Graphene Field Effect Transistor Sensor Research Articles

A Drug Molecule-Modified Graphene Field-Effect Transistor Nanosensor for Rapid, Label-Free, and Ultrasensitive Detection of Estrogen Receptor α Protein.

Estrogen receptor α (ERα) is an important biomarker in breast cancer diagnosis and treatment. Sensitive and accurate detection of ERα protein expression is crucial in guiding selection of an appropriate therapeutic strategy to improve the effectiveness and prognosis of breast cancer treatment. Herein, we report a liquid-gated graphene field-effect transistor (FET) biosensor that enables rapid, sensitive, and label-free detection of the ERα protein by employing a novel drug molecule as a capture probe. The drug molecule was synthesized and subsequently immobilized onto the sensing surface of the fabricated graphene FET, which was able to distinguish the ERα-positive from the ERα-negative protein. The developed sensor not only demonstrated a low detection limit (LOD: 2.62 fM) but also achieved a fast response to ERα protein samples within 30 min. Moreover, depending on the relationship between the change of dirac point and the ERα protein concentrations, the dissociation constant (Kd) was estimated to be 7.35 ± 0.06 pM, indicating that the drug probe-modified graphene FET had a good affinity with ERα protein. The nanosensor was able to analyze ERα proteins from 36 cell samples lysates. These results show that the graphene FET sensor was able to differentiate between ERα-positive and ERα-negative cells, indicating a promising biosensor for the ultrasensitive and rapid detection of ERα protein without antibody labeling.

High enhancement of sensitivity and reproducibility in label-free SARS-CoV-2 detection with graphene field-effect transistor sensors through precise surface biofunctionalization control

The COVID-19 pandemic has taught us valuable lessons, especially the urgent need for a widespread, rapid and sensitive diagnostic tool. To this, the integration of bidimensional nanomaterials, particularly graphene, into point-of-care biomedical devices is a groundbreaking strategy able to potentially revolutionize the diagnostic landscape. Despite advancements in the fabrication of these biosensors, the relationship between their surface biofunctionalization and sensing performance remains unclear. Here, we demonstrate that the combination of careful sensor fabrication and its precise surface biofunctionalization is crucial for exalting the sensing performances of 2D biosensors. Specifically, we have biofunctionalized Graphene Field-Effect Transistor (GFET) sensors surface through different biochemical reactions to promote either random/heterogeneous or oriented/homogeneous immobilization of the Anti-SARS-CoV-2 spike protein antibody. Each strategy was thoroughly characterized by in-silico simulations, physicochemical and biochemical techniques and electrical characterization. Subsequently, both biosensors were tested in the label-free direct titration of SARS-CoV-2 virus in simulated clinical samples, avoiding sample preprocessing and within short timeframes. Remarkably, the oriented GFET biosensor exhibited significantly enhanced reproducibility and responsiveness, surpassing the detection sensitivity of conventional non-oriented GFET by more than twofold. This breakthrough not only involves direct implications for COVID-19 surveillance and next pandemic preparedness but also clarify an unexplored mechanistic dimension of biosensor research utilizing 2D-nanomaterials.

Side charge propagation in simultaneous KPFM and transport measurement of humidity exposed graphene FET sensor

The surface diffusion (dissipation) of charge carriers enhanced by water molecules in solution-based biosensors and ambient operating gas sensors strongly influence their resistance response, sensitivity, and stability in time. Therefore, the information on the charge distribution at interfaces of conductive and insulating parts is essential for the operating sensors. This work presents the simultaneous measurement of the longitudinal macroscopic resistance response and local surface potential (SP) mapping by Kelvin probe force microscopy (KPFM) on a graphene Hall bar sensor. The results show the propagation of an electric charge from the main graphene channel onto the neighboring SiO2 surface. The charge propagation strongly increases with the relative humidity and can be controlled by a bottom-gate voltage used in most sensors based on a field effect transistor (FET) architecture. As proved by the longitudinal resistance measurements, the resulting side charge accumulation has a very small impact on the 2D resistivity of the graphene channel. It has been explained by an application of the Thomas-Fermi theory, proving an efficient screening of side accumulated charge potential caused by a redistribution of the charge inside the wide graphene channel. The combination of a transport resistance response and KPFM provides a deeper understanding of sensors/biosensors functionality and their design features than a simple resistance response usually observed.

