Graphene Oxide Field Effect Transistor Research Articles

Amine and Thiol Cofunctionalized Reduced Graphene Oxide Field-Effect Transistor for the Selective Detection of Formaldehyde

Amine and Thiol Cofunctionalized Reduced Graphene Oxide Field-Effect Transistor for the Selective Detection of Formaldehyde

Integrated FET sensing microsystem for specific detection of pancreatic cancer exosomal miRNA10b

Tumor-derived exosome (TD-Ex) serves as a crucial early diagnostic biomarker of pancreatic cancer (PC). However, accurate identification of TD-Ex from PC is still a challenging work. In this paper, a detection microsystem that integrates magnetic separation and FET biosensor is developed, which is capable of selectively separating TD-Ex of PC from the plasma and detecting exosomal miRNA10b in a sensitive and specific manner. The magnetic beads were functionalized with dual antibody (GPC-1 antibody and EpCAM antibody), enabling selective recognition and capture of PC-derived exosomes. On the other hand, a peptide nucleic acid (PNA)- functionalized reduced graphene oxide field-effect transistor (RGO FET) biosensor was subsequently utilized to detect the exosomal miRNA10b, which is highly expressed in PC- derived exosomes. This system could achieve a low detection limit down to 78 fM, and selectively identify miRNA10b from single-base mismatched miRNA. In addition, 40 clinical plasma samples were tested with this microsystem, and the results indicate that it could effectively distinguish PC patients from healthy individuals. The assay combines specific capture and enrichment of PC-derived exosomes with sensitive and selective detection of exosomal miRNA, showing its potential to be used as an effective scheme for PC early diagnosis.

Feature extractions from transfer characteristics of hybrid GO FETs for selective detection of volatile organic compounds

The current work concerns a new strategy for selective classifications of multiple volatile organic compounds (VOCs) by using an array of field effect transistor (FET) type sensors and extraction of device-specific features from transfer characteristics of the FET-sensors to use in classification algorithm(s). A few layered graphene oxide (GO) was used as the base channel materials which was then functionalized very precisely with different metal oxides (WO3, TiO2) and metals (Au, Pd) nanoforms to construct an array of five FET structure sensors on SiO2/Si platform. The deviation in Id-Vgs characteristics in different VOCs were accounted with a distinctive features. The features were organized based on their importance score and used to construct different feature matrices. A minimum four sensors and six features were required to successful classification of seven VOCs by linear discriminant analysis (LDA). The classification accuracy was tested with supportive vector machine (SVM) classifier and it was 92.86%.

A Multivariate Computational Approach With Hybrid Graphene Oxide Sensor Array for Partial Fulfillment of Breath Acetone Sensing

Breath analysis is becoming more prominent in the field of medical science because of its noninvasive nature. In the human breath, there exist thousands of volatile organic components; however, only a limited number are correlated with a specific disease. Acetone is the disease marker for diabetes, and an acetone concentration of more than 1800 ppb in a person’s breath indicates diabetes. In this study, different concentrations of acetone were detected in a dummy exhaled human breath consisting of a large number of interfering volatile organic compounds (VOCs), relative humidity (70%), and synthetic air. An array of six sensors comprised of hybridized graphene oxide (GO) field-effect transistors (FETs) was fabricated and tested for the detection of acetone within the concentration range of approximately 400 ppb to 80 ppm. A GO channel was hybridized with noble metal nanoparticles (NPs) like Au, Pd, and Pt along with WO3 flower-like nanostructures to enhance the acetone selective behavior under suitable back gate voltage in the FET structured sensors. A wide variety of feature vectors were extracted from the sensor data and used via linear discriminant analysis (LDA) and principal component analysis (PCA) to discriminate acetone with different stages of interfaces. Then, the exact acetone concentrations were quantified with a polynomial curve fitting in the sensitivity versus concentration characteristics of all the individual sensors. Acetone concentration in a complex VOC mixture was detected with an average 6.65% relative error by implementing the above technique.

