiﬁed with

a BSA layer, and c) a DNA-TDN layer. d) Antifouling strategy in the COF/graphene FET biosensors. The red circle shows the chelating eect at the

sensing surface. e) Scheme illustrating the hydrogel-based encapsulation of sensor electrodes. (a–c) Reproduced under the terms of CC BY license.[]

Copyright , The Authors, published by Springer Nature Limited. (d) Adapted with permission.[] Copyright , Wiley-VCH Verlag GmbH & Co.

KGaA, Weinheim. (e) Reproduced under the terms of CC BY license.[] Copyright , published by Springer Nature Limited.

block layers eciently avoid nonspeciﬁc binding and enhance

the long-term stability in solutions. By comparison, Yang et al.

prevented potential foiling by combining covalent organic frame-

works (COFs) with GFET biosensors (Figure d).[] In this con-

ﬁguration, the mesoporous crystalline structure of COFs not only

avoids nonspeciﬁc adsorption but also leads to a chelating eect

for an ultrashort response time ( ms). Most recently, Atkin-

son et al. demonstrated an electrode packaging strategy based

on the combination of nanoparticles and hydrogels, leading to

enhanced sensor sensitivity and high stability at the same time

(Figure e).[] Noticeably, this encapsulation strategy facilitates

ecient electron transfer at the analyte-electrode interface, which

provides design rules to build miniature biosensors with high re-

liability and accuracy in real-world tests.

2.3. Performance Assessment

An ultimate goal of GFET biosensors is to enable home-

care monitoring applications in the digital health era.[– ]

However, variations in material quality, device conﬁguration,

surface functionalization, testing conditions, operation proce-

dures, and signal evaluation lead to uncertainty during prac-

tical applications.[,, ] To better quantify the sensor per-

formance, several characterization metrics are deﬁned as

follows:

Limit of Detection: The limit of detection (LoD) means the

ability to measure the smallest amount or concentration level

of analytes. A common LoD calculation approach recommended

by the IUPAC utilizes the exposure of a sensing device to

known analyte concentrations to generate a calibration curve

(Figure f).[,,] Experimentally, most analytical speciﬁcations

depend on the three-deviation approach (i.e., the analyte concen-

tration at the signal that is three times the background noise level)

to obtain credible output signals (Equation ).[– ]

LoD =𝜎

S()

where 𝜎is the standard deviation of the testing results (i.e.,

𝜎·root-mean-square noise); and Sis the slope of the calibration

curves.

Range of Detection: The range of detection (RoD) is the linear

region in which the detection signal is proportional to the num-

ber/amount of analytes. Notably, the RoD can be obtained from

the smallest to the largest point in the calibration curves rather

than the analytical measuring interval.

Response: Response in this Review is determined by the rela-

tive change of testing signals, including current, resistance, volt-

age, etc. To set the current change as an example, the sensor re-

sponse can be calculated as follows:

response =Isen −I

I

×%()

where Iis the initial current of the sensor and Isen is the mea-

sured current after testing. According to Equation , the response

time is determined as the detection time when the signal arrives

at % of the maximum detection value.

Accuracy: Testing accuracy reﬂects measure deviation with

the true value, denoted as “accuracy =(average – true value)/true

value”. Practically, accuracy is often demonstrated by the area un-

der receiver operating characteristic (ROC) curves.[] In typical

ROC curves, the cross-validation prediction performance is com-

posed of two parts:

Sensitivity =TP

TP +FN ()

Speciﬁcity =TN

TN +FP ()

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where TP represents true positives, TN represents true negatives,

FP represents false positives, and FN represents false negatives.

Thus, speciﬁcity demonstrates the sensor capability to provide

a statistically signiﬁcant signal to target analytes and a negligi-

ble signal to nonspeciﬁc analytes. As a comparison, sensitivity

reﬂects the response to target analytes at a tiny concentration.

Durability: Sensor durability refers to the long-standing qual-

ity or maintenance capability of the initial response under

variable external conditions (e.g. temperature, pressure, time,

moisture).[, ] Equation  gives a calculation approach to obtain

the temperature durability:

responsetemp =responsetemp −response

response

()

where responseis the initial sensor response and responsetemp

is the response temperature measured after storage.

Relative Standard Deviation: The relative standard deviation

(RSD) reﬂects the deviated degree of a set of data. In other words,

the RSD value indicates how precise the average of the experi-

mental results is, which can be calculated as follows:

RSD =SD



X×%()

where SD is the standard deviation of the observations and 

Xis

the mean value of the data.

3. Biomarker Analysis

There is an everlasting demand for sensitive detection of health-

related biomarkers, including nucleic acids,[] proteins,[] small

biomolecules,[, ] and cell-related products.[, ] This section

summarizes the recent progress in GFET biosensors for bio-

chemical sensing and related ﬁtness applications over the past

few decades.

3.1. Nucleic Acid

Nucleic acids are the carriers of genetic information that plays

a crucial role in biological processes.[– ] Early advances in

GFET nucleic acid sensors have been made in the sensitive de-

tection of DNA/RNA in unprocessed biological ﬂuids.[,,,]

For example, Ganguli et al. demonstrated zeptomolar-level de-

tection of enzymatic DNA ampliﬁcation using crumpled GFETs

(Figure 4a).[] The authors took advantage of the crumpled

graphene and Bst polymerase in loop-mediated isothermal am-

pliﬁcation to electrically detect the reduction in the primer

molecules.[] Due to the increased Debye length at the crumpled

graphene surface, the GFET sensor exhibited larger VDirac shifts

than ﬂat graphene-based sensors (Figure b), leading to an LoD

as low as  ×−  ssDNA in buer. Similarly, Huang et al.

applied a reverse transcription-loop-mediated isothermal ampli-

ﬁcation to fabricate a GFET nucleic acid sensor with an LoD of

 copies mL−RNA in a sample.[]

Research progress has also been demonstrated in the rapid nu-

cleic acid detection of unampliﬁed samples.[– ] In , Ha-

jian et al. combined GFETs with clustered regularly interspaced

short palindromic repeat (CRISPR)-associated nuclease (Cas)-

based technologies to enable the digital detection of a target

gene sequence.[] The as-prepared platform achieved -minute

detection of unampliﬁed DNA samples with a sensitivity of

. ×− .[] Shortly after, the same research group modi-

ﬁed the GFET sensor by using dierent Cas variants and ortho-

logues to improve single-nucleotide polymorphisms (SNP) dis-

crimination, to fabricate a single-nucleotide polymorphism chip

and to enable multiplex electrical measurements at the same time

(Termed CRISPR–SNP-Chip, Figure c).[] The CRISPR-SNP-

Chip achieved the digital testing of a target gene with SNP speci-

ﬁcity. As a demonstration, the chip was able to discriminate ge-

nomic DNA from wild-type hiPSCs (termed WTC) and hiPSCs

(termed CS) in the fALS disease model with a statistically sig-

niﬁcant dierence (Figure d). Apart from CRISPR technologies,

researchers have designed other DNA aptamers for ultrasensi-

tive sensing applications.[, ] For example, Kong et al. demon-

strated a GFET nucleic acid sensor based on Y-shaped DNA dual

probes (Y-dual probes, Figure e).[] Owing to the modiﬁed Y-

dual probes that simultaneously bind with ORFab and N genes

of SARS-CoV- nucleic acid, the GFET sensor exhibited a high

recognition ratio and an LoD down to . copy μL−(Figure f),

which is − orders of magnitude lower than existing nucleic

acid assays.

This section summarizes representative applications of GFET

nucleic acid sensors, including quantitative detection, genome

mutation monitoring, and disease diagnosis. Table 1 presents

and compares their sensing performance by categorizing the tar-

get analytes.

3.2. Protein

Proteins consisting of one or more amino acid chains are unique

components in all living organisms and are of vital impor-

tance in physiological systems.[, ] This section summarizes

the latest advances in GFET protein sensors for healthcare ap-

plications such as monitoring enzymatic activity,[] protein-

to-protein interactions,[ ] early disease diagnosis,[ ] and epi-

demic prevention and control,[,, ] and so on.[, ]

Research progress has been made in aptamer-conjugated

strategies for sensitively detecting proteins at dierent

concentrations.[, ] For example, Yu et al. functionalized

solution-gate GFETs with a -base DNA aptamer on the gate

electrode to detect thrombin molecules as low as − .[ ]

After recognizing the heparin-binding site of thrombin, the

DNA aptamer formed a stable G-quadruplex structure and

caused measurable current changes within  s.[ ] Most

recently, Ban et al. developed a DNA aptamer-conjugated GFET

sensor platform to rapidly (< min) detect nucleocapsid, spike,

and receptor-binding domain (RBD) proteins of SARS-CoV-

in saliva samples.[] The hand-held wireless sensor device

exhibited LoDs of . and . PFU mL−for spike and

nucleocapsid proteins, respectively. Furthermore, the sensor

platform was able to distinguish COVID--positive samples

(e.g., Omicron-B.., NY, DG, and YF virtual

variants) from negative controls (i.e., Middle East Respiratory

Syndrome samples) with statistical signiﬁcance, indicating its

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Figure 4. GFET nucleic acid sensors. a,b) Crumpled GFET nucleic sensor: a) Schematic illustration of detecting target and nontarget dsDNA near the

crumpled graphene surface. b) Response contrast for ampliﬁed and nonampliﬁed primer DNA. c,d) CRISPR-based GFET nucleic sensor: c) Working

principle of the Cas-CS enzyme in the presence of CS (left panel) or WTC genomic DNA (right panel). d) Response contrast for DNA samples

with a single-base mismatch. e,f) DNA-based Y dual-probe GFET sensor: e) Device conﬁguration and sensing mechanism at the sensing interface of

GFETs. f ) Signal response with dierent probes at dierent SARS-CoV- cDNA concentrations. (a, b) Adapted with permission. [] Copyright ,

WILEY-VCH Verlag GmbH & Co. KGa. (c, d) Reproduced under the terms of CC BY license. [] Copyright , The Authors, published Springer Nature.