Unamplified system for sensitive and typing detection of ASFV by the cascade platform that CRISPR-Cas12a combined with graphene field-effect transistor

At present, the 100% case fatality and the cross-infection of virus strains make the ASFV 's harm to society continue to expand. The absence of an effective commercial vaccine poses early detection remains the most effective means of curbing ASFV infection. Here, we report a cascaded detection platform based on the CRISPR-Cas12a system combined with graphene field-effect transistor sensors. The cascade platform could detect ASFV as low as 0.5 aM within 30 min and achieve typing of wild and vaccine strains of ASFV in a single detection system. The evaluation of 16 clinical samples proved that, compared with the gold standard Real-time PCR method, this platform has outstanding advantages in sensitivity, specificity and typing. Combining CRISPR-Cas12a's high specificity with the bipolar electric field effect of graphene field-effect transistor, the cascade platform is expected to achieve clinical application in the field of DNA disease detection, and provides a new direction for multi-strain disease typing.

A Machine Learning Approach to COVID-19 Detection via Graphene Field-Effect-Transistor (GFET).

In the wake of the COVID-19 pandemic, there has been a need for reliable diagnostic testing. However, state-of-the-art detection methods rely on laboratory tests and also vary in accuracy. We evaluate that the usage of a graphene field-effect-transistor (GFET) coupled with machine learning can be a promising alternate diagnostic testing method. We processed the current-voltage data gathered from the GFET sensors to assess information about the presence of COVID-19 in biosamples. We perform binary classification using the following machine learning algorithms: Linear Discriminant Analysis (LDA), Support Vector Machines (SVM) with the Radial Basis Function (RBF) kernel, and K-Nearest Neighbors (KNN) in conjunction with Principal Component Analysis (PCA). We find that LDA and SVM with RBF proved to be the most accurate in identifying positive and negative samples, with accuracies of 99% and 98.5%, respectively. Based on these results, there is promise to develop a bioelectronic diagnostic method for COVID-19 detection by combining GFET technology with machine learning.

Advances in flexible graphene field-effect transistors for biomolecule sensing.

With the increasing demand for biomarker detection in wearable electronic devices, flexible biosensors have garnered significant attention. Additionally, graphene field-effect transistors (GFETs) have emerged as key components for constructing biosensors, owing to their high sensitivity, multifunctionality, rapid response, and low cost. Leveraging the advantages of flexible substrates, such as biocompatibility, adaptability to complex environments, and fabrication flexibility, flexible GFET sensors exhibit promising prospects in detecting various biomarkers. This review provides a concise summary of design strategies for flexible GFET biosensors, including non-encapsulated gate without dielectric layer coverage and external gate designs. Furthermore, notable advancements in sensing applications of biomolecules, such as proteins, glucose, and ions, are highlighted. Finally, we discuss the future challenges and prospects in this field, aiming to inspire researchers to address these issues in their further investigations.

Graphene Transistors for In Vitro Detection of Health Biomarkers

AbstractBiomarkers 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.

Undecorated GFET for determinations of heavy metal ions aided by machine learning algorithms

The Graphene field effect transistor (GFET) has become one of the most popular biochemical-sensing platforms. The sensitivity of traditional GFET sensor usually depends on functionalization of aptamers. Here, we reported a machine learning algorithms assisted heavy metal ions detecting method by using pristine GFET. In this work, GFETs are prepared by screen printing technology, which has the advantages of total solution processes and low costs. The graphene ink (G-ink) used in screen printing procedures of GFET is synthesized by combining liquid exfoliation with solvent exchange method. The FTIR and XPS show there are oxygen-containing groups on the graphene channel, which have different affinities for heavy metal ions and can be used to determine ions. Seven algorithms are used to establish and train prediction models by using the data of transfer curves as inputs and ions’ types and concentrations as labels, the results indicate that the accuracy of AdaBoost (0.91) is the highest, which demonstrate this machine learning assisted GFET sensing platform can be used to determine metal ions’ types and concentrations at the same time. Moreover, the complicated processes for bio-functionalizing GFET and data analysis are omitted, which is benefit to simplify the fabricating procedures, we believe it will be an attractive strategy to promote the development of GFET.

