Attomolar Sensitivity of FET Biosensor Based on Smooth and Reliable Graphene Nanogrids

Basu, J.; RoyChaudhuri, C.

doi:10.1109/led.2016.2526064

Cited by 45 publications

(15 citation statements)

References 31 publications

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“…This hinders the reliable placement of GNRs to form a continuous network, restricting the detection limit to a few µM in FET biosensors [9]. These challenges of graphene nanostructures have been addressed recently by a reliable and scalable chemical method for fabrication of smooth graphene nanogrids which has resulted in attomolar sensitivity of a FET biosensor [10]. This has been attributed to the combined effects of insignificant line edges, quantum dot-like transport behaviors and improved interaction of the biomolecules within the nanopores.…”

Section: Introductionmentioning

confidence: 99%

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Graphene Nanogrids FET Immunosensor: Signal to Noise Ratio Enhancement

Basu

RoyChaudhuri

2016

Sensors

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Recently, a reproducible and scalable chemical method for fabrication of smooth graphene nanogrids has been reported which addresses the challenges of graphene nanoribbons (GNR). These nanogrids have been found to be capable of attomolar detection of biomolecules in field effect transistor (FET) mode. However, for detection of sub-femtomolar concentrations of target molecule in complex mixtures with reasonable accuracy, it is not sufficient to only explore the steady state sensitivities, but is also necessary to investigate the flicker noise which dominates at frequencies below 100 kHz. This low frequency noise is dependent on the exposure time of the graphene layer in the buffer solution and concentration of charged impurities at the surface. In this paper, the functionalization strategy of graphene nanogrids has been optimized with respect to concentration and incubation time of the cross linker for an enhancement in signal to noise ratio (SNR). It has been interestingly observed that as the sensitivity and noise power change at different rates with the functionalization parameters, SNR does not vary monotonically but is maximum corresponding to a particular parameter. The optimized parameter has improved the SNR by 50% which has enabled a detection of 0.05 fM Hep-B virus molecules with a sensitivity of around 30% and a standard deviation within 3%. Further, the SNR enhancement has resulted in improvement of quantification accuracy by five times and selectivity by two orders of magnitude.

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Section: Introductionmentioning

confidence: 99%

“…For this purpose, graphene nanogrids FET immunosensor has been developed following a similar methodology reported earlier [10]. This has been followed by optimizing the incubation time and concentration of glutaraldehyde as the crosslinker.…”

Section: Introductionmentioning

confidence: 99%

Graphene Nanogrids FET Immunosensor: Signal to Noise Ratio Enhancement

Basu

RoyChaudhuri

2016

Sensors

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“… [65] Hepatitis B virus rGO 50aM Basu et al. [66] HIV Graphene 1 pM Kwon et al. [67] HIV Graphene 10 fg/mL Islam et al.…”

Section: Graphene-based Fet Biosensors For Virus Detectionmentioning

confidence: 99%

Graphene-based field-effect transistor biosensors for the rapid detection and analysis of viruses: A perspective in view of COVID-19

2021

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“…Graphene oxide based nanoparticle and immunosensor can be used for food safety screening of viruses 66 . As a novel technology, reduced graphene oxide (RGO) nanogrids are used to detect hepatitis B virus based on a nanoporous silicon oxide template 67 . Scanning electron microscopy, surface profilometry, Raman, and conductance measurement are used to verify the RGO nanogrid structure.…”

Section: Nanomaterials In Detection Of Foodborne Virusesmentioning

confidence: 99%

Recent Advances in Biosensor Development for Foodborne Virus Detection

Neethirajan¹,

Ahmed²,

Chand³

et al. 2017

Nanotheranostics

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Outbreaks of foodborne diseases related to fresh produce have been increasing in North America and Europe. Viral foodborne pathogens are poorly understood, suffering from insufficient awareness and surveillance due to the limits on knowledge, availability, and costs of related technologies and devices. Current foodborne viruses are emphasized and newly emerging foodborne viruses are beginning to attract interest. To face current challenges regarding foodborne pathogens, a point-of-care (POC) concept has been introduced to food testing technology and device. POC device development involves technologies such as microfluidics, nanomaterials, biosensors and other advanced techniques. These advanced technologies, together with the challenges in developing foodborne virus detection assays and devices, are described and analysed in this critical review. Advanced technologies provide a path forward for foodborne virus detection, but more research and development will be needed to provide the level of manufacturing capacity required.

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Attomolar Sensitivity of FET Biosensor Based on Smooth and Reliable Graphene Nanogrids

Cited by 45 publications

References 31 publications

Graphene Nanogrids FET Immunosensor: Signal to Noise Ratio Enhancement

Graphene Nanogrids FET Immunosensor: Signal to Noise Ratio Enhancement

Graphene-based field-effect transistor biosensors for the rapid detection and analysis of viruses: A perspective in view of COVID-19

Recent Advances in Biosensor Development for Foodborne Virus Detection

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