Spin-ordered electronic states in hydrogen-terminated zigzag nanographene give rise to magnetic quantum phenomena 1,2 that have sparked renewed interest in carbon-based spintronics 3,4 . Zigzag graphene nanoribbons (ZGNRs)quasi one-dimensional semiconducting strips of graphene featuring two parallel zigzag edges along the main axis of the ribbonare predicted to host intrinsic electronic edge states that are ferromagnetically ordered along the edges of the ribbon and antiferromagnetically coupled across its width 1,2,5 . Despite recent advances in the bottom-up synthesis of atomically-precise ZGNRs, their unique electronic structure has thus far been obscured from direct observations by the innate chemical reactivity of spin-ordered edge states [6][7][8][9][10][11] . Here we present a general technique for passivating the chemically highly reactive spin-polarized edge states by introducing a superlattice of substitutional nitrogen-dopants along the edges of a ZGNR. First-principles GW calculations and scanning tunneling spectroscopy reveal a giant spin splitting of the low-lying nitrogen lone-pair flat bands by a large exchange field (~850 Tesla) induced by the spin-polarized ferromagnetically ordered edges of ZGNRs. Our findings directly corroborate the nature of the predicted emergent magnetic order in ZGNRs and provide a robust platform for their exploration and functional integration into nanoscale sensing and logic devices [11][12][13][14][15][16][17] .Graphene nanostructures terminated by zigzag edges host spin-ordered electronic states that give rise to quantum magnetism 1,2 . These intrinsic magnetic edge states emerge from the zigzag edge structure of graphene itself, and create opportunities for the exploration of carbon-based spintronics and qubits [18][19][20] , paving the way for the realization of high-speed, low-power operation spin-logic devices for data storage and information processing [21][22][23][24] . The edge states of zigzag graphene nanoribbons (ZGNRs) have been predicted to exhibit a parallel (ferromagnetic) alignment of spins on either edge of the ribbon while the spins on opposing edges are antiferromagnetically coupled (antiparallel alignment) 1,2 . This unusual electronic structure can give rise to field-or strain-driven half-metallicity in ZGNRs 2,25 . A strong hybridization of the electronic states of ZGNRs with those of the underlying support, along with the susceptibility of zigzag edges to undergo passivation through atom-abstraction or radical-recombination reactions represents a veritable challenge to their exploration.
A series of trigonal planar N-, O-, and S-dopant atoms incorporated along the convex protrusion lining the edges of bottom-up synthesized chevron graphene nanoribbons (cGNRs) induce a characteristic shift in the energy of conduction and valence band edge states along with a significant reduction of the band gap of up to 0.3 eV per dopant atom per monomer. A combination of scanning probe spectroscopy and density functional theory calculations reveals that the direction and the magnitude of charge transfer between the dopant atoms and the cGNR backbone are dominated by inductive effects and follow the expected trend in electronegativity. The introduction of heteroatom dopants with trigonal planar geometry ensures an efficient overlap of a p-orbital lone-pair centered on the dopant atom with the extended π-system of the cGNR backbone effectively extending the conjugation length. Our work demonstrates a widely tunable method for band gap engineering of graphene nanostructures for advanced electronic applications.
The integration of substitutional dopants at predetermined positions along the hexagonal lattice of graphene-derived polycyclic aromatic hydrocarbons is a critical tool in the design of functional electronic materials. Here, we report the unusually mild thermally induced oxidative cyclodehydrogenation of dianthryl pyrazino[2,3-g]quinoxalines to form the four covalent C–N bonds in tetraazateranthene on Au(111) and Ag(111) surfaces. Bond-resolved scanning probe microscopy, differential conductance spectroscopy, along with first-principles calculations unambiguously confirm the structural assignment. Detailed mechanistic analysis based on ab initio density functional theory calculations reveals a stepwise mechanism featuring a rate determining barrier of only ΔE ⧧ = 0.6 eV, consistent with the experimentally observed reaction conditions.
The rational bottom-up synthesis of graphene nanoribbons (GNRs) provides atomically precise control of widths and edges that give rise to a wide range of electronic properties promising for electronic devices such as field-effect transistors (FETs). Since the bottom-up synthesis commonly takes place on catalytic metallic surfaces, the integration of GNRs into such devices 2 requires their transfer onto insulating substrates, which remains one of the bottlenecks in the development of GNR-based electronics. Herein, we report on a method for the transfer-free placement of GNRs on insulators. This involves growing GNRs on a gold film deposited onto an insulating layer followed by gentle wet etching of the gold, which leaves the nanoribbons to settle in place on the underlying insulating substrate. Scanning tunneling microscopy (STM) and Raman spectroscopy confirm that atomically precise GNRs of high density uniformly grow on the gold films deposited onto SiO 2 /Si substrates and remain structurally intact after the etching process. We have also demonstrated transfer-free fabrication of ultra-short channel GNR FETs using this process. Our work here represents an important step towards large-scale integration of GNRs into electronic devices.
Scanning tunneling spectroscopy (STS), a technique that records the change in the tunneling current as a function of the bias (dI/dV) across the gap between a tip and the sample, is a powerful tool to characterize the electronic structure of single molecules and nanomaterials. While performing STS, the structure and condition of the scanning probe microscopy (SPM) tips are critical for reliably obtaining high quality point spectra. Here, we present an automated program based on machine learning models that can identify the Au(111) Shockley surface state in dI/dV point spectra and perform tip conditioning on clean or sparsely covered gold surfaces with minimal user intervention. We employed a straightforward height-based segmentation algorithm to analyze STM topographic images to identify tip conditioning positions and used 1789 archived dI/dV spectra to train machine learning models that can ascertain the condition of the tip by evaluating the quality of the spectroscopic data. Decision tree based ensemble and boosting models and deep neural networks (DNNs) have been shown to reliably identify tips in suitable conditions for STS. We expect the automated program to reduce operational costs and time, increase reproducibility in surface science studies, and accelerate the discovery and characterization of novel nanomaterials by STM. The strategies presented in this paper can readily be adapted to STM tip conditioning on a wide variety of other common substrates.
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