(e, f) Adapted with permission. [] Copyright , American Chemical Society.

potential for wide-range public health applications and real-time

environmental monitoring.[]

Advances in GFET protein sensors have also focused on im-

proving sensor performance with emerging technologies.[, ]

For example, Dai et al. developed a multiantibody functionaliza-

tion strategy to enhance the sensitivity and accuracy of GFET

biosensors. In this conﬁguration, three dierential antibodies

were immobilized on the graphene surface to bind not only RBD

domains but also adjacent sites of SARS-CoV- (Figure 5a).[]

Owing to the cooperative recognition eect, the sensor demon-

strated an antigen-binding anity of . ×−  and an LoD

down to . ×− gmL

−spike protein in artiﬁcial saliva.

Furthermore, the multiantibody GFET sensor was also able to

detect diluted COVID- clinical samples, while conventional

single-antibody sensors exhibited negligible response changes

(Figure b). By comparison, Hwang et al. introduced a special

functionalization strategy based on crumpled graphene to im-

prove sensor performance.[ ] The authors found that the cav-

ernous planar graphene surface can decrease the Debye length,

leading to almost tenfold enhanced sensitivity in .×PBS so-

lution (Figure c). Moreover, the as-prepared GFET with vari-

able crumpled ratios (–%) is a powerful tool to investigate

protein-to-protein interactions.

Although considerable eorts have been made to enhance the

sensitivity of GFET biosensors, several challenges remain before

their successful translation to the medical market. One of the key

challenges in practice is how to avoid nonspeciﬁc adsorption in

unprocessed biological ﬂuids, which would block sensing active

sites, generate background noise, and damage the long-term sta-

bility of GFETs.[, ] To address this issue, Wang et al. designed

a self-assembled sti tetrahedral double-stranded DNA structure

to avoid nonspeciﬁc adsorption from background biomolecules

(Figure d).[] The individual DNA tetrahedron consists of a .-

nm-high base and a ﬂexible probe, where the rigid bases serve

as an anti-fouling layer that keeps nontarget analytes away from

the graphene channel. The developed GFET sensor exhibited

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Tabl e 1 . Sensing Performance of GFET Nucleic Acid Sensors.

Analyte Receptor LoD Detection time [s] Condition Ref.

DNA CRISPR–dCas . ×− < MgClbuer []

DNA CRISPR–dCas ≈. ×− < Buer []

DNA DNA . ×−   PB []

DNA DNA  ×− ≈ PBS []

DNA DNA . ×−   .×PBS []

DNA DNA  ×− ≈ ×PBS []

DNA DNA  ×− ≈ .×PBS []

DNA DNA  ×− > .×PBS []

DNA DNA  ×− < PBS []

DNA DNA ≈×−  N/A DI water []

DNA DNA ≈×− < DI water []

DNA DNA Tweezer  ×− > Tris buer []

DNA G-quadruplex DNAzymes . ×−   PBS, serum []

DNA ssDNA ≈×−   ×SCC []

miRNA DNA .×−  N/A PBS []

miRNA- DNA ≈×−   PBS []

miRNA- ssDNA  ×−   .×PBS []

miRNA- DNA  ×−   ×PBS []

RNA CRISPR-Casa . copies μL− .×PBS []

RNA CRISPR-Casa  ×−   nuclease-free water []

RNA CRISPR-Casa . ×−   ×PBS []

RNA DNA  ×−   serum []

RNA DNA  ×−   serum []

RNA DNA . fg mL− throat swab []

RNA DNA . copies μL− throat swab []

RNA DNA ≈. ×− < PBS []

RNA DNA – copies μL− ×TRB []

RNA DNA .–. copies μL− full artiﬁcial saliva []

RNA DNA .–. copies μL− full artiﬁcial saliva []

RNA DNA  copies μL− full artiﬁcial saliva []

RNA PMO . ×− . ×−   serumthroat swab []

Note: N/A, not available. DI, deionized. PB, phosphate buer. PBS, phosphate-buered saline. PMO, phosphorodiamidate morpholino oligos. PNA, peptide nucleic acid.

SCC, saline-sodium citrate.

almost % accuracy with an LoD down to .–. copies μL−

unampliﬁed SARS-CoV- nucleic acids in bioﬂuids.[] Because

this anti-fouling strategy is not a trade-o between sensitivity and

accuracy, it enables the GFET to be a comprehensive tool for accu-

rate detection of ions ( ×− Hg

+), proteins (e.g.,  ×−

 thrombin), and small biomolecules (e.g.,  ×−  adeno-

sine triphosphate) in complex biological ﬂuids.[]

To date, rigorous progress has been made in developing GFET

protein sensors based on dierent conﬁgurations and functional-

ization strategies. Table 2 summarizes the sensing performance

of selective GFET protein sensors.

3.3. Small Biomolecules

Small biomolecules are key biomarkers in early disease di-

agnosis, including glucose,[] dopamine,[] adenosine

triphosphate,[ ] urea,[ ] and metabolites with an average

molecular weight of < kDa.[,, ] This section analyzes

notable progress in GFET small biomolecular sensors by

categorizing their recognition probes.

Early advances in GFET small biomolecular sensors focused

on enzyme-based functionalization for detecting a vast number

of analytes.[, ] These GFET biosensors work by monitoring

relative pH changes during enzyme-catalyzed hydrolysis.[, ]

For instance, Fenoy et al. developed an acetylcholine sensor based

on an acetylcholinesterase (AchE)-modiﬁed strategy and the elec-

trosynthesis of an amino gmoiety-bearing polymer layer on the

graphene channel (Figure 6a).[ ] In this device, the copoly-

mer poly(-amino-benzylamine-co-aniline) ﬁlm can not only pro-

vide a non-denaturing environment for enzyme immobilization,

but also enhance the pH sensitivity of GFETs (from . to

. μApH

−). Thus, the as-prepared GFET sensor responded

to pH changes induced by enzyme-catalyzed hydrolysis of tar-

get analytes. The LoD reaches . ×− with a sensitivity of

(−. ±.) μAAch

−decade and an average response time of

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Figure 5. GFET protein sensors. a,b) Multiantibody GFET sensor: a) Schematic illustration of the sensing principle on the graphene surface, where the

three antibodies bind to dierent sites of the SARS-CoV- virus. b) Comparison of the detection of the diluted clinical sample using multiantibodies

and a single antibody. c) Crumpled GFET sensor with a schematic illustration of the binding process. d) Anti-foiling mechanism of the GFET sensor:

Electrostatic actuation enables probes to capture target analytes, while rigid DNA bases prevent nonspeciﬁc absorption on the graphene. (a, b) Adapted

with permission. [] Copyright  American Chemical Society. (c) Adapted with permission. [ ] Copyright , Wiley-VCH GmbH. (d) Reproduced

under the terms of CC BY license. [] Copyright , The Authors, published by Springer Nature Limited.

 s (Figure b). Moreover, the enzyme-modiﬁed device exhib-

ited a long-term stability of .% and good device-to-device re-

producibility with an RSD of .%, indicating its high potential

for practical applications. Nevertheless, the covalent attachment

of enzymatic bioreceptors on the graphene surface exhibits sev-

eral drawbacks, including disrupted folding of bioreceptors, ex-

cess active sites, damage to the sensing materials, and so on.[, ]

To address this issue, research eorts have been devoted to de-

veloping nonenzymatic GFET biosensors for the sensitive detec-

tion of small biomolecules.[– ] For example, Danielson et al.

demonstrated a GFET lactose sensor based on gold nanoparti-

cles and speciﬁc protein receptors, which was able to detect as

low as  ×−  analyte over a dynamic range from − to

− mol L−.[ ] Similarly, Ku et al. fabricated a GFET sen-

sor by immobilizing the cortisol monoclonal antibody (C-Mab)

onto the surface of graphene.[ ] In this design, graphene acted

as a transducer that converted antibody-analyte binding interac-

tions into electrical signals. Owing to the high stability of C-Mab

(-hour storage at room temperature), the as-prepared GFET

sensor can be integrated into a prototypical system that enables

continuous and noninvasive cortisol monitoring of the human

body (Figure c). The sensor exhibited an LoD of  pg mL−cor-

tisol in various biological ﬂuids, which is low enough to measure

the cortisol concentration in human tears (Figure d).[ ]

3.4. Cell

Cellular living microenvironments contain a wide range of

biomarkers, including enzymes, nucleic acids, proteins, ions,

small organic molecules, etc.[– ] The monitoring of cellu-

lar activity and products provides key information associated

with cancer, biological reactions, and genetic processes.[,,]

This section summarizes advances in GFET biosensors for

achieving in vitro detection of neural potential and cellular

products.

