five

Dielectrophoresis-Enhanced Graphene Field-Effect Transistors for Nano-Analyte Sensing

收藏
NIAID Data Ecosystem2026-05-02 收录
下载链接:
https://figshare.com/articles/dataset/Dielectrophoresis-Enhanced_Graphene_Field-Effect_Transistors_for_Nano-Analyte_Sensing/29127883
下载链接
链接失效反馈
官方服务:
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
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作