Pattern-Potential-Guided Growth of Textured Macromolecular Films on Graphene/High-Index Copper
Embargo End Date2022-06-06
Permanent link to this recordhttp://hdl.handle.net/10754/669449
MetadataShow full item record
AbstractMacromolecular films are crucial functional materials widely used in the fields of mechanics, electronics, optoelectronics, and biology, due to their superior properties of chemical stability, small density, high flexibility, and solution-processing ability. Their electronic and mechanical properties, however, are typically much lower than those of crystalline materials, as the macromolecular films have no long-range structural ordering. The state-of-the-art for producing highly ordered macromolecular films is still facing a great challenge due to the complex interactions between adjacent macromolecules. Here, the growth of textured macromolecular films on a designed graphene/high-index copper (Cu) surface is demonstrated. This successful growth is driven by a patterned potential that originates from the different amounts of charge transfer between the graphene and Cu surfaces with, alternately, terraces and step edges. The textured films exhibit a remarkable improvement in remnant ferroelectric polarization and fracture strength. It is also demonstrated that this growth mechanism is universal for different macromolecules. As meter-scale graphene/high-index Cu substrates have recently become available, the results open a new regime for the production and applications of highly ordered macromolecular films with obvious merits of high production and low cost.
CitationZhou, D., Zhang, Z., Zhu, Y., Xiao, Y., Ding, Q., Ruan, L., … Han, G. (2021). Pattern-Potential-Guided Growth of Textured Macromolecular Films on Graphene/High-Index Copper. Advanced Materials, 2006836. doi:10.1002/adma.202006836
SponsorsThe authors acknowledge the financial support from the National Natural Science Foundation of China (Grant Nos. U1909212, U1809217, 21771161, and 51991342), the Natural Science Foundation of Zhejiang Province (LR21E020004), the Key R&D Program of Zhejiang Province (2020C01124), the Zhejiang Provincial Natural Science Foundation of China (Grant No. LR18B030003), the financial support from Research Grant Council of Hong Kong (General Research Fund No. 24306318), the Key R&D Program of Guangdong Province (2020B010189001 and 2019B010931001), Bureau of Industry and Information Technology of Shenzhen (Graphene platform 201901161512), and the Thousand Talents Program for Distinguished Young Scholars.