Research progress in GFET cell sensors has been made for

monitoring extracellular or intracellular potential signals.[, ]

For example, Dupuit et al. combined solution-gate GFETs with a

multicompartment microﬂuidic chip for multimodal recording

of neuron electrical activity (Figure 7a).[ ] The GFETs provided

a highly sensitive and transparent sensing area, while the ﬂuidic

microchannels, synaptic, and somatic chambers helped demon-

strate the spontaneous activity of neuron networks. The spike

signals acquired by GFETs and the customized TiN MEA exhibit

several dierences (lower panels in Figure a). Although the

MEA can resolve the ﬁrst spikes, it had diculty in extracting

secondary spikes from the background noise due to the low

amplitude ability. By comparison, the GFETs realized threefold

higher detection eciency to enable continuous mapping of

neural network topology.[] In addition, Yang et al. detected

weak neural potential signals directly in a complex bioenviron-

ment by combining GFETs with covalent organic frameworks

(COFs).[] In this study, murine neurons were cultured di-

rectly on the graphene channel in medium for long-standing

measurements (Figure b). Due to the COF’s mesoporous

structure, the culture solution can permeate the COF, generate

synaptic potential signals, and ﬁnally arouse rhythmic transient

current changes. Moreover, the device exhibited impressive anti-

fouling capability with a high signal-to-noise ratio of ≈. when

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Tabl e 2 . Sensing Performance of GFET Protein Sensors.

Analyte Receptor LoD Detection time [s] Condition Ref.

A𝛽protein antibody  pg mL−> . m PBS []

A𝛽protein antibody . pg mL− PBS/Human plasma/aCSF []

Anti-diuretic hormone aptamer . ag mL−≈  m PBS, serum []

Anti-p p antigen  ×− – ×PBS []

BGP-C antibody  fg mL− fg mL− pg mL−>  m PB .% serum% serum []

CD antibody  particles μL− .×PBS []

cTnI aptamer . ×− (. pg mL−) N/A PBS, serum []

Cytokine aptamer . ×− ≈ ×PBS, sweat []

Ferritin antibody  ×−  – .×PBS []

GFAP antibody  fg mL− fg mL−< ×PBSHuman plasma []

HBsAg antibody  ×−   serum []

HBsAg antibody  ×− < PBS, serum, saliva []

Hemin catalyst  ×−≈ beef []

Hemoglobin aptamer . ×− . ×− . ×−  – ×PBSSerumurine []

Hepatitis C virus core protein aptamer . ×−  N/A reaction buer []

HPV- E aptamer . ×−≈   PBS, saliva []

IFN-𝛾aptamer . ×− ≈ ×PBS, human sweat []

IFN-𝛾aptamer . ×−  N/A bioﬂuids []

IgE aptamer . ×−   .×PBS []

IL- aptamer . ×− < ×PBS []

IL- aptamer . ×− N/A×PBS, / serum []

IL- aptamer . ×−  N/A bioﬂuids []

IL- aptamer . ×− < ×PBS, human saliva []

Insulin aptamer . ×− N/A×PBS, / diluted serum []

Insulin antibody  ×− ≈ buer []

Ligand odorant receptor  ×− ≈ ×PBS []

MMP- polypeptide  ×− . ×−< buerplasma []

Neuropeptide Y aptamer  ×−   artiﬁcial sweat []

Neuropeptide Y aptamer  ×− < .×PBS []

NT-proBNP antibody  pg mL− PBS []

PSA antibody  pg mL−N/A serum []

PSA aptamer  ×−< PBS []

SARS-CoV- spike antibody antigen . fg mL−(. ×− )  serum []

SARS-CoV- spike antibody antigen . ×−  seconds UTM []

SARS-CoV- antigen antibody  ×−  N/A PBS []

SARS-CoV- antigen antibody  fg mL− ×PBS []

SARS-CoV- antigen aptamer . PFU mL−< ×PBS []

SARS-CoV- N protein antibody  ag mL−< buer []

SARS-CoV- spike protein antibody  fg mL−(≈. ×− ) seconds PBSUTM []

SARS-CoV- spike protein antibody  ×−  seconds UTM []

SARS-CoV- spike protein antibody  pg mL−(. ×– ) < artiﬁcial saliva []

SARS-CoV- spike protein antibody  ×− N/A.×PBS []

SARS-CoV- spike protein antibody  ×−  N/A PBS []

SARS-CoV- spike protein antibody  ag mL−.  ×PBS []

SARS-CoV- spike protein Antibody  fg mL−. PBS []

SARS-CoV- spike protein Antibody  fg mL−N/A PBS []

SARS-CoV- spike protein Antibody  fg mL−seconds ×PBS, artiﬁcial saliva []

streptavidin pyrene–PEG ×−< Trizma buer []

Thrombin Aptamer . ×− ≈ .×PBS []

(Continued)

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Tabl e 2 . (Continued).

Analyte Receptor LoD Detection time [s] Condition Ref.

Thrombin Aptamer  ×−   .×PBS []

Thrombin Aptamer  ×− < ×TRB []

TNF-𝛼Aptamer . ×−  N/A bioﬂuids []

TNF-𝛼Aptamer . ×−   ×PBS []

t-Tau Antibody . pg mL− PBS/Human plasma/aCSF []

t-Tau Antibody  fg mL−N/A ×PBS []

Zika protein Antibody . ×− ≈ ×PBS% serum []

Note: N/A, not available. aCSF, artiﬁcial cerebrospinal ﬂuid. BGP-C, one type of the anti-bone Gla protein. cTnI, cardiac troponin I. GFAP, Glial ﬁbrillary acidic protein. HBsAg,

hepatitis B surface antigen. IgE, immunoglobulin E. IL-, Interleukin . PB, phosphate buer. PBS, phosphate-buered saline. PSA, prostate speciﬁc antigen. UTM, universal

transport medium. TNF, tumor necrosis factor. TRB, thorbim-binding buer.

Figure 6. GFET small biomolecular sensors. a,b) GFET acetylcholine sensor: a) Schematic illustration of the surface functionalization and the

acetylcholinesterase-catalyzed hydrolysis of acetylcholine. AchE, acetylcholinesterase. PABA, poly(-amino-benzylamine-co-aniline) ﬁlm. b) Flow re-

sponse of the sensor at dierent analyte concentrations ( μLmin

−,Vgs =−. V, Vds =. V). c,d) GFET cortisol sensor system: c) A prototype

integrated with GFET, resistor, capacitor, D printed interconnect, and rigid island for signal acquisition and transmission. d) Sensor response to cortisol

concentrations in the buer and artiﬁcial tear solvent. (a, b) Adapted with permission. [ ] Copyright , Elsevier B.V. (c, d) AReproduced under the

terms of CC BY-NC license. [ ], Copyright , The Authors, published by the American Association for the Advancement of Science.

operating in  ppm PEDOT:PSS or complex fouling bioenvi-

ronments (Figure c).[]

Advantages in GFET cell sensors have also been demonstrated

in the sensitive detection of cellular products.[, ] For instance,

Zhang et al. developed an electrolyte-gate GFET managed us-

ing ultrahigh frequencies (UHF) to enable direct detection of

biomarkers in high-salt solutions (Figure d,e).[] To o vercom e

the universal Debye screening eect, the developed GFETs were

operated in reﬂectometry mode (UHF, ≈ GHz) and achieved

an LoD of  ×− streptavidin (– orders of magnitude

lower than conventional dielectric-modulated Si-based FET sen-

sors). Thus, the UHF GFET sensor was able to record the ac-

tion of ≈ cells mm−cardiomyocytes cells cultured on the

graphene channel (Figure e). Notably, the UHF reﬂectometry

detection scheme provides a solution to fully overcome the high-

ionic screening eect (i.e., Debye screening eect) in GFET-

based biomarker detection applications.[, ] Most recently, Lei

et al. demonstrated a photocatalysis-induced renewable reduced

graphene oxide nanosheet FET (rGON-FET) biosensor for real-

time recording of extracellular activity.[] The sensing chan-

nel comprised a sandwich structure of rGON, TiO-rGO, and

Fluo-AM molecules that are speciﬁc to Ca+(Figure f). Be-

cause it exports one Ca+from the cell at the expense of im-

porting three Na+, the as-prepared rGON-FET achieved second-

level detection of Na+concentrations with a remarkable response

of .% (Figure g).[] Moreover, the photo-responsive self-

cleaning TiOenabled regenerative detection of such exocytotic

Ca+in living cells.[ ]

In short, GFET biosensors have exhibited major advantages

in wide-range biomarker detection applications. Noticeably, the

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Figure 7. GFET cell sensors. a) Schematics of the neuron-gated GFET and GMEA, showing the sensor results of neuronal spikes acquired from the

GFET and the GMEA, respectively. b,c) GFET neural sensor: b) Optical microscope image of nerve cells cultured on a graphene channel. c) Sensor

response upon neural electrical signals and ≈ ppm PEDOT:PSS. d,e) Ultrahigh frequency GFET sensor for the detection of cell action: d) Schematic

of the binding process on the graphene surface, where a pyrene linker group was attached to peptide E (i.e., (EIAALEK)). e) Detection of the unique

contractility information of cardiomyocyte cells. Inset: Illustration of a cardiomyocyte cell on GFETs. f,g) The rGON-FET biosensor for cellular Ca+

detection: f) Schematics of the sensing principle, in which high Na+solution stimulates, the Na+/Ca+exchanger opens and Ca+euxes. g) Real-time

detection of Na+released from living cells. Inset: Optical image of human umbilical vein endothelial cells (HUVECs) cultured on the device. (a) Adapted

with permission. [ ] Copyright , Wiley-VCH GmbH. (b, c) Adapted with permission. [] Copyright , WILEY-VCH Verlag GmbH & Co. KGaA,

Weinheim. (d, e) Adapted with permission. [] Copyright , Wiley-VCH GmbH. (f, g) Adapted with permission. [ ] Copyright , Elsevier Ltd.

sensing performance depends largely on the interfacial design

and surface chemistry because most recognition processes and

signal transduction occur at the graphene surface. Thus, further

studies should focus on probe selection and surface functional-

ization strategies to improve the above-mentioned performance

metrics of GFET biosensors, which will accelerate their practical

applications from lab to fab.