(Invited) Surface Functionalization of Graphene Field-Effect Transistors for Biosensing Applications

Graphene field-effect transistors (GFETs) present beneficial features for their application as biomolecular or chemical sensors. Due to their atomically-thin 2D dimensionality, their high electrical conductance is particularly sensitive to small changes in the distribution of charged species near the graphene surface. Taking advantage of this property, GFET sensors have been designed to report the detection or quantitation of various types of biologically-relevant molecular analytes such as nucleic acids, proteins, ions or small molecules. By analyzing data from published literature on GFET bioanalytical sensors, we have recently shown that the detection metrics of such sensors vary enormously between studies, and argued that this variance is mainly driven by disparities in the bio-recognition interface [1]. Indeed, the selectivity of GFET sensors must be engineered, typically by covering the graphene surface with biological molecules having a specific affinity for the chosen analyte (e.g. antibodies to capture the corresponding antigen, ssDNA to capture its complementary sequence). Yet, the coverage, orientation, stability and interactions between immobilized probes, blocking species and captured analytes are often not well known or controlled. In this presentation, I will discuss our efforts to understand and regulate the surface functionalization of graphene field-effect transistors. Using electrical conductance measurements and Raman spectroscopy, I will characterize the response of devices to both covalent chemistry, using aryldiazonium reagents, and non-covalent chemistry, using pyrene derivatives. I will describe our recent developments in controlling these functionalization routes, and compare their use for further bioconjugation with molecular probes for bioanalytical purposes.[1] A. Béraud, M. Sauvage, C. M. Bazán, M. Tie, A. Bencherif and D. Bouilly. Graphene Field-Effect Transistors as Bioanalytical Sensors: Design, Operation and Performance. Analyst 146, 403-428 (2021) https://doi.org/10.1039/D0AN01661F

Rapid self-test of unprocessed viruses of SARS-CoV-2 and its variants in saliva by portable wireless graphene biosensor

We have developed a DNA aptamer-conjugated graphene field-effect transistor (GFET) biosensor platform to detect receptor-binding domain (RBD), nucleocapsid (N), and spike (S) proteins, as well as viral particles of original Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) coronavirus and its variants in saliva samples. The GFET biosensor is a label-free, rapid (≤20 min), ultrasensitive handheld wireless readout device. The limit of detection (LoD) and the limit of quantitation (LoQ) of the sensor are 1.28 and 3.89 plaque-forming units (PFU)/mL for S protein and 1.45 and 4.39 PFU/mL for N protein, respectively. Cognate spike proteins of major variants of concern (N501Y, D614G, Y453F, Omicron-B1.1.529) showed sensor response ≥40 mV from the control (aptamer alone) for fM to nM concentration range. The sensor response was significantly lower for viral particles and cognate proteins of Middle East Respiratory Syndrome (MERS) compared to SARS-CoV-2, indicating the specificity of the diagnostic platform for SARS-CoV-2 vs. MERS viral proteins. During the early phase of the pandemic, the GFET sensor response agreed with RT-PCR data for oral human samples, as determined by the negative percent agreement (NPA) and positive percent agreement (PPA). During the recent Delta/Omicron wave, the GFET sensor also reliably distinguished positive and negative clinical saliva samples. Although the sensitivity is lower during the later pandemic phase, the GFET-defined positivity rate is in statistically close alignment with the epidemiological population-scale data. Thus, the aptamer-based GFET biosensor has a high level of precision in clinically and epidemiologically significant SARS-CoV-2 variant detection. This universal pathogen-sensing platform is amenable for a broad range of public health applications and real-time environmental monitoring.

UV Illumination as a Method to Improve the Performance of Gas Sensors Based on Graphene Field-Effect Transistors.

The ability to detect and recognize airborne chemical species is essential to enable applications in security, health, and environmental monitoring. Here, we report a sensing platform based on graphene field-effect transistor (GFET) devices combined with optical illumination for the detection of volatile compounds. We compare the change in resistance of GFET sensors upon exposure to analytes such as ethanol, dimethyl methylphosphonate (DMMP), and water vapors with and without the presence of a local illuminating ultraviolet (UV) light-emitting diode (LED). Our results show that UV illumination acts as a control knob for the electronic transport properties of graphene, increasing the device's response to ethanol, water, and DMMP, up to a factor of 54, and enabling ppb-level detection of DMMP at 800 ppb without chemical functionalization of the graphene layer. The sensing response can be optimized to reveal an analyte-specific interplay between the induced changes in carrier concentration and mobility of the GFET. These findings provide a pathway to enhancing the sensitivity of GFET sensors and a differentiation channel to improve their selectivity.

