Dielectrophoresis-Enhanced Graphene Field-Effect Transistors for Nano-Analyte Sensing
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https://figshare.com/articles/dataset/Dielectrophoresis-Enhanced_Graphene_Field-Effect_Transistors_for_Nano-Analyte_Sensing/29127883
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资源简介:
Dielectrophoretic (DEP) sensing is an extremely important
sensing
modality that enables the rapid capture and detection of polarizable
particles of nanoscale size. This makes it a versatile tool for applications
in medical diagnostics, environmental monitoring, and materials science.
Because DEP relies upon the creation of sharp electrode edges, its
sensitivity is fundamentally limited by the electrode thickness. Graphene,
with its monolayer thickness, enables scaling of the DEP force, allowing
trapping of particles at graphene edges at ultralow voltages. However,
to date, this enhanced trapping efficiency of graphene has not been
translated into an effective sensing geometry. Here, we demonstrate
the expansion of graphene DEP trapping capability into a graphene
field effect transistor (GFET) geometry that allows the trapped particles
to be electrically detected. This four-terminal multifunctional hybrid
device structure operates in three distinct modes: DEP, GFET, and
DEP-GFET. By segmenting the channel of the GFET into multiple parallel
channels, greatly increased density of particle trapping is demonstrated
using fluorescence microscopy analysis. We show further enhancement
of the trapping efficiency using engineered “nanosites,”
which are holes in the graphene with size on the order of 200–300
nm. Scanning electron microscope analysis of immobilized gold nanoparticles
(AuNPs) shows trapping efficiency >90% for properly engineered
nanosites.
We also demonstrate real-time, rapid electrical sensing of AuNPs,
with >2% current change occurring in 4.1 s, as well as rapid sensing
of a variety of biomolecule-coated nanoparticles. This work shows
that graphene DEP is an effective platform for nanoparticle and biomolecule
sensing that overcomes diffusion-limited and Brownian motion-based
interactions.
创建时间:
2025-05-22