4. Prototypical Applications

The healthcare market has witnessed notable progress in GFET-

based sensor systems, including wearable, biomimetic, and

point-of-care electronics.[,, ] In this section, we summarize

the latest advances in fabrication techniques and prototypical

applications of GFET sensor systems. The challenges of these

sensor systems and the possible technical solutions are also

analyzed.

4.1. Wearable Sensor Systems

Wearable sensors attached to the human body can mea-

sure physiological biomarkers for continuous healthcare

monitoring.[,,, ] Among existing wearable sensor sys-

tems, GFETs have demonstrated major advantages such as fast

response speed, reliable material quality, high compatibility

with ﬂexible substrates, and large deformations on human

skin.[,, ] Furthermore, they are capable of detecting various

disease indicators in complex biological ﬂuids and monitoring

human activity in real time.

Attempts in wearable sensors have also been made in health

monitoring by the detection of wide-range biomarkers.[, ] For

example, Gao et al. demonstrated a skin-based ﬂexible gel elec-

trolyte graphene transistor for human sweat monitoring directly

on the skin (Figure 8a).[ ] The constructed GFET epidermal

sensor realized noninvasive and continuous detection of glucose

with a dynamic range from  ×−to . ×−.[ ] In addi-

tion, Sheibani et al. reported a wearable sensory electronic chip

based on a platinum/graphene aptamer extended gate FET for

the identiﬁcation of cortisol in biological buers.[] The device

has a LoD of  ×−  in physiological ﬂuids with cortisol

concentrations of  ×−to  ×−. Although these GFET

biosensors enable sensitive detection of various biomarkers, their

single-analyte detection ability is not sucient for practical appli-

cations.

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Figure 8. GFET-based wearable sensor systems. a) A wearable GFET sensor incorporated with gel electrolyte for noninvasive glucose monitoring. b)

Ultraﬂexible GFET biosensors with a schematic illustration of aptamer functionalization on the graphene surface (upper panel). Photographs of the

ﬂexible device conformably attached onto the ﬁnger and artiﬁcial eyeball (lower panel). c,d) Intelligent aptameric DGTFET wireless biosensing device: c)

Image of the DGTFET device worn on the participant’s forehead. The four wires in the left panel are the ) sensing channel signal, ) ground operation,

) gate voltage, and ) reference channel signal. d) Time-resolved measurement of TNF-𝛼in human sweat. e–g) GFET-based eyeball sensor systems:

e) The sensor ﬁts the ﬁnger entirely. f) Image of the GFET sensor attached to an artiﬁcial eye. g) ΔVDirac as a function of the L-cysteine concentration in

artiﬁcial tears. Error bars are determined by the standard deviation of  measurements. (a) Adapted fwith permission. [ ] Copyright , Elsevier B.V.

(b) Reproduced under the terms of CC BY license.[ ] Copyright , The Authors, published by MDPI. (c, d) Adapted with permission. [ ] Copyright

, Wiley-VCH GmbH. (e–g) Adapted with permission.[ ] Copyright , Wiley-VCH GmbH.

To address this issue, Hao et al. developed a wearable GFET

sensor based on a polyethylene terephthalate substrate for

minute-level detection of multiplexed cytokine biomarkers, in-

cluding IFN-𝛾,TNF-𝛼and IL-.[ ,] The sensor maintains

its sensitivity and response after withstanding large deforma-

tions on the human ﬁnger or an artiﬁcial eye (Figure b). Inte-

grated with an Android platform, the GFET sensor enables re-

liable signal acquisition and transmission in on-site detection

of clinical samples from asymptomatic or mild COVID- pa-

tients (Figure c). The sensor platform enabled multiplexed de-

tection of IFN-𝛾,TNF-𝛼and IL- with LODs of . ×− ,

. ×−, and . ×− , respectively (Figure d). No-

tably, the testing accuracy, device reliability, and operation cost

of such sensor systems require further evaluation before appli-

cations for population-wide tests.[, ] More recently, Huang et al.

developed ultraﬂexible and transparent GFET sensor platforms

attached to artiﬁcial eyes for continuous L-cysteine monitoring in

the human body (Figure e,f).[ ] After -cycle deformations of

fold (°), bend (radii  μm), and shrink (%) recovery, this

portable sensor system still enables consistent and reliable detec-

tion of . ×− L-cysteine in artiﬁcial tears (Figure g).[ ] In

addition, the sensor platforms based on the WO/Au/WOelec-

trode exhibit negligible inﬂuence on human eyesight, compared

with devices covered by conventional Au electrodes.

Advantages have also been demonstrated in GFET

biosensors to monitor human activity and control robotic

movement.[,, ] In , Park et al. reported a wearable

GFET pressure sensor for breath and heart rate detection fab-

ricated by using vertically aligned, position- and size-controlled

arrays of ZnO nanotubes grown on a graphene layer.[] The

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graphene layer acts as a substrate for the catalyst-free growth

of ZnO nanotubes and serves as a ﬂexible conduction channel

connecting the ZnO nanotubes to the metal electrodes. This

wearable pressure sensor based on the hybridization of D ZnO

nanotubes with D graphene ﬁlms shows a high-pressure sensi-

tivity (. kPa−) and can detect weak ﬂows of inert gases. The

nanotube sensor can be used to monitor respiration and heart

rate when attached to the volunteer’s mid-chamber and wrist.[ ]

Similarly, Paul et al. developed a wearable GFET pressure sensor

based on crumpled graphene ﬂake network channels for robotic

hand-free control.[ ] The device was ﬁxed to the temple area of

the face and controlled by the respective temporal muscles.[ ]

4.2. Biomimetic Sensor Systems

Biomimetic and bioinspired sensing devices have attracted ex-

tensive attention over the past few decades.[– ] They are the

key component for intelligent healthcare systems in our everyday

life.[,, ] Here, we summarize the latest advances in GFET-

based biomimetic sensor systems, including bioelectronic eyes,

noses, tongues, etc.

Attempts have been made in GFET sensor systems for devel-

oping electronic noses or tongues.[– ] Electronic noses can

convert interactions with odor molecules into electrical signals,

such as potential changes and surface charge modulation. Al-

though the concept of electronic noses (e-noses) dates back to

the s, their applications were limited by misbinding infor-

mation from odor changes in mixtures.[ ] To address this issue,

Hayasaka et al. developed a GFET-based e-nose to obtain high se-

lectivity under complex conditions (Figure 9a). This GFET elec-

tronic nose recorded the gas-sensitive conductivity proﬁle and

decoupled it into four unique physical parameters. These pa-

rameters were further projected onto a feature space as four-

dimensional output vectors, which were associated with dierent

target gases by machine learning analysis. As a demonstration,

the combination of this GFET e-nose with trained pattern recog-

nition algorithms enables almost % accuracy when testing

water, methanol and ethanol vapors individually (Figure b).[ ]

By comparison, electronic tongues generally detect taste-related

biomarkers in biological ﬂuids (Figure c).[– ] For exam-

ple, Ahn et al. demonstrated a duplex bioelectronic tongue

based on GFETs that were functionalized with speciﬁc recep-

tor nanovesicles (Figure d).[, ] The bioelectronic tongue

was able to detect as low as  aM monosodium glutamate

within  s (Figure e), paving the way to identify emerging

artiﬁcial tastants in food. Very recently, Lee et al. developed a

solution-gated GFET sensor functionalized with Naﬁon ﬁlms or

gold nanoparticles.[ ] These gold nanoparticles allow glucose

molecules to attach to the graphene surface more eectively than

other substances acting as receptors. Thus, the sensor can detect

glucose molecules as low as −  with a response/recovery

time of ≈/ s. Owing to its sensitive and rapid detection abil-

ity, this GFET electronic tongue was used to identify four real

beverages (orange juice, original coke, Sprite and Zero Coke), in-

dicating its promising potential in practical applications.[ ]

Advances have also been reported in developing bioelectronic

eyes based on GFET sensor systems.[– ] The human eye

perceives vision by converting light intensity into action poten-

tials through photosensitive receptors.[, ] Thus, artiﬁcial elec-

tronic eyes require high-performance bioengineered light re-

sponse elements for visual signal acquisition and conversion. For

example, Yang et al. demonstrated a phototransistor sensing sys-

tem for visual perception imaging by coupling ontogenetically

engineered living cells on a graphene channel.[ ] Engineered

living cells have photosensitive ion channels that convert light

intensity into bioelectrical signals. This process is similar to the

light pattern-to-action potential conversion process triggered by

the photoreceptors of retinal cells, making the phototransistor

imaging process similar to the human visual process. As an ap-

plication, the system can record the photocurrent changes at dif-

ferent locations of the original image and then recover a  ×

pixel image.[ ]

Apart from bioelectronic organs, mapping brain activity is

equally important for ﬁtness monitoring ﬁelds.[, ] Neural

mapping is generally based on noninvasive techniques such

as magnetoencephalography and electroencephalography for

recording or stimulation.[ ] However, these noninvasive tech-

niques do not provide suciently accurate and interpretable neu-

ral signals due to the ﬁltering of the human brain skull, prompt-

ing eorts to develop the next generation of electronic brains.

