69 research outputs found
Strain-tuning of vacancy-induced magnetism in graphene nanoribbons
Vacancies in graphene lead to the appearance of localized electronic states
with non-vanishing spin moments. Using a mean-field Hubbard model and an
effective double-quantum dot description we investigate the influence of strain
on localization and magnetic properties of the vacancy-induced states in
semiconducting armchair nanoribbons. We find that the exchange splitting of a
single vacancy and the singlet-triplet splitting for two vacancies can be
widely tuned by applying uniaxial strain, which is crucial for spintronic
applications
Nonlinear phononics using atomically thin membranes
Phononic crystals and acoustic meta-materials are used to tailor phonon and
sound propagation properties by facilitating artificial, periodic structures.
Analogous to photonic crystals, phononic band gaps can be created, which
influence wave propagation and, more generally, allow engineering of the
acoustic properties of a system. Beyond that, nonlinear phenomena in periodic
structures have been extensively studied in photonic crystals and atomic
Bose-Einstein Condensates in optical lattices. However, creating nonlinear
phononic crystals or nonlinear acoustic meta-materials remains challenging and
only few examples have been demonstrated. Here we show that atomically thin and
periodically pinned membranes support coupled localized modes with nonlinear
dynamics. The proposed system provides a platform for investigating nonlinear
phononics
Multi-scale approach for strain-engineering of phosphorene
A multi-scale approach for the theoretical description of deformed
phosphorene is presented. This approach combines a valence-force model to
relate macroscopic strain to microscopic displacements of atoms and a
tight-binding model with distance-dependent hopping parameters to obtain
electronic properties. The resulting self-consistent electromechanical model is
suitable for large-scale modeling of phosphorene devices. We demonstrate this
for the case of an inhomogeneously deformed phosphorene drum, which may be used
as an exciton funnel
FPU physics with nanomechanical graphene resonators: intrinsic relaxation and thermalization from flexural mode coupling
Thermalization in nonlinear systems is a central concept in statistical
mechanics and has been extensively studied theoretically since the seminal work
of Fermi, Pasta and Ulam (FPU). Using molecular dynamics and continuum modeling
of a ring-down setup, we show that thermalization due to nonlinear mode
coupling intrinsically limits the quality factor of nanomechanical graphene
drums and turns them into potential test beds for FPU physics. We find the
thermalization rate to be independent of radius and scaling as
, where and
are effective resonator temperature and prestrain
Geometric deep learning reveals the spatiotemporal fingerprint of microscopic motion
The characterization of dynamical processes in living systems provides
important clues for their mechanistic interpretation and link to biological
functions. Thanks to recent advances in microscopy techniques, it is now
possible to routinely record the motion of cells, organelles, and individual
molecules at multiple spatiotemporal scales in physiological conditions.
However, the automated analysis of dynamics occurring in crowded and complex
environments still lags behind the acquisition of microscopic image sequences.
Here, we present a framework based on geometric deep learning that achieves the
accurate estimation of dynamical properties in various biologically-relevant
scenarios. This deep-learning approach relies on a graph neural network
enhanced by attention-based components. By processing object features with
geometric priors, the network is capable of performing multiple tasks, from
linking coordinates into trajectories to inferring local and global dynamic
properties. We demonstrate the flexibility and reliability of this approach by
applying it to real and simulated data corresponding to a broad range of
biological experiments.Comment: 17 pages, 5 figure, 2 supplementary figure
Nonlinear damping in graphene resonators
Based on a continuum mechanical model for single-layer graphene, we propose and analyze a microscopic mechanism for dissipation in nanoelectromechanical graphene resonators. We find that coupling between flexural modes and in-plane phonons leads to linear and nonlinear damping of out-of-plane vibrations. By tuning external parameters such as bias and ac voltages, one can cross over from a linear-to a nonlinear-damping dominated regime. We discuss the behavior of the effective quality factor in this context. DOI: 10.1103/PhysRevB.86.23543
Fast and Accurate Nanoparticle Characterization Using Deep-Learning-Enhanced Off-Axis Holography
Characterization of suspended nanoparticles in their native environment plays a central role in a wide range of fields, from medical diagnostics and nanoparticleenhanced drug delivery to nanosafety and environmental nanopollution assessment. Standard optical approaches for nanoparticle sizing assess the size via the diffusion constant and, as a consequence, require long trajectories and that the medium has a known and uniform viscosity. However, in most biological applications, only short trajectories are available, while simultaneously, the medium viscosity is unknown and tends to display spatiotemporal variations. In this work, we demonstrate a label-free method to quantify not only size but also refractive index of individual subwavelength particles using 2 orders of magnitude shorter trajectories than required by standard methods and without prior knowledge about the physicochemical properties of the medium. We achieved this by developing a weighted average convolutional neural network to analyze holographic images of single particles, which was successfully applied to distinguish and quantify both size and refractive index of subwavelength silica andpolystyrene particles without prior knowledge of solute viscosity or refractive index. We further demonstrate how these features make it possible to temporally resolve aggregation dynamics of 31 nm polystyrene nanoparticles, revealing previously unobserved time-resolved dynamics of the monomer number and fractal dimension of individual subwavelength aggregates
Dual-angle interferometric scattering microscopy for optical multiparametric particle characterization
Traditional single-nanoparticle sizing using optical microscopy techniques
assesses size via the diffusion constant, which requires suspended particles in
a medium of known viscosity. However, these assumptions are typically not
fulfilled in complex natural sample environments. Here, we introduce dual-angle
interferometric scattering microscopy (DAISY), enabling optical quantification
of both size and polarizability of individual nanoparticles without requiring a
priori information regarding the surrounding media or super-resolution imaging.
DAISY achieves this by combining the information contained in concurrently
measured forward and backward scattering images through twilight off-axis
holography and interferometric scattering (iSCAT). Going beyond particle size
and polarizability, single-particle morphology can be deduced from the fact
that hydrodynamic radius relates to the outer particle radius while the
scattering-based size estimate depends on the internal mass distribution of the
particles. We demonstrate this by optically differentiating biomolecular
fractal aggregates from spherical particles in fetal bovine serum at the single
particle level
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