Ultrasensitive detection of exosomal miRNA with PMO-graphene quantum dots-functionalized field-effect transistor biosensor

SummaryCompared with the conventional DNA probe immobilization on the planar surface, nanoparticles-based DNA probes enable more RNA molecules to be anchored to the sensor surface, thereby improving the detection sensitivity. In this work, we report phosphorodiamidate morpholino oligomers (PMO)-graphene quantum dots (GQDs)-functionalized reduced graphene oxide (RGO) field effect transistor (FET) biosensors for ultrasensitive detection of exosomal microRNAs. After the RGO FET sensor was fabricated, polylysine (PLL) film was deposited onto the RGO surface. GQDs-PMO hybrid was prepared and covalently bound to PLL surface, enabling detection of exosomal microRNAs (miRNAs). The method achieved a detection limit as low as 85 aM and high specificity. Furthermore, the FET sensor was able to detect exosomal miRNAs in plasma samples and distinguish breast cancer samples from healthy samples. Compared with other methods, we use GQDs to further improve the sensitivity of FET, making it a potential tool for early diagnosis of breast cancer.

ANN based approach for selective detection of breath acetone by using hybrid GO-FET sensor array

This research used hybrid graphene oxide (GO) field effect transistors (FETs) based sensor array to design an electronic nose (e-nose) for identifying exhaled breath acetone to diagnose diabetes mellitus through noninvasive route. Six back gated FET sensors were fabricated with hybrid channel of GO, WO3 and noble metals (Au, Pd and Pt) nanoparticles. The experiment was carried out by using four distinct forms of synthetic breath, each with a different level of interference. Linear discriminant analysis (LDA) and artificial neural networks (ANN) were utilized to classify and analyze the sensor response vector. In contrast, partial least square (PLS) and multiple linear regression (MLR) were used to evaluate the exact acetone concentration in synthetic breath. First, LDA was used to lower the dimensionality of the response vector, which was then provided as an input to the ANN model. ANN was performed with ten perceptrons model in the hidden layer and highest accuracy of 99.1% was achieved. Additionally, by using the loading plot of PLS, three sensors (Pt/WO3/GO, Pd/WO3/GO, and WO3/GO) had the ample use to predict the concentration of breath acetone. Moreover, the MLR approach with correlation coefficient (R2) of 0.9572 and root mean square error (RMSE) of 5.63% were used for obtaining the exact concentration of acetone. Consequently, e-nose with matrix of hybrid GO-FET sensors and pattern recognition algorithms (LDA, ANN, PLS and MLR) exhibited considerable ability in selective detection of acetone in synthetic breath.

Realization of ppb-level acetone detection using noble metals (Au, Pd, Pt) nanoparticles loaded GO FET sensors with simultaneous back-gate effect

In the current study, graphene oxide (GO), uniformly loaded with different noble metal nanoparticles (Au, Pd and Pt) were synthesized by spray coating technique and implemented in field effect transistor (FET) sensors. The morphology, structure, composition and electronic properties of the synthesized materials were characterized. The sensing results indicated that the incorporation of noble metals can greatly enhance the VOC sensing properties of a few layers of GO by using their chemical and electrical sensitization effect. At optimized gate potential, GO FET exhibited outstanding sensing properties at low operating temperature i.e. 50 °C. Specifically, the FET sensor based on Pd loaded GO exhibited the highest response, quickest response/recovery (37 s/91 s) characteristics, best selectivity and low operating temperature towards low concentration of acetone. Almost five times higher sensitivity towards acetone (400 ppb) was achieved in Pd/GO as compared to the pure GO channel nanoparticles under a suitable back gate bias.

