Carbon nanotubes and graphene nano field-effect transistor-based biosensors

Tran, Thien; Mulchandani, Ashok

doi:10.1016/j.trac.2015.12.002

Cited by 141 publications

(87 citation statements)

References 80 publications

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“…The TG configuration is also known as liquid gating or electrolyte gating. 11,17,93 Generally, in bottom-gate configuration, S, D and channel are fabricated on the top on SiO 2 /Si substrate where SiO 2 layer act like an insulator between graphene and heavily-doped Si (typically, p…”

Section: Graphene Based Fet Biosensing Systemmentioning

confidence: 99%

“…6,7 Among these nanomaterials, metallic nanomaterials, silica nanoparticles (NPs), dendrimers, quantum dots (QDs), polymer NPs, carbon nanotubes (CNTs), nanodiamonds (NDs), and 2D-nanomaterials [graphene, hexagonalboron nitride (h-BN), transition metal dichalcogenides (TMDs) such as niobium selenide (NbSe 2 ), molybdenum disulfide (MoS 2 ), and tungsten disulfide (WS 2 )] are highly studied and have significantly contribution to biosensor development. [8][9][10][11][12] In particular, graphene is the most promising nanostructured carbon material that is used in the bioanalytical area and has stimulated intense research as it possesses an excellent combination of enhanced specific surface area, electrical conductivity, chemical stability, ease of manipulation and biocompatibility, thus, providing more specific sites to capture foreign moieties with high sensitivity. In addition, it is resistant to the harsh ionic solutions found in body.…”

Section: Introductionmentioning

confidence: 99%

“…[95][96][97] And it has been found that there are primarily four type of mechanisms possible that are possible when a single molecule or a collection of biomolecules approach the graphene-biosensor that can give rise to a significant change in the electronic properties of graphene based-FET: (i) surface charge-inducted gating effect or electrostatic gating, (ii) charge transfer between the biomolecules and graphene (acceptor or donor), (iii) charge scattering across graphene, and (iv) Schottky-barrier formation between the graphene and metal electrodes. 11,93 For instance, Mohanty and Berry proposed the first GFET biosensor by demonstrating a single bacterium resolution interfacial device, a label-free, reversible DNA detector, and a polarity-specific molecular transistor for protein/ DNA adsorption from GO or graphene amines produced by treating GO with nitrogenous plasmas or ethylenediamine. 99 A flexible glucose sensor has been fabricated using CVD-grown GFET in TG configuration.…”

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confidence: 99%

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Graphene based biosensors—Accelerating medical diagnostics to new-dimensions

2017

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Graphene has emerged as a champion material for a variety of applications cutting across multiple disciplines in science and engineering. Graphene and its derivatives have displayed huge potential as a biosensing material due to their unique physicochemical properties, good electrical conductivity, optical properties, biocompatibility, ease of functionalization, and flexibility. Their widespread use in making biosensors has opened up new possibilities for early diagnosis of life-threatening diseases and real-time health monitoring. Following an introduction and discussion on the significance of fabrication protocols and assembly, this review is intended to assess why graphene is suitable to build better biosensors, the working of existing biosensing schemes and their current status toward commercialization for wearable diagnostic and prognostic devices. We believe this review will provide a critical insight for harnessing graphene as a suitable biosensor for the clinical diagnostics, its future prospects and challenges ahead.

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Section: Graphene Based Fet Biosensing Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Graphene based biosensors—Accelerating medical diagnostics to new-dimensions

2017

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“…Fluorescence-based techniques (total internal reflection fluorescence microscope, confocal microscopy and fluorescence correlation spectroscopy), opposed or combined to force-based techniques (optical trap, magnetic trap and atomic force microscope) (1)(2)(3)(4), offer the possibility to study molecules one at a time and to highlight reaction pathways and reaction intermediates that ensemble methods tend to hide because of their effect of averaging (5). More recently, another approach has been emerging based on single-molecule field-effect transistors (smFETs), in which an individual molecule is immobilized on a nanoscale electrical circuit, such as a carbon nanotube (6)(7)(8)(9)(10)(11)(12) or silicon nanowire (13)(14)(15)(16). By recording fluctuations in the electrical conductance of such circuits, studies have reported the realtime monitoring of transitions between different conformational states, such as hybridization (17)(18)(19)(20), folding events in nucleic acids (17,21), enzymatic catalysis with a ultra-high sensitivity and specificity (22,23), and also other applications in biology and medicine, such as imaging (24,25) and drug delivery (26,27) to cancer and brain.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Idealization of Nano-Electronic Sensors Recordings with Concept Drifts: A Compressive Feature Learning Approach for Non-Stationary Single-Molecule Data Analysis

Ouqamra

2020

Preprint

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Single-molecule nanocircuits based on field-effect transistors (smFETs) are emerging and promising nano-bioelectronic sensors for the functional detection of molecular dynamics involved in biochemical transformations, in particular for applications in cancer thanks to a potentially better understanding of some hidden and complex molecular interactions. In fact, functionalized carbon nanotubes have been recently exploited to probe molecular events occurring at a single molecule scale with ultra high sensitivity and specificity, such as nucleic acids hybridization, enzyme folding in catalysis reactions, or protein-nucleic acids interactions. Extracting the kinetics and thermodynamics from such single-molecule dynamics implies robust analytic tools that can handle the complexity of the sensed reaction system changing between transient and steady-state molecular conformations, but also some challenging signal specificities, such as the multi-source composition of the recorded signals, the mixed and high-level noises, and the sensor baseline drift, leading to non-stationary time series. We present a new smFET data analysis framework, based on a compressive feature learning scheme to optimize unsupervised idealization of smFET traces, by a precise and accurate molecular events detection and states characterization algorithm, tailored for non-stationary signals at high sampling rate and long acquisition periods, without any prior knowledge on the data generating process nor signal prefiltering. Experimental results show the accuracy and robustness of our trace idealization algorithm to stochastic state-space models, and better performances than commonly used hidden Markov models.

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“…Graphene is an allotrope of carbon which is made up of monolayer of sp2 bonded carbon atoms arranged in honeycomb lattice (Tran & Mulchandani 2016;Wang et al 2015). The carbon-carbon (C-C) bond is the basic bonding in graphene, with bond length of approximately 0.142 nm (Yan et al 2014).…”

Section: Introductionmentioning

confidence: 99%