The role of graphene patterning in field-effect transistor sensors to detect the tau protein for Alzheimer's disease: Simplifying the immobilization process and improving the performance of graphene-based immunosensors

We report the improvement in the sensing performance of electrolyte-gated graphene field-effect transistor (FET) sensors capable of detecting tau protein through a simplified, linker-free, anti-tau antibody immobilization process. For most of the graphene-based immunosensor, linkers, such as pyrenebutanoic acid, succinimidyl ester (PSE) must be used to the graphene surface, while the other side of linkers serves to capture the antibodies that can specifically interact with the target biomarker. In this study, graphene was patterned into eight different types and linker-free patterned graphene FET sensors were fabricated to verify their detection performance. The linker-free antibody immobilization to patterned graphene exhibited that the antibody was immobilized to the edge defect and had a doping-like behaviors on graphene. As the tau protein concentration in the electrolyte increased from 10 fg/ml to 1 ng/ml, the performances, charge neutral point shift and current change rate of the patterned graphene sensors without linkers were enhanced 2–3 times compared to a pristine graphene sensor with the PSE linker. Moreover, tau protein in the plasma of five Alzheimer's disease patients was measured using a linker-free patterned graphene sensor. It shows a 3–4 times higher current change rate than that of pristine graphene sensor with the PSE linker. Since the antibody is immobilized directly without a linker, a patterned graphene sensor without a linker can operate more sensitively in higher ionic concentration electrolyte.

Graphene FET Sensors for Alzheimer's Disease Protein Biomarker Clusterin Detection.

We report on the fabrication and characterisation of graphene field-effect transistor (GFET) biosensors for the detection of Clusterin, a prominent protein biomarker of Alzheimer’s disease (AD). The GFET sensors were fabricated on Si/SiO2 substrate using photolithographic patterning and metal lift-off techniques with evaporated chromium and sputtered gold contacts. Raman Spectroscopy was performed on the devices to determine the quality of the graphene. The GFETs were annealed to improve their performance before the channels were functionalized by immobilising the graphene surface with linker molecules and anti-Clusterin antibodies. Concentration of linker molecules was also independently verified by absorption spectroscopy using the highly collimated micro-beam light of Diamond B23 beamline. The detection was achieved through the binding reaction between the antibody and varying concentrations of Clusterin antigen from 1 to 100 pg/mL, as well as specificity tests using human chorionic gonadotropin (hCG), a glycoprotein risk biomarker of certain cancers. The GFETs were characterized using direct current (DC) 4-probe electrical resistance (4-PER) measurements, which demonstrated a limit of detection of the biosensors to be ∼ 300 fg/mL (4 fM). Comparison with back-gated Dirac voltage shifts with varying concentration of Clusterin show 4-PER measurements to be more accurate, at present, and point to a requirement for further optimisation of the fabrication processes for our next generation of GFET sensors. Thus, we have successfully fabricated a promising set of GFET biosensors for the detection of Clusterin protein biomarker. The developed GFET biosensors are entirely generic and also have the potential to be applied to a variety of other disease detection applications such as Parkinson’s, cancer, and cardiovascular.

MXene-Graphene Field-Effect Transistor Sensing of Influenza Virus and SARS-CoV-2.