Design and Development of Graphene FET Biosensor for the Detection of SARS-CoV-2

The most affected disease in recent years is Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2) that is notable as COVID-19. It has been started as a disease in one place and arisen as a pandemic throughout the world. A serious health problem is developed in the lungs due to the effect of this coronavirus. Sometimes it may result in death as a consequence of extensive alveolar damage and progressive respiratory failure. Hence, early detection and appropriate diagnosis of corona virus in patient’s body is very essential to save the lives of affected patients This work evolves a Silicon (Si) based label-free electrical device i.e. the reduced graphene oxide field-effect transistor (rGO FET) for SARS-CoV-2 detection. Firstly rGO FET functionalized with SARS-CoV-2 monoclonal antibodies (mAbs). Then the rGO FET characteristic response is observed to detect the antibody-antigen reaction of SARS-CoV-2 with different molar ranges. The developed GFET shows better performance towards the drain current and limit-of-detection (LoD) up to 2E-18 M. Therefore, we believe that an intense response was observed than the earlier developed devices and signifies impressive capability for subsequent implementation in point-of-care (PoC) diagnostic tests.

Hybrid Modeling and Sensitivity Analysis on Reduced Graphene Oxide Field-Effect Transistor

The reduced graphene oxide (RGO) field-effect transistor (FET) has been developed and applied in various areas. However, the effective modeling and sensitivity analysis on RGO FET is still a very challenging problem due to the randomness of bandgap and density of states (DOS) in RGO. In this paper, we propose to solve the RGO FET modeling problem by integrating the data-driven thinking and the graphene FET model to develop a hybrid model. The proposed model takes advantages of the similarities between graphene and RGO to generalize the existing graphene FET model, and employs RGO FET drain-current data to characterize the specificity of the model. The basic idea in the proposed model is to modify the graphene DOS to approximate the RGO DOS so that the charge density, mobility and other parameters can be achieved through the approximated RGO DOS. We validate the model accuracy with the RGO FET based sensors that detect chemical concentrations in the aqueous environment. The RGO FET sensitivity analysis is also demonstrated to provide guidance for RGO FET application and manufacturing.

Graphene Field Effect Transistor Biosensors Based on Aptamer for Amyloid-βDetection

The development of cost-efficient, sensitive and specific methods to detect amyloid-beta 42 ( $\text{A}\beta 42$ ) biomarkers in cerebrospinal fluid and serum-samples is of considerable interest to enable early and reliable diagnosis of Alzheimer’s disease as a precondition for future disease-modifying therapies. This paper presents a reduced graphene oxide field effect transistor (r-GO FET) for label free ultrasensitive detection of an $\text{A}\beta 42$ -biomarker with RNA aptamer. The channel in the device was formed by reduction of graphene oxide nanosheets by self-assembly process. As a result, the interaction between $\text{A}\beta 42$ and RNA aptamer on the surface of r-GO channel caused a linear response in the shift of the gate voltage ( $\text{V}_{\mathbf {TG}}$ ) where the minimum conductivity occurs. The r-GO FET can detect the biomarker in range of 1ng/ml to 1pg/ml at pH 7.4 with high specificity. The developed r-GO FET is a low-cost, highly sensitive and selective method for detecting tiny concentrations of $\text{A}\beta 42$ , which would also enable measurements in serum-samples.

Reduced graphene oxide-based field effect transistors for the detection of E7 protein of human papillomavirus in saliva.

Several challenging biological sensing concepts have been realized using electrolyte-gated reduced graphene oxide field effect transistors (rGO-FETs). In this work, we demonstrate the interest of rGO-FET for the sensing of human papillomavirus (HPV), one of the most common sexually transmitted viruses and a necessary factor for cervical carcinogenesis. The highly sensitive and selective detection of the HPV-16 E7 protein relies on the attractive semiconducting characteristics of pyrene-modified rGO functionalized with RNA aptamer Sc5-c3. The aptamer-functionalized rGO-FET allows for monitoring the aptamer-HPV-16 E7 protein binding in real time with a detection limit of about 100 pg mL−1 (1.75 nM) for HPV-16 E7 from five blank noise signals (95% confidence level). The feasibility of this method for clinical application in point-of-care technology is evaluated using HPV-16 E7 protein suspended in saliva and demonstrates the successful fabrication of a promising field effect transistor biosensor for HPV diagnosis.Graphical abstract

