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Imaging and Analysis of Encapsulated Objects through Self-Assembled Electron and Optically Transparent Graphene Oxide Membrane

Published

Author(s)

Alexander Yulaev, Alexey Lipatov, Annie Xi Lu, Alexander Sinitskii, Leite Marina, Andrei Kolmakov

Abstract

We demonstrate a technique for facile adhesion and encapsulation of micro- and nano objects on arbitrary substrates, stencils, and micro structured surfaces by ultrathin graphene oxide membranes via a simple drop casting of graphene oxide solution. A stable encapsulating membrane forms during the drying process at liquid-air and liquid-solid interfaces and consists of a water-permeable quasi-2D network of overlapping graphene oxide flakes. Upon drying and interlocking between the flakes, the encapsulating graphene oxide coating around the object becomes mechanically robust, chemically protective, and yet highly transparent to electrons and photons in a wide energy range, enabling microscopic and spectroscopic access to encapsulated objects. The characteristic encapsulation scenarios were demonstrated on a set of representative inorganic and organic micro and nano-objects and microstructured surfaces. Different encapsulation regimes can be achieved by controlling the pH of a solution, and the hydrophobicity and morphology of interfaces. Several specific phenomena such as compression of encapsulated objects by contracting membranes as well as hierarchical encapsulations were observed. Finally, electron as well as optical microscopy and analysis of encapsulated objects along with membrane effect on image contrast formation, and signal attenuation are discussed.
Citation
Advanced Functional Materials
Volume
4
Issue
2

Keywords

graphene oxide, encapsulation, scanning electron microscopy, histology, forensics

Citation

Yulaev, A. , Lipatov, A. , , A. , Sinitskii, A. , Marina, L. and Kolmakov, A. (2017), Imaging and Analysis of Encapsulated Objects through Self-Assembled Electron and Optically Transparent Graphene Oxide Membrane, Advanced Functional Materials, [online], https://doi.org/10.1002/admi.201600734, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=920938 (Accessed July 27, 2021)
Created January 22, 2017, Updated June 24, 2021