The DOI matches its Crossref record; 68 of the 68 references it cites were found in a registry or an index.
Progress and Challenges in Transfer of Large‐Area Graphene Films
Open accessAbstract
Graphene, the thinnest, strongest, and stiffest material with exceptional thermal conductivity and electron mobility, has increasingly received world‐wide attention in the past few years. These unique properties may lead to novel or improved technologies to address the pressing global challenges in many applications including transparent conducting electrodes, field effect transistors, flexible touch screen, single‐molecule gas detection, desalination, DNA sequencing, osmotic energy production, etc. To realize these applications, it is necessary to transfer graphene films from growth substrate to target substrate with large‐area, clean, and low defect surface, which are crucial to the performances of large‐area graphene devices. This critical review assesses the recent development in transferring large‐area graphene grown on Fe, Ru, Co, Ir, Ni, Pt, Au, Cu, and some nonmetal substrates by using various synthesized methods. Among them, the transfers of the most attention kinds of graphene synthesized on Cu and SiC substrates are discussed emphatically. The advances and the main challenges of each wet and dry transfer method for obtaining the transferred graphene film with large‐area, clean, and low defect surface are also reviewed. Finally, the article concludes the most promising methods and the further prospects of graphene transfer.
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Cites
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Cited by
2D Material Based Synaptic Devices for Neuromorphic Computing
Cao, G., Meng, P., Chen, J., Liu, H., Bian, R., Zhu, C., Liu, F., & Liu, Z. (2021) · Advanced Functional Materials · doi:10.1002/adfm.202005443 · Cited by 382
Graphene-based wearable sensors
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Metadata
- Authors
- Chen, Y., Gong, X., & Gai, J.
- Venue
- Advanced Science
- Publisher
- Wiley
- Published
- 2016-02-04
- Volume
- 3
- Issue
- 8
- Pages
- 1500343-1500343
- ISSN
- 2198-3844 (ISSN-L)
- OpenAlex
- W2290637078
- Cited by
- 357
- References
- 68
- FWCI
- 16.44
- Type
- journal article
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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