Multiplexed femtomolar detection of Alzheimer's disease biomarkers in biofluids using a reduced graphene oxide field-effect transistor

Alzheimer's disease (AD) is a neurodegenerative disease that accounts for 70% of all dementia. Early stage diagnosis of AD is essential as there is no certain treatment after the lesion has progressed in the late stage. Nevertheless, there are still limitations of early diagnosis of AD using neuroimaging and psychological memory assessments. Here, we demonstrate ultrasensitive and multiplexed detection of pivotal AD biomarkers (Aβ1-42 and t-Tau) in biofluids using a reduced graphene oxide field-effect transistor (gFET). The proposed approach provides a wide logarithmically linear range of detection from 10−1–105 pg mL−1 and a femtomolar-level limit of detection in biofluids (human plasma and artificial cerebrospinal fluid) as well as phosphate-buffered saline (PBS). Furthermore, as these core biomarkers have different surface charges in physiological conditions based on the isoelectric point (pI), we achieved a distinctive output signal for each biomarker. The gFET biosensor platform presented in this paper has great potential and can be used for early diagnosis of AD in clinical practice as well as accurate analysis based on the surface charge of the analytes.

All-inkjet-printed high-performance flexible MoS2 and MoS2-reduced graphene oxide field-effect transistors

Two-dimensional (2D) materials have been utilized to design flexible field-effect transistors (FETs) with promising performance. However, flexible FETs still face challenges with poor switching features and ultra-low drive current. In this paper, a facile and repeatable large-area integration process is presented for inkjet-printed FETs with 2D materials active channels and PI films as gate dielectrics. The MoS2 FETs reported here exhibit n-type channel feature with an outstanding average subthreshold swing of 75 mV/dec, an on-state/off-state current ratio of 104, and on-state current up to 10 μA at a power supply voltage of 3.0 V. Besides, MoS2–rGO FETs also exhibit n-type semiconductor features with good electrical properties by the inkjet-printing technology.

(Invited) Graphene-Based Field Effect Transistor Biosensor for Electrical and Label-Free Quantification of Exosomes

Exosomes are small membrane-bound nanovesicles with a size of 50-150 nm, which contain many functional biomolecules, such as nucleic acids and proteins. Due to their high homology with parental generation, they are of great significance in clinical diagnosis. At present, the quantitative detection of low-concentration of cancer-derived exosomes present in biofluids is still a great challenge. In this study, we develop an electrical and label-free method to directly detect exosomes with high sensitivity based on reduced graphene oxide (RGO) field effect transistor (FET) biosensor. The RGO FET biosensor modified with specific antibody CD63 in the sensing area was fabricated, which was used for electrical and label-free quantification of exosomes. The method achieved a low limit of detection down to 33 particles/µL, which was lower than that of many other available methods. In addition, the FET biosensor was employed to detect exosomes in clinical serum samples, showing significant differences in detecting healthy people and prostate cancer (PCa) patients. Different from other technologies, this study provides a unique technology capable of directly quantifying exosomes without labeling, indicating its potential as a tool for early diagnosis of cancer.

Dual-Aptamer Modified Graphene Field-Effect Transistor Nanosensor for Label-Free and Specific Detection of Hepatocellular Carcinoma-Derived Microvesicles

