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Raman Mapping Investigation of Graphene on Transparent Flexible Substrate: The Strain Effect
Abstract
We report a Raman mapping investigation of strain effects on graphene on transparent and flexible substrate. Raman mappings reveal a significant red-shift of the 2D mode with introduction of tensile strain, distribution of local strain in the strained graphene, and immediate recovery after strain relaxation. The systematic fitting and statistical analysis quantify the tensile strain sensitivity of graphene, which is comparable to the single-walled carbon nanotubes (SWNTs) and implies the potential of graphene as an ultrasensitive strain sensor. The uniaxial strain will break the sublattice symmetry of graphene, hence changing its electronic band structures, for example, bandgap opening. This suggests the potential to desirably tune electronic band structures of graphene by controllably introducing strain.
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Cites
Experimental observation of the quantum Hall effect and Berry's phase in graphene
Zhang, Y., Tan, Y.-W., Stormer, H. L., & Kim, P. (2005) · Nature · doi:10.1038/nature04235 · Cited by 13,435
Cited by
Stretchable Graphene Thermistor with Tunable Thermal Index
Yan, C., Wang, J., & Lee, P. S. (2015) · ACS Nano · doi:10.1021/nn507441c · Cited by 359
Self-powered graphene thermistor
Bendi, R., Bhavanasi, V., Parida, K., Nguyen, V. C., Sumboja, A., Tsukagoshi, K., & Lee, P. S. (2016) · Nano Energy · doi:10.1016/j.nanoen.2016.06.014 · Cited by 30
Metadata
- Authors
- Yu, T., Ni, Z., Du, C., You, Y., Wang, Y., & Shen, Z.
- Venue
- The Journal of Physical Chemistry C
- Publisher
- American Chemical Society (ACS)
- Published
- 2008-07-31
- Volume
- 112
- Issue
- 33
- Pages
- 12602-12605
- ISSN
- 1932-7447 (ISSN-L)
- 1932-7455
- OpenAlex
- W2096453913
- Cited by
- 278
- References
- 1
- FWCI
- 9.26
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
