On-Surface Synthesis and Characterization of Acene-Based Nanoribbons Incorporating Four-Membered Rings.
Carlos Sánchez-SánchezThomas DienelAdrien NicolaïNeerav KharcheLiangbo LiangColin DanielsVincent MeunierJunzhi LiuXinliang FengKlaus MüllenJuan Ramón Sánchez-ValenciaOliver GröningPascal RuffieuxRoman FaselPublished in: Chemistry (Weinheim an der Bergstrasse, Germany) (2019)
A bottom up method for the synthesis of unique tetracene-based nanoribbons, which incorporate cyclobutadiene moieties as linkers between the acene segments, is reported. These structures were achieved through the formal [2+2] cycloaddition reaction of ortho-functionalized tetracene precursor monomers. The formation mechanism and the electronic and magnetic properties of these nanoribbons were comprehensively studied by means of a multitechnique approach. Ultra-high vacuum scanning tunneling microscopy showed the occurrence of metal-coordinated nanostructures at room temperature and their evolution into nanoribbons through formal [2+2] cycloaddition at 475 K. Frequency-shift non-contact atomic force microscopy images clearly proved the presence of bridging cyclobutadiene moieties upon covalent coupling of activated tetracene molecules. Insight into the electronic and vibrational properties of the so-formed ribbons was obtained by scanning tunneling microscopy, Raman spectroscopy, and theoretical calculations. Magnetic properties were addressed from a computational point of view, allowing us to propose promising candidates to magnetic acene-based ribbons incorporating four-membered rings. The reported findings will increase the understanding and availability of new graphene-based nanoribbons with high potential in future spintronics.
Keyphrases
- room temperature
- high resolution
- raman spectroscopy
- atomic force microscopy
- high speed
- molecularly imprinted
- single molecule
- optical coherence tomography
- ionic liquid
- molecular dynamics simulations
- density functional theory
- high throughput
- electron microscopy
- risk assessment
- deep learning
- mass spectrometry
- machine learning
- quantum dots
- tandem mass spectrometry