Cancerous microvesicles (MVs), which are heterogeneous membrane-bound nanovesicles shed from the surfaces of cancer cells into the extracellular environment, have been widely recognized as promising "biofingerprints" for various cancers. High-performance identification of cancerous MVs plays a vital role in the early diagnosis of cancer, yet it is still technically challenging. Herein, we report a gold nanoparticle (AuNP)-decorated, dual-aptamer modified reduced graphene oxide (RGO) field-effect transistor (AAP-GFET) nanosensor for the label-free, specific, and sensitive quantification of HepG2 cell-derived MVs (HepG2-MVs). After GFET chips were fabricated, AuNPs were then decorated on the RGO surface. For specific capture and detection of HepG2-MVs, both sulfhydrylated HepG2 cell specific TLS11a aptamer (AptTLS11a) and epithelial cell adhesion molecule aptamer (AptEpCAM) were immobilized on the AuNP surface through an Au-S bond. This developed nanosensor delivered a broad linear dynamic range from 6 × 105 to 6 × 109 particles/mL and achieved a high sensitivity of 84 particles/μL for HepG2-MVs detection. Moreover, this AAP-GFET platform was able to distinguish HepG2-MVs from other liver cancer-related serum proteins (such as AFP and CEA) and MVs derived from human normal cells and other cancer cells of lung, pancreas, and prostate, suggesting its excellent method specificity. Compared with those modified with a single type of aptamer alone (AptTLS11a or AptEpCAM), such an AAP-GFET nanosensor showed greatly enhanced signals, suggesting that the dual-aptamer-based bio-nano interface was uniquely designed and could realize more sensitive quantification of HepG2-MVs. Using this platform to detect HepG2-MVs in clinical blood samples, we found that there were significant differences between healthy controls and hepatocellular carcinoma (HCC) patients, indicating its great potential in early HCC diagnosis.

Electrical and Label-Free Quantification of Exosomes with a Reduced Graphene Oxide Field Effect Transistor Biosensor.

Exosomes are small membrane-bound nanovesicles with a size of 50-150 nm which contain many functional biomolecules, such as nucleic acids and proteins. Due to their high homology with parental generation, they are of great significance in clinical diagnosis. At present, the quantitative detection of low concentrations of cancer-derived exosomes present in biofluids is still a great challenge. In this study, we develop an electrical and label-free method to directly detect exosomes with high sensitivity based on a reduced graphene oxide (RGO) field effect transistor (FET) biosensor. An RGO FET biosensor modified with specific antibody CD63 in the sensing area was fabricated and was used for electrical and label-free quantification of exosomes. The method achieved a low limit of detection down to 33 particles/μL, which is lower than that of many other available methods. In addition, the FET biosensor was employed to detect exosomes in clinical serum samples, showing significant differences in detecting healthy people and prostate cancer (PCa) patients. Different from other technologies, this study provides a unique technology capable of directly quantifying exosomes without labeling, indicating its potential as a tool for early diagnosis of cancer.

Clinical available circulating tumor cell assay based on tetra(4-aminophenyl) porphyrin mediated reduced graphene oxide field effect transistor

Circulating tumor cell (CTC) assay is one of the emerging “liquid biopsy” for cancer's diagnostic, an easy-to-operate and state-of-the-art CTC sensor is the imperative need of clinical medical laboratory. A label-free and solid-state sensor for clinical sample's CTC detection based on an aptamer (AS1411) functionalized graphene field effect transistor (GFET) by using tetra(4-aminophenyl) porphyrin mediated reduced graphene oxide as the channel material. Objects are CTCs of A549, MDA-MB-231, HeLa and HUVEC (as a control) in buffer solutions and EDTA anti-coagulant whole blood samples, as well as the anti-coagulant clinical blood samples from 49 breast cancer patients and 5 healthy persons. The sensitivities for CTCs in the range of 10 - 106 cells/ml are 3.14, 3.32, 2.60%/log10(cells/ml) for A549 (R2 = 0.97), MDA-MB-231 (R2 = 0.96) and HeLa (R2 = 0.98), respectively. Meanwhile, the selectivity for CTCs is also confirmed by the low sensitivity and R2 for HUVEC which are −0.13%/log10(cells/ml) and 0.50. Furthermore, whole blood interference is found to make the sensing range narrow. At last, their applications in grading clinical blood samples are found to be in agreements with the parallel diagnostic conclusions of these patients, samples of adenofibroma and effect of chemotherapy can also be identified. The aptamer specific, directly electronic CTC detection is accomplished by the proposed GFET based CTC sensor, it can provide a novel analytical technique for cancers' clinical assay.

