Interesting electronic properties arise in vertically stacked graphene sheets, some of which can be controlled by mutual orientation of the adjacent layers. In this study, we investigate the MBE grown multilayer graphene on Ir(111) by means of STM, LEED and XPS and we examine the influence of the substrate on the geometric and electronic properties of bilayer graphene by employing XSW and ARPES measurements. We find that the MBE method does not limit the growth to two graphene layers and that the wrinkles, which arise through extended carbon deposition, play a crucial role in the multilayer growth. We also find that the bilayer and trilayer graphene sheets have graphitic-like properties in terms of the separation between the two layers and their stacking. The presence of the iridium substrate imposes a periodic potential induced by the moiré pattern that was found to lead to the formation of replica bands and minigaps in bilayer graphene. From tight-binding fits to our ARPES data we find that band renormalization takes place in multilayer graphene due to a weaker coupling of the upper-most graphene layer to the iridium substrate.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1039/d0nr04788k | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Dye Molecule-Induced Optoelectronic Synaptic Behaviors of Monolayer MoSe.
ACS Appl Mater Interfaces
December 2024
School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China.
Although MoSe-based photodetectors have achieved excellent performance, the ultrafast photoresponse has limited their application as an optoelectronic synapse. In this paper, the enhancement of the rhodamine 6G molecule on the memory time of MoSe is reported. It is found that the memory time of monolayer MoSe can be obviously enhanced after assembly with rhodamine 6G exhibiting synaptic characteristics in comparison to pristine MoSe.
View Article and Find Full Text PDFMultiple Magnetic Phase Transitions and Giant Refrigerant Capacity in a GdDyHoErTm High-Entropy Alloy.
ACS Appl Mater Interfaces
December 2024
School of Materials Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
Magnetocaloric high-entropy alloys (HEAs) have recently garnered significant interest owing to their potential applications in magnetic refrigeration, offering a wide working temperature range and large refrigerant capacity. In this study, we thoroughly investigated the structural, magnetic, and magnetocaloric properties of equiatomic GdDyHoErTm HEAs. The as-cast alloy exhibits a single hexagonal phase, a randomly distributed grain orientation, and complex magnetism.
View Article and Find Full Text PDF<i>Ormocarpum trichocarpum</i> (Taub.) Engl. is a shrub or small tree harvested from the wild as a source of food, traditional medicines and wood.
View Article and Find Full Text PDFPlasma-aided direct printing of silver nanoparticle conductive structures on polydimethylsiloxane (PDMS) surfaces.
Sci Rep
December 2024
Department of Engineering, Norfolk State University, Norfolk, USA.
We report a controlled deposition process using atmospheric plasma to fabricate silver nanoparticle (AgNP) structures on polydimethylsiloxane (PDMS) substrates, essential for stretchable electronic circuits in wearable devices. This technique ensures precise printing of conductive structures using nanoparticles as precursors, while the relationship between crystallinity and plasma treatment is established through X-ray diffraction (XRD) analysis. The XRD studies provide insights into the effects of plasma parameters on the structural integrity and adhesion of AgNP patterns, enhancing our understanding of substrate stretchability and bendability.
View Article and Find Full Text PDFApproaching coupled-cluster accuracy for molecular electronic structures with multi-task learning.
Nat Comput Sci
December 2024
Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
Machine learning plays an important role in quantum chemistry, providing fast-to-evaluate predictive models for various properties of molecules; however, most existing machine learning models for molecular electronic properties use density functional theory (DFT) databases as ground truth in training, and their prediction accuracy cannot surpass that of DFT. In this work we developed a unified machine learning method for electronic structures of organic molecules using the gold-standard CCSD(T) calculations as training data. Tested on hydrocarbon molecules, our model outperforms DFT with several widely used hybrid and double-hybrid functionals in terms of both computational cost and prediction accuracy of various quantum chemical properties.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!