講演情報

[18a-A37-5]Controlled n-type doping in graphene using a photobase generator andpolyethylene oxide blends

〇(D)YUQING WANG1,2, Masatou Ishihara1, Kazuhiro Kirihara1, Shohei Horike3, Qingshuo Wei1,2 (1.AIST, 2.Univ. Tsukuba, 3.Kobe Univ.)

キーワード:

Graphene、Doping、Nanocarbon

Achieving stable n-type doping in graphene is a critical challenge in materials science, which is essential for various device applications. Our recent research has successfully maintained stable n-type doping in graphene for over two months using a photobase generator, specifically 2-(9-oxoxanthen-2-yl)propionic acid 1,5,7-triazabicyclo[4.4.0]dec-5-ene (PBG), under UV light. The UV irradiation induces a photodecarboxylation reaction at the interface between PBG and graphene, yielding a strong base—1,5,7-triazabicyclo[4.4.0]dec-5-ene (TBD). This base donates electrons to graphene, prompting a transition from p-type to n-type. However, the small size of PBG molecules complicates achieving uniform dispersion on the graphene surface through spin-coating, leading to PBG aggregation and a thicker layer postdrying. Though this does not hinder n-type doping, it can cause PBG crystallization depending on ambient temperature and humidity, affecting the film's uniformity and transparency.
Integrating PBG into polyethylene oxide (PEO) films minimizes PBG crystallization, simplifying controlled spin coating. Hybridizing PBG with PEO effectively reduces PBG crystallization on the graphene surface, thus mitigating its detrimental effects on reactivity and transmittance. Crucially, varying the PBG concentration within the PEO matrix allows for precise control over the time required to transition graphene from p-type to n-type doping. Our innovative hybrid approach provides enhanced control over doping transitions, resulting in optimized graphene-based devices with sustainable functionalities. In particular, the method demonstrated long-term stability of more than 160 days, making it viable for practical applications in thermoelectric devices and other areas. This represents a significant advancement over previous methods in terms of cost, scalability, and stability.

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