journal article·2021-04-01

Protein interactions with chemical vapor deposited graphene modified by substrate

Eleanor L Brightbill, Katherine T Young, Hilena F Gezahagne, Decarle S Jin, Bryce Hitchcock, Eric M Vogel

2D Materials· Volume 8· Issue 2
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Abstract

Abstract Graphene has been utilized in sensors to detect a wide range of biomolecules (e.g. glucose, DNA, antigens, enzyme activity, dopamine) using various sensing modalities (e.g. surface plasmon resonance, potentiometry, electro-impedance spectroscopy, cyclic voltammetry). However, while graphene-based biosensors have been demonstrated in many different architectures, little attention has been given to the effects of the substrate that supports the atomically thin graphene layer. In this work, we investigate protein adhesion of model small (lysozyme), medium (bovine serum albumin), and large (fibrinogen) proteins on monolayer graphene with support substrates of varying hydrophobicity and surface polarity. Ex situ adsorption is measured via ellipsometry. For Au and Si support substrates, in situ adhesion of lysozyme is measured via quartz crystal microbalance. The results indicate that not only the equilibrium attachment, but also the kinetics of interaction, can be affected by the substrate. Overall, a more hydrophobic substrate leads to a larger amount of adsorption to graphene. Moreover, the effect is only observed with monolayer graphene, where no substrate effect is observed with the addition of a second graphene layer. This work indicates that the substrate of a graphene-based biosensor is an important but currently overlooked parameter when understanding and optimizing the performance of the device. The level of non-selective protein adsorption on graphene can be independently engineered through modifying the support substrate without directly modifying the graphene itself.
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Research context

OpenAlex topics: Advanced biosensing and bioanalysis techniques · Graphene and Nanomaterials Applications · Electrochemical sensors and biosensors

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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