In addition, conventional noninvasive techniques are severely

hampered by current microelectrode materials when recording

ultralow or sublow brain activity (<. Hz).[ ] To address this

issue, Graaido’s group developed extra and intracortical arrays

for ultralow neural recording based on graphene solution-gated

ﬁeld-eect transistors (gSGFETs), which are capable of record-

ing neural signals from infralow (<. Hz) frequencies to typ-

ical local ﬁeld potential bandwidths (Figure f).[] Similar to

solution-ﬁlled glass microtubes, such gSGFETs can work with-

out the constraints of spatial sampling, thus oering the possi-

bility of spatially resolved mapping.[] Shortly after, this group

further advanced gSGFET technology by integrating a single

gSGFET to build a -channel sensing array.[,] The sens-

ing array was used to monitor the local ﬁeld potential of the

cortex in a freely moving rat model while monitoring its D

position over a long period of time up to  h (Figure g).

This work demonstrates the maturity of the technology in terms

of its long-term and broadband recording capabilities in freely

moving animals.[ ] Most recently, the group developed im-

plantable graphene depth neural probes (gDNPs) based on sg-

GFETs and applied them to epilepsy research.[ ] The gDNPs

consist of a linear array of graphene microtransistors. To en-

able them to penetrate the mouse cortex and reach the hip-

pocampus without ﬂexion, silk ﬁbroin was used to temporar-

ily harden the ﬂexible gDNPs. The gDNPs could reliably record

and map high spatial resolution seizures, preictal DC shifts and

seizure-related diusion depolarisations, as well as high fre-

quencies through the cortical layers to the hippocampus.[] In

short, these studies demonstrate the potential of graphene tran-

sistor technology to reveal seizure-related mechanisms in the

awake brain in vivo, advance the application of invasive tech-

niques for mapping brain activity, provide a reliable tool for

future applications in neuroscience and biomedical engineer-

ing, and pave the way for the development of the electronic

brain.

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Figure 9. GFET-based biomimetic sensor systems. a,b) GFET bioelectronic nose: a) Schematic of the smell sensor system comprising a cap chamber, a

GFET test chip, an IC socket, a casing, and BNC connector ports. b) Confusion matrix of multiclass classiﬁcation using the as-prepared sensor systems.

c–e) GFET bioelectronic tongue: c) Schematic of an artiﬁcial tongue and d) the device conﬁguration. e) Real-time response toward umami and sweet

tastants using the sensor systems. MSG, monosodium glutamate. f,g) GFET neural sensor systems: f) Device conﬁguration of gSGFETs enabling ultralow

frequency neural recording. g) Illustration of a rat implanted with an untethered recording system. The neural signals transmitted by the gSGFET array

sensors are digitized and transmitted wirelessly to a signal receiver, which is connected to a computer for signal recording. The right lower panel shows

a photograph of the gSGFET array placed on the rat cortex. (a, b) Reproduced under the terms of CC BY license. [ ] Copyright , The Authors,

published by MDPI. (c) Reproduced under the terms of CC BY-NC license. [ ] Copyright , The Authors, published by American Association for

the Advancement of Science. (d, e) Adapted with permission. [ ] Copyright , American Chemical Society. (f ) Reproduced with permission. []

Copyright , The Authors, under exclusive license to Springer Nature Limited. (g) Reproduced under the terms of CC BY license. [ ] Copyrights

, The Authors, published by Springer Nature.

4.3. Point-of-Care Diagnostics

The past few decades have witnessed a remarkable tech-

nical revolution in point-of-care (POC) diagnostic tools for

home-care health monitoring applications.[,, ] Among these

emerging POC technologies, GFET biosensors have exhib-

ited major advantages such as high sensitivity, biocompatibil-

ity, multifunctionality, ultrashort response time, and integrated

capabilities.[,, ] Corporated with electrical readouts and dig-

ital chips, GFET-based POC sensor systems hold great potential

for next-generation ﬁeld-deployable sensing tools in resource-

poor settings.[,, ] This section summarizes the latest ad-

vances in GFET POC sensor systems for the detection of cancer

biomarkers, pathogenic bacteria, and infectious viruses.

4.3.1. Cancer Biomarker

Cancer screening requires the sensitive detection of low-

abundance biomarkers at an early stage.[,, ] Research eorts

have been made in GFET biosensors for rapid, low-cost, multi-

plexed detection of cancer-related small biomolecules.[, ] In

, Hao et. al. demonstrated aptamer-functionalized GFETs

based on a HfOburied gate for cytokine detection.[ ] With

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Figure 10. GFET-based cancer and pathogenic sensor systems. a,b) GFET-based portable cytokine sensor systems: a) Photograph of the portable sensor

system consisting of signal transduction circuits, sample well, signal processing circuits, ampliﬁer and regulate circuits, WiFi module, and LCD screen.

b) Detection of interleukin- in human saliva solutions from dierent individuals using the GFET portable system. c) Schematic of GFET EV-Chip (left

panel) and the detection of blood-derived exosomes from plasma samples (right panel). d–f) An ABX-GMFET potable sensor system from the d) front

view and e) top view. f) The extended photograph demonstrates an ABX-GMFET microﬂuidic on an electrode, in which the target bacteria ﬂow. (a, b)

Adapted with permission. [ ]. Copyright , Elsevier B.V. (c) Adapted with permission. [] Copyright , Wiley-VCH GmbH. (d–f ) Adapted with

permission. [ ] Copyright , Elsevier B.V.

the aid of customized printed circuit boards, they developed

POC GFET sensor systems that visualize cytokine concentra-

tion changes via a smartphone or cloud server (Figure 10a).

This nanosensing system can detect as few as . ×− 

interleukin- molecules within  s (Figure b), indicating its

unique potential for noninvasive early diagnosis and long-term

healthcare applications. Similarly, the same group developed an-

other polyethyleneimine-based aptamer GFET for minute-level

detection (– min) of hemoglobin in undiluted biological ﬂu-

ids, with detection limits down to . ×−  (in serum) and

. ×−  (in urine).[ ] In , Mandal et al. demonstrated

a portable GFET sensor platform with a computationally opti-

mized coplanar gate electrode for PSA detection.[ ] Integrated

with customized readout modules and a rotating disk microﬂu-

idic device, the GFET sensor platform achieved an LoD of  ×

− mL−PSA in serum with a dynamic range up to  ×−

gmL

−.[ ] Moreover, the platform exhibited sucient speci-

ﬁcity to nontarget interferers induced by BSA and IgG molecules,

which is critical to medical diagnosis in future home-care tests.

Research progress has also been made in exosome detec-

tion via GFET biosensors.[,, ] Exosomes released from can-

cer cells contain speciﬁc information that is of vital importance

for precise diagnosis.[ ] In , Hajian et al. demonstrated

an exosome speciﬁc GFET termed EV-chip based on antibody-

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functionalized graphene (Figure c).[] The EV-chip works by

combining the speciﬁcity of antigen–antibody interactions and

the high electrophoretic mobility of exosomes, which allows real-

time exosome analyte quantiﬁcation in blood. The device LoD

reached  ×particles mL−and the quantiﬁcation limit was

as low as  ×particles mL−. Notably, GFET sensor systems

consisting of a picoammeter, potentiostat, multiplexer, micropro-

cessor controller, and serial interface weigh less than  kg, mak-

ing them a potential POC tool for clinical tests.[ ] Although the

integrated EV-chip exhibited several advantages in exosome de-

tection, the insucient sensitivity might lead to poor diagnostic

accuracy in population-wide applications. To address this issue,

Ramadan et al. developed a GFET exosome sensor system by dop-

ing carbon dots on the graphene surface to enhance sensitivity.[]

The incorporated carbon dots allowed the formation of a compat-

ible heterogeneous structure on sp-hybridized graphene, thus

leading to improved recognition eciency. As a demonstration,

the carbon-dot-enhanced GFET biosensors successfully detected

exosomes down to  particles μL−, approximately  orders of

magnitude higher than conventional exosome sensors.[, ] No-

tably, research eorts should be devoted to simplifying the op-

eration procedure and shortening the sample-to-answer time for

ideal user-friendliness.

4.3.2. Pathogenic Bacteria

Human pathogenic bacteria are disease-causing bacteria

that can lead to many serious diseases, epidemics, and

pandemics.[,,, ] There is an ever-lasting demand to

eectively detect pathogenic bacteria in various environmental

conditions such as water, food, soil, and air.[ ,] Unfortunately,

conventional assays with sophisticated operation depend largely

on specialist sta and expensive instruments, which take several

hours or days to identify target bacteria in unprocessed biological

samples.