An MXene–graphene field-effect transistor (FET) sensor for both influenza virus and 2019-nCoV sensing was developed and characterized. The developed sensor combines the high chemical sensitivity of MXene and the continuity of large-area high-quality graphene to form an ultra-sensitive virus-sensing transduction material (VSTM). Through polymer linking, we are able to utilize antibody–antigen binding to achieve electrochemical signal transduction when viruses are deposited onto the VSTM surface. The MXene–graphene VSTM was integrated into a microfluidic channel that can directly receive viruses in solution. The developed sensor was tested with various concentrations of antigens from two viruses: inactivated influenza A (H1N1) HA virus ranging from 125 to 250,000 copies/mL and a recombinant 2019-nCoV spike protein ranging from 1 fg/mL to 10 pg/mL. The average response time was about ∼50 ms, which is significantly faster than the existing real-time reverse transcription-polymerase chain reaction method (>3 h). The low limit of detection (125 copies/mL for the influenza virus and 1 fg/mL for the recombinant 2019-nCoV spike protein) has demonstrated the sensitivity of the MXene–graphene VSTM on the FET platform to virus sensing. Especially, the high signal-to-viral load ratio (∼10% change in source-drain current and gate voltage) also demonstrates the ultra-sensitivity of the developed MXene–graphene FET sensor. In addition, the specificity of the sensor was also demonstrated by depositing the inactivated influenza A (H1N1) HA virus and the recombinant 2019-nCoV spike protein onto microfluidic channels with opposite antibodies, producing signal differences that are about 10 times lower. Thus, we have successfully fabricated a relatively low-cost, ultrasensitive, fast-responding, and specific inactivated influenza A (H1N1) and 2019-nCoV sensor with the MXene–graphene VSTM.

Study of Graphene field effect transistor (GFET) for chemical sensing application.

This paper presents a model that provides the transfer and output characteristic of a grapheme FET transistor. This model also provides close form inquiring expressions for the drain current, trance conductance of the device. In this paper we have investigate the transfer characteristic curve for chemical sensing application. In this present scenario this work proposes to develop a superfast mycotoxin sensor based on Graphene field effect transistor (GFET) using standard microelectronics technology. GFET would be able work in liquid and also in the biological substances. In this work, we will use the simulation program to explore the performance of GFET. The GFET sensor will be integrated to IoT and mobile phone platforms. This study will show that the fully integrated mycotoxin sensor with high sensitivity, fast response, and high dynamic range of toxins concentrations is feasible by proper GFET functionalization.GFET was modelled and simulated Using MATLAB software Introduction.

Graphene field effect transistor scaling for ultra-low-noise sensors

The discovery of the field effect in graphene initiated the development of graphene field effect transistor (FET) sensors, wherein high mobility surface conduction is readily modulated by surface adsorption. For all graphene transistor sensors, low-frequency 1/f noise determines sensor resolution, and the absolute measure of 1/f noise is thus a crucial performance metric for sensor applications. Here we report a simple method for reducing 1/f noise by scaling the active area of graphene FET sensors. We measured 1/f noise in graphene FETs with size 5 μm × 5 μm to 5.12 mm × 5.12 mm, observing more than five orders of magnitude reduction in 1/f noise. We report the lowest normalized graphene 1/f noise parameter observed to date, 5 × 10−13, and we demonstrate a sulfate ion sensor with a record resolution of 1.2 × 10−3 log molar concentration units. Our work highlights the importance of area scaling in graphene FET sensor design, wherein increased channel area improves sensor resolution.

Mechanism and Suppression of Physisorbed-Water-Caused Hysteresis in Graphene FET Sensors.

Hysteresis is a problem in field-effect transistors (FETs) often caused by defects and charge traps inside a gate isolating (e.g., SiO2) layer. This work shows that graphene-based FETs also exhibit hysteresis due to water physisorbed on top of graphene determined by the relative humidity level, which naturally happens in biosensors and ambient operating sensors. The hysteresis effect is explained by trapping of electrons by physisorbed water, and it is shown that this hysteresis can be suppressed using short pulses of alternating gate voltages.

Highly-sensitive graphene field effect transistor biosensor using PNA and DNA probes for RNA detection

DNA probe-based biosensors have been widely developed for detecting a range of analytes. However, the DNA probe-based sensors suffer from many problems, such as long hybridization time, background electrical noise, and relatively poor specificity. In this paper, we report the ultrasensitive detection for RNA by graphene field effect transistor (G-FET) biosensor using PNA and DNA probes. The limit of detection (LOD) of the PNA probe-modified G-FET sensor is down to 0.1 aM, which is three orders of magnitude lower than that of DNA probe-modified G-FET sensor. We demonstrate that both PNA and DNA probe-modified G-FET have great potential in quantitative detection of RNA. A good linear electrical response to RNA concentrations is obtained in a broad range from 0.1 aM to 1 pM for PNA probe-modified G-FET and from 100 aM to 1 pM for DNA probe-modified G-FET, respectively. The PNA probe-modified G-FET sensors significantly reduce the detection time compared to DNA probe-modified G-FET sensors. Moreover, the electrical response of PNA probe-modified G-FET biosensor to non-complementary RNA is negligible, showing high specificity for RNA detection. What’s more, the G-FET sensor was also used to detect RNA in human serum, making it a promising way for future detection of RNA in biomedical research and early clinical diagnosis.

