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Title: Imaging electric field dynamics with graphene optoelectronics

Abstract

The use of electric fields for signalling and control in liquids is widespread, spanning bioelectric activity in cells to electrical manipulation of microstructures in lab-on-a-chip devices. However, an appropriate tool to resolve the spatio-temporal distribution of electric fields over a large dynamic range has yet to be developed. Here we present a label-free method to image local electric fields in real time and under ambient conditions. Our technique combines the unique gate-variable optical transitions of graphene with a critically coupled planar waveguide platform that enables highly sensitive detection of local electric fields with a voltage sensitivity of a few microvolts, a spatial resolution of tens of micrometres and a frequency response over tens of kilohertz. Our imaging platform enables parallel detection of electric fields over a large field of view and can be tailored to broad applications spanning lab-on-a-chip device engineering to analysis of bioelectric phenomena.

Authors:
 [1];  [1];  [2];  [3];  [3];  [1];  [2];  [1]
  1. Univ. of California, Berkeley, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  2. Stanford Univ., Stanford, CA (United States)
  3. Univ. of California, Berkeley, CA (United States)
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
OSTI Identifier:
1413714
Grant/Contract Number:  
AC02-05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
Nature Communications
Additional Journal Information:
Journal Volume: 7; Journal ID: ISSN 2041-1723
Publisher:
Nature Publishing Group
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; 59 BASIC BIOLOGICAL SCIENCES

Citation Formats

Horng, Jason, Balch, Halleh B., McGuire, Allister F., Tsai, Hsin -Zon, Forrester, Patrick R., Crommie, Michael F., Cui, Bianxiao, and Wang, Feng. Imaging electric field dynamics with graphene optoelectronics. United States: N. p., 2016. Web. doi:10.1038/ncomms13704.
Horng, Jason, Balch, Halleh B., McGuire, Allister F., Tsai, Hsin -Zon, Forrester, Patrick R., Crommie, Michael F., Cui, Bianxiao, & Wang, Feng. Imaging electric field dynamics with graphene optoelectronics. United States. doi:10.1038/ncomms13704.
Horng, Jason, Balch, Halleh B., McGuire, Allister F., Tsai, Hsin -Zon, Forrester, Patrick R., Crommie, Michael F., Cui, Bianxiao, and Wang, Feng. Fri . "Imaging electric field dynamics with graphene optoelectronics". United States. doi:10.1038/ncomms13704. https://www.osti.gov/servlets/purl/1413714.
@article{osti_1413714,
title = {Imaging electric field dynamics with graphene optoelectronics},
author = {Horng, Jason and Balch, Halleh B. and McGuire, Allister F. and Tsai, Hsin -Zon and Forrester, Patrick R. and Crommie, Michael F. and Cui, Bianxiao and Wang, Feng},
abstractNote = {The use of electric fields for signalling and control in liquids is widespread, spanning bioelectric activity in cells to electrical manipulation of microstructures in lab-on-a-chip devices. However, an appropriate tool to resolve the spatio-temporal distribution of electric fields over a large dynamic range has yet to be developed. Here we present a label-free method to image local electric fields in real time and under ambient conditions. Our technique combines the unique gate-variable optical transitions of graphene with a critically coupled planar waveguide platform that enables highly sensitive detection of local electric fields with a voltage sensitivity of a few microvolts, a spatial resolution of tens of micrometres and a frequency response over tens of kilohertz. Our imaging platform enables parallel detection of electric fields over a large field of view and can be tailored to broad applications spanning lab-on-a-chip device engineering to analysis of bioelectric phenomena.},
doi = {10.1038/ncomms13704},
journal = {Nature Communications},
number = ,
volume = 7,
place = {United States},
year = {2016},
month = {12}
}

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