Conductance quantization is the quintessential feature of electronic transport in non-interacting mesoscopic systems. This phenomenon is observed in quasi one-dimensional conductors at zero magnetic field B, and the formation of edge states at finite magnetic fields results in wider conductance plateaus within the quantum Hall regime. Electrostatic interactions can change this picture qualitatively. At finite B, screening mechanisms in narrow, gated ballistic conductors are predicted to give rise to an increase in conductance and a suppression of quantization due to the appearance of additional conduction channels. Despite being a universal effect, this regime has proven experimentally elusive because of difficulties in realizing one-dimensional systems with sufficiently hard-walled, disorder-free confinement. Here, we experimentally demonstrate the suppression of conductance quantization within the quantum Hall regime for graphene nanoconstrictions with low edge roughness. Our findings may have profound impact on fundamental studies of quantum transport in finite-size, two-dimensional crystals with low disorder.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5811439 | PMC |
http://dx.doi.org/10.1038/s41467-018-03064-8 | DOI Listing |
Biomed Eng Lett
January 2025
School of Chemistry and Chemical Engineering, Tianjin University of Technology, Tianjin, 300384 People's Republic of China.
Brain-computer interface (BCI) has been widely used in human-computer interaction. The introduction of artificial intelligence has further improved the performance of BCI system. In recent years, the development of BCI has gradually shifted from personal computers to embedded devices, which boasts lower power consumption and smaller size, but at the cost of limited device resources and computing speed, thus can hardly improve the support of complex algorithms.
View Article and Find Full Text PDFEnviron Monit Assess
January 2025
Chinese-Israeli International Center for Research and Training in Agriculture, China Agricultural University, Beijing, People's Republic of China.
Specific yield (S) is an essential hydrogeological parameter in groundwater-related modeling and estimation. In this study, we proposed several new analytical expressions of S to characterize the nonlinear variations of S under shallow groundwater environments, encompassing S for three-layered soil, transition zone S, and flux-dependent S (in Boussinesq-type equation). The proposed S expression for three-layered soils expanded the applicability of previous expressions for homogeneous soil.
View Article and Find Full Text PDFNano Lett
January 2025
State Key Laboratory of Low Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing 100084, China.
Material challenges are the key issue in Majorana research, where surface disorder constrains device performance. Here, we tackle this challenge by embedding PbTe nanowires within a lattice-constant-matched crystal. The wire edges are shaped by self-organized growth instead of lithography, resulting in nearly atomically flat facets along both cross-sectional and longitudinal directions.
View Article and Find Full Text PDFEntropy (Basel)
November 2024
EPITA Research Laboratory, 14-16 Rue Voltaire, 94270 Le Kremlin-Bicêtre, France.
The purpose of this article is to provide a novel approach and justification of the idea that classical physics and quantum physics can neither function nor even be conceived without the other-in line with ideas attributed to, e.g., Niels Bohr or Lev Landau.
View Article and Find Full Text PDFAnimals (Basel)
December 2024
Graduate Program in Production Engineering, Universidade Paulista, Rua Dr. Bacelar 1212, São Paulo 04026-002, SP, Brazil.
A critical issue in image analysis for analyzing animal behavior is accurate object detection and tracking in dynamic and complex environments. This study introduces a novel preprocessing algorithm to bridge the gap between computational efficiency and segmentation fidelity in object-based image analysis for machine learning applications. The algorithm integrates convolutional operations, quantization strategies, and polynomial transformations to optimize image segmentation in complex visual environments, addressing the limitations of traditional pixel-level and unsupervised methods.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!