Research eorts have been devoted to developing POC tools

that enable on-site detection of antibiotic-resistant pathogenic

bacteria in complex samples.[, ] For instance, Kim et al. devel-

oped a two-channel GFET-based portable device (ABX-GMFETs)

for the real-time detection of gram bacteria with an LoD down

to ≈CFUmL

−(Figure d).[] The antibiotic-modiﬁed ABX-

GMFET was able to distinguish gram-positive/negative bacte-

ria as a result of the chemical moiety interaction between the

bioprobes and target bacteria. Furthermore, the dual microﬂu-

idics incorporated with the platform minimized human inter-

vention and then facilitated automation in practical applications

(Figure e,f).[] Although the ABX-GMFET can identify Gram

bacteria, it is not sucient for multianalyte detection on a self-

contained chip.[, ] To this end, Kumar et al. demonstrated a

peptide-modiﬁed GFET sensor platform for -minute and multi-

analyte detection of antibiotic-resistant bacteria.[ ] The authors

dramatically reduced the LoD to cells mL−by employing di-

electrophoresis in GFETs. Additionally, two separate sample wells

in this sensor platform allowed the simultaneous detection of two

dierent bacteria, Staphylococcus aureus and antibiotic-resistant

Acinetobacter baumannii.[ ]

Research in GFET POC systems has also realized the sensitive

detection of bacterial products.[, ] In , Lin et al. achieved

-ppb-level detection of indole gas molecules, a metabolite prod-

uct of Escherichia coli (E. coli).[] This strategy introduced an-

other way to develop bacterial sensor systems by monitoring tar-

get gas products, which would make sense in food safety moni-

toring ﬁelds. By comparison, Tan et al. proposed a GFET-based

sensor system for continuous and real-time monitoring of E.

coli.[ ] In this conﬁguration, graphene was functionalized us-

ing two-terminal tail spike proteins, of which the N-terminal

domain can be anchored by -ethyl--(-dimethylaminopropyl)-

carbodiimide/N-hydroxy-succinimide and the C-terminal do-

main can bind to E. coli cellular lipopolysaccharide. Thus, the

as-prepared GFET sensor system was able to detect E. coli

with a sensitivity of . mV per bacteria.[ ] Most recently,

Burch and coworkers reported an aptamer-based GFET platform

that enables simultaneous detection of three dierent opioid

metabolites (i.e., noroxycodone, norfentanyl, and -ethylidene-

,-dimethyl-,-diphenylpyrrolidine).[ ] The authors placed

four isolated sets of GFETs with a Pt reference electrode on a

single chip (. cm ×. cm) to functionalize separately and re-

alize multianalyte detection simultaneously. In addition, the de-

vice LoD reaches .–. ×− gmL

−for these opioid metabo-

lites, which is − orders of magnitude lower than previous costly

laboratory-based techniques.

4.3.3. Neurodegenerative Disease

Neurodegenerative diseases refer to a heterogeneous group of

disorders in the central or peripheral nervous system, which

commonly include Alzheimer’s disease (AD), Parkinson’s dis-

ease, schizophrenia, and others.[,– ] Among these diseases,

AD is a widely investigated neurodegenerative disease that ac-

counts for % of all dementia and aects the quality of human

life.[– ] Early diagnosis of AD is of vital importance because

there is no certain treatment after the lesion has progressed in

the late stage.[, ] In , Park et al. demonstrated a dual-

antibody-modiﬁed GFET biosensor for multiplex detection of two

AD biomarkers (Figure 11a).[] In this conﬁguration, a cus-

tomized jig was combined with GFETs and an Ag/AgCl refer-

ence electrode to monitor the output signals of each biomarker

(Figure b). The as-prepared platform realized sensitive detec-

tion down to . pg mL−target proteins in bioﬂuids (human

plasma and artiﬁcial cerebrospinal ﬂuid). Owing to the dierent

isoelectric points and surface charges of AD biomarkers (A𝛽-

and t-Tau proteins), the GFET biosensor exhibited high selectiv-

ity with a distinctive output signal for each analyte (Figure c).

More recently, Kwon et al. demonstrated a simpliﬁed, linker-

free, anti-tau antibody immobilization process to build GFET AD

biosensors (see Figure b).[] The linker-free antibody immobi-

lized on the patterned graphene achieved an LoD of  mL−fg

Tau proteins in plasma. Apart from AD diagnostics, research ef-

forts have also been made in developing on-chip GFET biosen-

sors to detect neurological biomarkers in blood.[,, ] In ,

Xu et al. reported sensitive and ultrafast detection of glial ﬁb-

rillary acidic proteins in patient plasma by using GFET biosen-

sor chips.[ ] Compared with commercial ELISA kits and single-

molecule array (Simoa) technology, the proposed GFET approach

exhibited competitive LoD down to  fg mL−and a short

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Figure 11. GFET-based Alzheimer sensor systems. a) Schematic illustration of the GFET platform for multiplex detection of Alzheimer’s biomarkers

(A𝛽- and t-Tau). b) Device conﬁguration in front and side views. c) Selectivity of the GFET platform toward pivotal Alzheimer biomarkers. (a–c)

Adapted with permission. [ ] Copyright , Elsevier B.V.

sample-to-answer time of < min, indicating its great promise

for POC diagnosis applications.[, ]

4.3.4. Infectious Virus

Epidemics caused by infectious viruses are often characterized by

rapid transmission, wide dispersal and high public risk.[– ]

The primary step for epidemic prevention and control is rapid

and accurate diagnosis of presymptomatic and asymptomatic in-

dividuals who have low viral loads.[– ] This section summa-

rizes the notable progress in GFET-based POC systems for sen-

sitively detecting viruses such as Zika,[ ] Ebola,[ ] SARS-CoV-

,[ ] hepatitis C,[ ] tuberculosis,[] and others.[]

Early advances have been made in antibody-modiﬁed GFET

biosensors for ampliﬁcation-free detection of infectious viruses

in unprocessed samples.[, ] At the very beginning of the

COVID- outbreak, Seo et al. invented antibody-modiﬁed

GFETs enabling successful detection of SARS-CoV- in universal

transport medium (LoD: . ×pfu mL−) and clinical sam-

ples (. ×copies mL−).[ ] Additionally, Piccinini et al.

developed a portable GFET sensor based on monoclonal an-

tibodies for the detection of SARS-CoV- spike protein (LoD:

. ×− ).[ ] Although these single antibody-modiﬁed sen-

sor platforms realized rapid SARS-CoV- detection within min-

utes, the testing accuracy (<%) was limited because of poten-

tial antigen escape from a single captured binding site.[, ] To

prevent potential viral escape mutants, Dai et al. demonstrated

a multiantibody-modiﬁed strategy to achieve highly precise anti-

gen pool testing (Figure 12a).[] In this strategy, the SARS-CoV-

antigen was locked in a tight “antigen−antibody complex” cap-

tured by three dierent antibodies, thus leading to an LoD down

to . copy μL−in clinical samples and a diagnostic accu-

racy up to ≈%. Owing to such satisfactory sensor perfor-

mance, the multiantibody-modiﬁed GFET sensor systems enable

-in- pooled screening tests with high detection throughput

(Figure b), which is key to understanding the clinical relevancy

between sensor information and population-wide tests.[, ]

Research attempts have also been made in aptamer-based

GFET sensor systems for on-site detection of SARS-CoV- nu-

cleic acids. In , Park et al. combined the sensitive end-point

detection ability of crumped graphene with the ampliﬁcation

capability of reverse transcriptase loop-mediated isothermal

ampliﬁcation technology to develop a portable GFET sensor

system (Figure c). This method took advantage of crumped

graphene to adsorb SARS-CoV- nucleic acids, thus leading to

an LoD of  μL−SARS-CoV- in universal transport medium.

Furthermore, the GFET sensor system can dierentiate COVID-

-positive/negative samples with % accuracy, demonstrating

its potential in large-scale tests (Figure d,e). Most recently,

Wang et al. developed molecular electromechanical sys-

tems powered by LG-GFETs (MoIEMS-gFETs) for the direct

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Figure 12. GFET-based infectious sensor systems. a,b) A portable GFET sensor prototype: a) Schematic of sample collection, treatment, and image of

the integrated platform. b) ΔIds/Ids versus t curve upon the addition of COVID- -in- pooled samples. c–e) GFET-based COVID- sensor systems:

c) Device conﬁguration and surface functionalization of the GFET sensor systems. d) Responses of positive and negative clinical SARS-CoV- samples

on the crumpled GFETs. e) ROC curve analysis for positive and negative SARS-CoV- clinical samples. f–h) A MoIEMS-gFET prototype: f) Schematic

illustration of the device conﬁguration and working principle. g) A probe is conjugated at the tip of the ss-DNA cantilever for speciﬁc recognition. Note:

E indicates an electric ﬁeld. h) A prototype of the main system and the Mole-EMS GFET testing module. i) ∆Ids/Ids versus time curve for the MoIEMS-

gFET when adding COVID--positive clinical samples. Inset: The average diagnosis time in clinical tests. (a, b) Adapted with permission. [] Copyright

, American Chemical Society. (c–e) Adapted with permission. [] Copyright , American Chemical Society. (f–h) Adapted with permission.[]

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detection of SARS-CoV- RNA in nasopharyngeal swab samples

(Figure f–i).[] The MoIEMS was self-assembled from four

DNA strands consisting of a ﬂexible arm and a rigid base. By

immobilizing the MoIEMS onto the GFET, its rigid base helped

to avoid nonspeciﬁc adsorption of contaminants, while the

ﬂexible arm could precisely modulate the molecular recognition

and signal conversion processes driven by an external electric

ﬁeld, thus signiﬁcantly enhancing the detection sensitivity

(Figure f,g). Critically, the MoIEMS-gFETs eliminated the

need for complex and time-consuming nucleic acid extraction

and ampliﬁcation processes in neo-coronavirus nucleic acid

samples. As a demonstration, the system realized an LoD down

to  copies mL−SARS-CoV- RNA and an average response

time of less than  min when testing nasopharyngeal samples

from  patients with COVID- and  COVID--negative

controls (Figure i).[]

As shown above, impressive advances in GFET biosensors

have been demonstrated in prototypical applications, including

wearable devices, biomimetic electronics, and diagnostic plat-

forms. Nevertheless, the commercialization of such sensing sys-

tems is restricted by their clinical consistency with population-

wide human tests. Further investigation and enormous research

eorts are desired to make GFET biosensors suitable for the clin-

ical market, which will pave the way for future healthcare moni-

toring technologies.

