arXiv subjects

Le Zhou

Publications and source records attributed to Le Zhou.

21 records · Page 2Linked to original sources

arXiv2023

Optical vortices enabled by structural vortices

The structural symmetry of solids plays an important role in defining their linear and nonlinear optical properties. The quest for versatile, cost-effective, large-scale, and defect-free approaches and materials platforms for tailoring structural and optical properties on demand has been underway for decades. We experimentally demonstrate a bottom-up self-assembly-based organic engineered material comprised of synthesized molecules with large dipole moments that are crystallized into a spherulite structure. The molecules align in an azimuthal direction, resulting in a vortex polarity with spontaneously broken symmetry leading to strong optical anisotropy and nonlinear optical responses. These unique polarization properties of the judiciously designed organic spherulite combined with the symmetry of structured optical beams enable a plethora of new linear and nonlinear light-matter interactions, including the generation of optical vortex beams with complex spin states and on-demand topological charges at the fundamental, doubled, and tripled frequencies. The results of this work are likely to enable numerous applications in areas such as high-dimensional quantum information processing, with large capacity and high security. The demonstrated spherulite crystals facilitate stand-alone micro-scale devices that rely on the unique micro-scale spontaneous vortex polarity that is likely to enable future applications for high-dimensional quantum information processing, spatiotemporal optical vortices, and a novel platform for optical manipulation and trapping.

physics.optics↗

arXiv2021

Highly sensitive strain sensor from topological-structure modulated dielectric elastic nanocomposites

Flexible strain sensors are critical to several potential intelligent applications, such as human-machine interfaces, soft robotics, human motion detection, and safety monitoring of components. Stretchable functional materials are important components of strain sensors, and they are still major challenges for high performance strain sensors. Herein, we demonstrate a novel strategy of designing and optimizing flexible strain sensor by developing topological structure modulated high permittivity elastic nanocomposite. The topological structure with three-phase percolative nano-nanonetworks produces synergistic effects of space charge enhancement and local electric field modulation, and it gives rise to an ultrahigh dielectric permittivity (113.4, at 1 kHz, over 1500% enhancement than that of commercial elastic polyurethane matrix) and excellent comprehensive electromechanical performance, and the optimal comprehensive electromechanical performance reaches to 542.91 MPa-1, which is over 9-fold than that of commercial polyurethane elastic film. An interdigital capacitive strain sensor is designed using the topological structured elastic dielectric nanocomposite. It possesses high initial capacitance density and positive capacitance response with stain, achieving high signal-to-noise ratio, high capacitance response sensitivity, and wide linear range, and it breaks through disadvantages of negative sensitivity and narrow linear range for conventional interdigital strain sensors. The prepared integrated strain sensor arrays are able to measure local strain of convoluted surfaces and monitor the motion of soft actuators in real time, and they would make conditions for intelligent control systems and the study of morphological intelligence.

physics.app-ph↗

arXiv2020

Enveloped Huber Regression

Huber regression (HR) is a popular robust alternative to the least squares regression when the error follows a heavy-tailed distribution. We propose a new method called the enveloped Huber regression (EHR) by considering the envelope assumption that there exists some subspace of the predictors that has no association with the response, which is referred to as the immaterial part. More efficient estimation is achieved via the removal of the immaterial part. Different from the envelope least squares (ENV) model whose estimation is based on maximum normal likelihood, the estimation of the EHR model is through Generalized Method of Moments. The asymptotic normality of the EHR estimator is established, and it is shown that EHR is more efficient than HR. Moreover, EHR is more efficient than ENV when the error distribution is heavy-tailed, while maintaining a small efficiency loss when the error distribution is normal. Moreover, our theory also covers the heteroscedastic case in which the error may depend on the covariates. Extensive simulation studies confirm the messages from the asymptotic theory. EHR is further illustrated on a real dataset.

stat.ME↗