(Invited) Detection of Heart Failure-Related Biomarker in Whole Blood with Graphene Field Effect Transistor Biosensor

Since brain natriuretic peptide (BNP) has become internationally recognized biomarkers in the diagnosis and prognosis of heart failure (HF), it is highly desirable to search for a novel sensing tool for detecting the patient’s BNP level at the early stage. Here we report a platinum nanoparticles (PtNPs)-decorated reduced graphene oxide (rGO) field effect transistor (FET) biosensor coupled with a microfilter system for label-free and highly sensitive detection of BNP in whole blood. The PtNPs-decorated rGO FET sensor was obtained by drop-casting rGO onto the pre-fabricated FET chip and subsequently assembling PtNPs on the graphene surface. After anti-BNP was bound to the PtNPs surface, BNP was successfully detected by the anti-BNP immobilized FET biosensor. It was found that the developed FET biosensor was able to achieve a low detection limitation of 100 fM. Moreover, BNP was successfully detected in human whole blood sample treated by a custom-made microfilter, suggesting the sensor’s capability of working in a complex sample matrix. The developed FET biosensor provides a new sensing platform for protein detection, showing its potential applications in clinic sample.

Microvesicle detection by a reduced graphene oxide field-effect transistor biosensor based on a membrane biotinylation strategy.

Unlike other extracellular vesicle (EV) subtypes such as exosomes, the lack of well-defined universal markers on the surface of microvesicles (MVs) has led to difficulty in the detection of the entire MV population. To design a universal MV detection method, we reported highly sensitive electrical detection of MVs using a reduced graphene oxide (RGO)-based field-effect transistor (FET) biosensor by the introduction of a membrane biotinylation strategy in this work. Biotinylated MVs (B-MVs) were obtained by supplying the culture medium with 1,2-distearoyl-sn-glycero-3-phosphoethanolamine-N-[biotinyl(polyethylene glycol)-2000] (DSPE-PEG-biotin) while cultivating the cells. Excellent biotinylation efficiency of MVs (92.6%) was then realized. A streptavidin (SA) probe was subsequently modified onto the channel surface of the as-fabricated RGO-based FET device, which was capable of specifically recognizing B-MVs due to the high affinity between SA and biotin in a 1 : 4 recognition format. The results showed that the RGO-based FET biosensor could detect B-MVs in a wide range from 105 particles per mL to 109 particles per mL with a low detection limit down to 20 particles per μL, which was the lowest value compared with other previously reported results. This platform also allowed distinguishing B-MVs from other unbiotinylated EV types such as MVs and exosomes, exhibiting excellent specificity. Moreover, this FET biosensor demonstrated the capability of detecting B-MVs derived from different cell lines including cancer cells and normal cells, indicating its versatility and potential applications in the biomedical field.

Cascading reaction of arginase and urease on a graphene-based FET for ultrasensitive, real-time detection of arginine

Herein, a biosensor based on a reduced graphene oxide field effect transistor (rGO-FET) functionalized with the cascading enzymes arginase and urease was developed for the detection of L-arginine. Arginase and urease were immobilized on the rGO-FET sensing surface via electrostatic layer-by-layer assembly using polyethylenimine (PEI) as cationic building block. The signal transduction mechanism is based on the ability of the cascading enzymes to selectively perform chemical transformations and prompt local pH changes, that are sensitively detected by the rGO-FET. In the presence of L-arginine, the transistors modified with (PEI/urease(arginase)) multilayers showed a shift in the Dirac point due to the change in the local pH close to the graphene surface, produced by the catalyzed urea hydrolysis. The transistors were able to monitor L-arginine in the 10–1000 μM linear range with a LOD of 10 μM, displaying a fast response and a good long-term stability. The sensor showed stereospecificity and high selectivity in the presence of non-target amino acids. Taking into account the label-free, real-time measurement capabilities and the easily quantifiable, electronic output signal, this biosensor offers advantages over state-of-the-art L-arginine detection methods.