5. Conclusion and Outlook

Emerging GFET biosensors with unique properties and ap-

pealing electronic applications have promising potential to pro-

mote future innovation in healthcare domains.[,, ] In Sec-

tions . and ., we reviewed that GFET biosensors could suf-

fer from Debye screening eects and foiling eects under com-

plex physiological conditions, leading to diculty in quantita-

tive detection applications. Accordingly, we summarize recent

progress in GFET biosensors, with discussions on materials,

bioreceptor design, fabrication techniques, surface functional-

ization, antifouling approaches, and proof-of-concept applica-

tions at the integrated level. Over the past decade, GFET-based

sensor systems have demonstrated wide-ranging applications

across wearable electronics, biomimetic devices, health monitor-

ing, and medical diagnostics. Despite rapid development, some

major concerns still exist before the commercialization of GFET

biosensors.[,, ] Here, we list several key translation challenges

and discuss potential research directions:

5.1. Standardization

Ensuring that GFET biosensors are both accurate and reliable is

of vital importance for their acceptance in clinical tests.[, ] This

goal requires standardization in fabricating individual sensors

with high-quality graphene materials and minimized device-to-

device variations.[, ] Great eorts have been made to develop

GFET sensor systems with uniform fabrication, functionaliza-

tion approaches and testing conditions. Few technologies, how-

ever, can meet medical standards, which include biosafety, time

eciency, high-throughput requirements, and easy-to-operate

demands.[, ] Additionally, the analytical operation procedures

of GFET biosensors dier from research groups, hampering the

process of their launch in the real world.[,, ] Thus, standardiza-

tion is essential for GFET biosensors to maintain performance

and qualify home tests in healthcare ﬁelds.

5.2. System Integration

A commercial sensor system requires the integration of a power

supply, sensing components, digital processing, and data com-

munication modules.[,, ] Such stand-alone systems enable

home-care health monitoring at the molecular level. Neverthe-

less, most GFET-based sensor prototypes in this Review focused

primarily on single measurements, and research eorts should

continue toward developing multifunctional platforms that can

capture unique information of diverse health biomarkers.[, ]

In combination with other technologies such as optogenetics and

gene-editing techniques, integrated GFET sensor systems will be

a comprehensive tool not only for ﬁtness applications but also for

widespread biodefense applications.[, ]

5.3. Translation to the Healthcare Market

Recently, the global healthcare market has witnessed continuous

growth in biomarker monitoring applications.[, ] The success-

ful translation of GFET biosensors and prototypical demonstra-

tions to the medical community still faces several hurdles re-

lated to clinical operation. Foremost, ﬁtness monitoring appli-

cations promise rich sensor information, which requires exten-

sive correlation studies and successful validation before market

acceptance.[,, ] To this end, advanced technologies such as big

data, cloud computing, artiﬁcial intelligence, and machine learn-

ing algorithms are necessary to take full advantage of diagnos-

tic information obtained by GFET biosensors. Another key con-

cern is to control environmental conditions that aect the sen-

sor performance in real-world tests. For example, external tem-

perature and moisture should be considered when constructing

and operating GFET-based sensor systems to ensure diagnos-

tic accuracy.[,,] Third, there is an ever-lasting need for user-

friendly healthcare systems that are accurate, cost-eective, sim-

ple to perform with minimal training, and require fewer testing

reagents at minimal risk of harm.[, ] These concerns indicate

the outstanding need for research eorts before clinical transla-

tion and commercial realization.

Overall, GFET-based sensor systems facilitate their practical

use, especially in healthcare ﬁelds, due to their ultrahigh sensi-

tivity and facile integration. They will be able to monitor a wide

range of health biomarkers with high quality, ultimately enabling

home-care ﬁtness applications and personal diagnostics. Notably,

the successful lab-to-fab translation of these sensors requires

massive validation in understanding the clinical relevancy of sen-

sor information and developing universal assessment metrics in

large-scale human tests. Given the competitive and tremendous

research eorts in this ﬁeld, we anticipate exciting developments

in GFET biosensors that will change the medical community and

improve people’s lives in the near future.

Adv. Funct. Mater. 2023,  ©  Wiley-VCH GmbH

2301948 (20 of 26)

www.advancedsciencenews.com www.afm-journal.de

Acknowledgements

C.D., D.K., and C.C. contributed equally to this work. The authors

acknowledge ﬁnancial support from the National Key R&D Program

of China (YFC), National Natural Science Foundation of

China (), Strategic Priority Research Program of the Chinese

Academy of Sciences (XDB), Chongqing Bayu Scholar Program

(DP) and Fudan University.

Conﬂict of Interest

The authors declare no conﬂict of interest.

Keywords

biomarkers, biosensors, diagnostics, ﬁeld-eect transistors, graphene

Received: February , 

Revised: March , 

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Changhao Dai received his bachelor’s degree from Nanjing University of Science and Technology in

. He is currently pursuing a Ph. D. at the Department of Macromolecular Science, Fudan Univer-

sity at Shanghai, China, under the guidance of Prof. Dacheng Wei. His research interests center on the

preparation of D materials and their sensing applications.

Derong Kong received his Ph. D. degree from Fudan University in . She is currently a postdoctoral

scholar in Dr.Wei’s research group at Fudan University. Her major research interests are to combine

electrochemical bio-platforms and CRISPR-based technology for point-of-care detection of unpro-

cessed biological samples.

Chang Chen received her master’s degree from Fuzhou University in . She is currently a Ph. D.

candidate at the Department of Macromolecular Science, Fudan University at Shanghai, China, un-

der the guidance of Prof. Dacheng Wei. Her research focuses on the application of graphene-based

electronics in bioelectronic sensing.

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YunqiLiu is a professor of Fudan University. He was selected as an academician of the Chinese

Academy of Sciences in . He received his Ph.D. degree from Tokyo Institute of Technology(Japan)

in . He is an adjunct professor of Tsinghua University (China) and a guest professor of Kyoto Uni-

versity (Japan). His research interests include molecular materials and devices.

Dacheng Wei is a professor of Fudan University. He received his bachelor’s degree from Zhejiang

University in  and his Ph. D. from the Institute of Chemistry,Chinese Academy of Sciences, in

. His research interests include controllable growth of D materials and their applications in FET

devices and sensors.

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Stress granules (SGs) are cytosolic biomolecular condensates that form in response to cellular stress. Weak, multivalent interactions between their protein and RNA constituents drive their rapid, dynamic assembly through phase separation coupled to percolation. Though a consensus model of SG function has yet to be determined, their perceived implication in cytoprotective processes (e.g., antiviral responses and inhibition of apoptosis) and possible role in the pathogenesis of various neurodegenerative diseases (e.g., amyotrophic lateral sclerosis and frontotemporal dementia) have drawn great interest. Consequently, new studies using numerous cell biological, genetic, and proteomic methods have been performed to unravel the mechanisms underlying SG formation, organization, and function and, with them, a more clearly defined SG proteome. Here, we provide a consensus SG proteome through literature curation and an update of the user-friendly database RNAgranuleDB to version 2.0 (http://rnagranuledb.lunenfeld.ca/). With this updated SG proteome, we use next-generation phase separation prediction tools to assess the predisposition of SG proteins for phase separation and aggregation. Next, we analyze the primary sequence features of intrinsically disordered regions (IDRs) within SG-resident proteins. Finally, we review the protein- and RNA-level determinants, including post-translational modifications (PTMs), that regulate SG composition and assembly/disassembly dynamics.

Multi-omics microsampling for the profiling of lifestyle-associated changes in health

Article

Full-text available

Jan 2023
Nat. Biomed. Eng.

Current healthcare practices are reactive and use limited physiological and clinical information, often collected months or years apart. Moreover, the discovery and profiling of blood biomarkers in clinical and research settings are constrained by geographical barriers, the cost and inconvenience of in-clinic venepuncture, low sampling frequency and the low depth of molecular measurements. Here we describe a strategy for the frequent capture and analysis of thousands of metabolites, lipids, cytokines and proteins in 10 μl of blood alongside physiological information from wearable sensors. We show the advantages of such frequent and dense multi-omics microsampling in two applications: the assessment of the reactions to a complex mixture of dietary interventions, to discover individualized inflammatory and metabolic responses; and deep individualized profiling, to reveal large-scale molecular fluctuations as well as thousands of molecular relationships associated with intra-day physiological variations (in heart rate, for example) and with the levels of clinical biomarkers (specifically, glucose and cortisol) and of physical activity. Combining wearables and multi-omics microsampling for frequent and scalable omics may facilitate dynamic health profiling and biomarker discovery.