Graphene field-effect transistor biosensor for detection of biotin with ultrahigh sensitivity and specificity

Because avidin and biotin molecules exhibit the most specific and strongest non-covalent interaction, avidin-biotin technology is widely used in ELISA (enzyme-linked immunosorbent assay) kits for the detection of different bio-macromolecules linked to different diseases including cancer and influenza. Combining the outstanding electrical conductivity (200,000 cm2V-1s-1) of graphene with the unique avidin and biotin interaction, we demonstrate a novel graphene field-effect transistor (GFET) biosensor for the quantitative detection of bio-macromolecules. The GFET consists of six pairs of interdigital Cr/Au electrodes supported on Si/SiO2 substrate with an avidin immobilized single layer graphene channel as the sensing platform. By monitoring the real time current change upon the addition of biotin solution in bovine serum albumin (BSA) in the silicone pool preformed onto the GFET, the lowest detectable biotin concentration is estimated to be 90 fg/ml (0.37 pM). The specificity of the GFET is confirmed both by controlled and real sample measurements. From the magnitude of current change upon the addition of different concentrations of biotin solutions, the dissociation constant Kd is estimated to be 1.6 × 10-11 M. Since biotin is capable of conjugating with proteins, nucleotides and other bio-macromolecules without altering their properties, the present GFET sensor with its ultra-high sensitivity (0.37 pM) and specificity can be tailored to the rapid point-of-care detection of different types of desired biomolecules at very low concentration level through biotinylation as well as the exogenous biotin in blood serum.

Characterizing the Interactions of DNA Oligomers with Graphene Field-Effect Transistors

Introduction: Graphene field-effect transistors (G-FETs) constitute an emerging platform for biosensing applications. Indeed, graphene is an ideal material for the detection of biomolecules: every atom of its monolayer structure is in contact with its environment, resulting in an electrical conductivity that is highly responsive to local electrostatic fluctuations from adjacent molecules. For genomic applications, most detection methods use DNA probe sequences bound to the graphene surface, in order to capture a specific target DNA sequence and detect the corresponding change in the electrical response of the sensor1 - 2. However, the effect of interactions between DNA and graphene on the electrical conductance is still not fully understood. Here, we investigate specifically the adsorption of short DNA oligomers on graphene field-effect transistors, in order to model and control the effect of such interactions in biosensing applications with G-FETs. Methods and Results: First, we fabricated G-FET sensors as follow3: Using photolithography techniques, an array of source and drain electrodes were patterned in gold on a Si wafer with a SiO2 insulator layer, as well as a common on-chip gate electrode in platinum. High-quality monolayer CVD-grown graphene was transferred onto the substrate and then etched to create 6 x 4 µm ribbons between each source-drain pair. Transfer curves (Isd vs. Vg) performed in saline buffer solution revealed a conductance minimum at the charge neutrality point of the graphene. Devices were then exposed to solutions of DNA, consisting in 22-single-stranded nucleotides (ssDNA) or double-stranded nucleotides (dsDNA) DNA oligomers diluted in 0,01X PBS buffer. Selected G-FET devices were exposed to different ssDNA concentrations during 15 min, followed by washing steps with 0,01X PBS. Electrical curves were recorded before, during and after each DNA exposure. In this presentation, we will present results showing that ssDNA exposure causes a left-shift of the charge neutrality point above a concentration threshold, and that this shift is proportional to ssDNA concentration. In addition, non-covalent adsorption of ssDNA on graphene appears to be reversible upon washing. Finally, we will discuss differences between the adsorption of dsDNA and ssDNA. Conclusion and Relevance: Our results suggest that unspecific DNA adsorption on graphene can lead to a G-FET response, which needs to be modeled, compensated and /or passivated in biosensing experiments, especially in order to achieve low detection limits for target sequences in complex biological media.