Accelerated iterative synthesis of ultralong graphene nanoribbons with full atomic precision

abstract

Graphene nanoribbon (GNR) properties are dominated by structural variables, such as edge structure, length, width, and heteroatom-doping, all requiring atomic precision to harness the full application potential of GNRs. Although the length influences key GNR properties, synthetic methods that allow control over the length remain underdeveloped, and the effects of length on GNR properties remain underexplored. Herein, we report an accelerated iterative approach enabling the synthesis of a series of length-controlled, ultralong atomically precise GNRs. The longest GNR displays a 920-atoms core with a 35.8-nm long (147 linearly fused rings) backbone that has been obtained in just three synthetic steps from building blocks of similar to 2 nm in length. The unprecedented solubility of this set of GNRs enables their purification by column chromatography and their investigation by a broad range of structural, optoelectronic, and redox characterization techniques. In addition, this GNR length control allows us to unambiguously establishing correlations between GNR length and properties, particularly electrical conductivity.

keywords

ZIGZAG; EDGE

subject category

Chemistry

authors

Dubey, RK; Marongiu, M; Fu, S; Wen, GZ; Bonn, M; Wang, HI; Melle-Franco, M; Mateo-Alonso, A

Groups

G6 - Virtual Materials and Artificial Intelligence

Projects

Projeto de Investigação Exploratória: Manuel Melle (IF Manuel Melle)

CICECO - Aveiro Institute of Materials (UIDB/50011/2020)

CICECO - Aveiro Institute of Materials (UIDP/50011/2020)

Associated Laboratory CICECO-Aveiro Institute of Materials (LA/P/0006/2020)

acknowledgements

The authors would like to thank Sara Gil Guerrero and Karol Strutynski for the performance of preliminary exploratory calculations. This work was carried out with support from the Basque Science Foundation for Science (Ikerbasque) , POLYMAT, the University of the Basque Country, Diputacion de Guipuzcoa, Gobierno Vasco (PIBA_2022_1_0031 and BERC programme) , and Gobierno de Espana (projects PID2021-124484OB-I00 and CEX2020-001067-M financed by MCIN/AEI/10.13039/501100011033) . Project number PCI2022-132921 was funded by the Agencia Estatal de Investigacion through the PCI 2022 and M-ERA.NET 2021 calls. Technical and human support was provided by SGIker of UPV/EHU, and European funding (ERDF and ESF) is acknowledged. This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement no. 722951) . In addition, support through the project IF/00894/2015, through the advanced computing project 2021.09622.CPCA granting access to the Navigator cluster at LCA-UC, and within the scope of the project CICECO-Aveiro Institute of Materials, UIDB/50011/2020, UIDP/50011/2020, and LA/P/0006/2020, financed by national funds through the FCT/MEC (PIDDAC) , is gratefully acknowledged. H.I.W. acknowledges the funding support from DFG (Pro- jektnummer 514772236) . S.F. and G.W. are supported by CSC fellowships.