Highly Stretchable and Biocompatible Wrinkled Nanoclay‐Composite Hydrogel With Enhanced Sensing Capability for Precise Detection of Myocardial Infarction

Article

Full-text available

Jan 2023
ADV MATER

It is challenging to balance high biocompability with good mechanical‐electrical sensing performance, especially when triggered inflammatory stress response after in‐vivo implantation. Herein, we reported a bioinspired wrinkle‐reinforced adaptive nanoclay‐interlocked soft strain‐sensor based on a highly stretchable and elastic ionic‐conductive hydrogel. This novel nanoclay‐composite hydrogel exhibited excellent tensile properties and high sensing capacity with steady and reliable sensing performance due to the structural‐mechanical‐electrical integrity of the nanoclay crosslinked and nano‐reinforced interpenetrating network. The incorporation of amphiphilic ions provides the hydrogel with significant protein resistance, reducing its non‐specific adsorption to proteins upon implantation and improving its biosafety as an implanted device and maintaining the authenticity of the sensing results. Based on the revealed sensing enhanced mechanism based on hierarchical ordered structures, as a proof‐of‐concept application, it is for the first time to demonstrate this hydrogel sensor can accurately localize the region where myocardial infarction occurs and may become a novel strategy for real‐time monitoring of pathological changes in heart disease. This article is protected by copyright. All rights reserved

Attomolar detection of hepatitis C virus core protein powered by molecular antenna-like effect in a graphene field-effect aptasensor

Article

Full-text available

Dec 2022
BIOSENS BIOELECTRON

Biosensors based on graphene field-effect transistors have become a promising tool for detecting a broad range of analytes. However, their performance is substantially affected by the functionalization protocol. In this work, we use a controlled in-vacuum physical method for the covalent functionalization of graphene to construct ultrasensitive aptamer-based biosensors (aptasensors) able to detect hepatitis C virus core protein. These devices are highly specific and robust, achieving attomolar detection of the viral protein in human blood plasma. Such an improved sensitivity is rationalized by theoretical calculations showing that induced polarization at the graphene interface, caused by the proximity of covalently bound molecular probe, modulates the charge balance at the graphene/aptamer interface. This charge balance causes a net shift of the Dirac cone providing enhanced sensitivity for the attomolar detection of the target proteins. Such an unexpected effect paves the way for using this kind of graphene-based functionalized platforms for ultrasensitive and real-time diagnostics of different diseases.

Unamplified and Real‐Time Label‐Free miRNA‐21 Detection Using Solution‐Gated Graphene Transistors in Prostate Cancer Diagnosis

Article

Full-text available

Dec 2022

The incidence of prostate cancer (PCa) in men globally increases as the standard of living improves. Blood serum biomarker prostate‐specific antigen (PSA) detection is the gold standard assay that do not meet the requirements of early detection. Herein, a solution‐gated graphene transistor (SGGT) biosensor for the ultrasensitive and rapid quantification detection of the early prostate cancer‐relevant biomarker, miRNA‐21 is reported. The designed single‐stranded DNA (ssDNA) probes immobilized on the Au gate can hybridize effectively with the miRNA‐21 molecules targets and induce the Dirac voltage shifts of SGGT transfer curves. The limit of detection (LOD) of the sensor can reach 10⁻²⁰ M without amplification and any chemical or biological labeling. The detection linear range is from 10⁻²⁰ to 10⁻¹² M. The sensor can realize real‐time detection of the miRNA‐21 molecules in less than 5 min and can well distinguish one‐mismatched miRNA‐21 molecule. The blood serum samples from the patients without RNA extraction and amplification are measured. The results demonstrated that the biosensor can well distinguish the cancer patients from the control group and has higher sensitivity (100%) than PSA detection (58.3%). Contrastingly, it can be found that the PSA level is not directly related to PCa.

Graphene-Based Field-Effect Transistors in Biosensing and Neural Interfacing Applications: Recent Advances and Prospects

Article

Jan 2023

Ultrasensitive detection of SARS-CoV-2 spike protein by graphene field-effect transistors

Article

Dec 2022
Nanoscale

COVID-19, caused by the severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2), originated a global health crisis, causing over 2 million casualties and altering human daily life all over the world. This pandemic emergency revealed the limitations of current diagnostic tests, highlighting the urgency to develop faster, more precise and sensitive sensors. Graphene field effect transistors (GFET) are analytical platforms that enclose all these requirements. However, the design of a sensitive and robust GFET is not a straightforward objective. In this work, we report a GFET array biosensor for the detection of SARS-CoV-2 spike protein using the human membrane protein involved in the virus internalisation: angiotensin-converting enzyme 2 (ACE2). By finely controlling the graphene functionalisation, by tuning the Debye length, and by deeply characterising the ACE2-spike protein interactions, we have been able to detect the target protein with an extremely low limit of detection (2.94 aM). This work set the basis for a new class of analytical platforms, based on human membrane proteins, with the potential to detect a broad variety of pathogens, even before their isolation, being a powerful tool in the fight against future pandemics.

Graphene electrochemical transistor incorporated with gel electrolyte for wearable and non-invasive glucose monitoring

Article

Dec 2022
ANAL CHIM ACTA

With the rapid development of wearable electronic devices, health monitoring is undergoing a fundamental shift from hospital-centered treatment to patient-centered diagnosis. Solution-gated graphene transistors provide an effective platform for developing high-sensitivity wearable devices due to their unique signal amplification, low energy consumption, and compatibility for miniaturization. However, it is still a major challenge to perform real-time sweat composition monitoring directly on the dry skin surface. In this work, a skin-based flexible gel electrolyte graphene transistor (GEGT) was successfully designed and fabricated for glucose detection, consisting of a gate electrode decorated with Au nanoparticles modified reduced graphene oxide (AuNPs/RGO) nanocomposites and a monolayer graphene channel. Glycerin gel was used to replace the traditional liquid electrolyte, not only could better fit the human skin, but also play the role of fluid collection, providing stable testing conditions for the sensor. Based on the high electron mobility of graphene channel and the excellent electrocatalytic performance of AuNPs/RGO nanocomposites, the constructed GEGT sensor exhibits excellent sensing performance for glucose with good selectivity, low operating voltage (0.5 V), wide detection range (10 nM - 25 mM), and low detection limit (10 nM). The device maintains stable performance after up to 1000 bending cycles with a bending radius of 4 mm. In addition, the GEGT sensor displays good accuracy in sweat detection and sensitive dynamic response during actual wearing, which provides a guarantee for the construction of wearable transistor devices and real-time health tracking.

Rapid and Unamplified Detection of SARS-CoV-2 RNA via CRISPR-Cas13a-Modified Solution-Gated Graphene Transistors

Article

Dec 2022

The disease caused by severe acute respiratory syndrome coronavirus, SARS-CoV-2, is termed COVID-19. Even though COVID-19 has been out for more than two years, it is still causing a global pandemic. Due to the limitations of sample collection, transportation, and kit performance, the traditional reverse transcription-quantitative polymerase chain reaction (RT-qPCR) method has a long detection period and high testing costs. An increased risk of infection is inevitable, since many patients may not be diagnosed in time. The CRISPR-Cas13a system can be designed for RNA identification and knockdown, as a promising platform for nucleic acid detection. Here, we designed a solution-gated graphene transistor (SGGT) biosensor based on the CRISPR-Cas13a system. Using the gene-targeting capacity of CRISPR-Cas13a and gate functionalization via multilayer modification, SARS-CoV-2 nucleic acid sequences can be quickly and precisely identified without the need for amplification or fluorescence tagging. The limit of detection (LOD) in both buffer and serum reached the aM level, and the reaction time was about 10 min. The results of the detection of COVID-19 clinical samples from throat swabs agree with RT-PCR. In addition, the interchangeable gates significantly minimize the cost and time of device fabrication. In a nutshell, our biosensor technology is broadly applicable and will be suitable for point-of-care (POC) testing.

High-intensity vector signals for detecting SARS-CoV-2 RNA using CRISPR/Cas13a couple with stabilized graphene field-effect transistor

Article

Nov 2022
BIOSENS BIOELECTRON

False detection of SARS-CoV-2 is detrimental to epidemic prevention and control. The scalar nature of the detected signal and the imperfect target recognition property of developed methods are the root causes of generating false signals. Here, we reported a collaborative system of CRISPR-Cas13a coupling with the stabilized graphene field-effect transistor, providing high-intensity vector signals for detecting SARS-CoV-2. In this collaborative system, SARS-CoV-2 RNA generates a “big subtraction” signal with a right-shifted feature, whereas any untargets cause the left-shifted characteristic signal. Thus, the false detection of SARS-CoV-2 is eliminated. High sensitivity with 0.15 copies/μL was obtained. In addition, the wide concerned instability of the graphene field-effect transistor for biosensing in solution environment was solved by the hydrophobic treatment to its substrate, which should be a milestone in advancing it's engineering application. This collaborative system characterized by the high-intensity vector signal and amazing stability significantly advances the accurate SARS-CoV-2 detection from the aspect of signal nature.

Graphene Transistors for In Vitro Detection of Health Biomarkers

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