arXiv subjects

P. Xiao

Publications and source records attributed to P. Xiao.

3 recordsLinked to original sources

arXiv2020

Optomechanical crystals for spatial sensing of submicron sized analytes

Optomechanical crystal cavities have rich perspectives for detecting and indirectly analysing biological particles, such as proteins, bacteria and viruses. In this work we demonstrate the working principle of an optomechanical crystal cavity operating under ambient conditions as a sensor of submicrometer analytes by optically monitoring the frequency shift of thermally activated mechanical modes. The resonator has been specifically designed so that the cavity region supports a particular family of low modal-volume mechanical modes, commonly known as -pinch modes-. These involve the oscillation of only a couple of adjacent cavity cells that are relatively insensitive to perturbations in other parts of the resonator. The eigenfrequency of these modes decreases as the deformation is localized closer to the centre of the resonator. Thus, by identifying specific modes that undergo a frequency shift that amply exceeds the mechanical linewidth, it is possible to infer if there are particles deposited on the resonator, how many are there and their approximate position within the cavity region.

physics.optics↗

arXiv2020

A Robust Deep Unfolded Network for Sparse Signal Recovery from Noisy Binary Measurements

We propose a novel deep neural network, coined DeepFPC-$\ell_2$, for solving the 1-bit compressed sensing problem. The network is designed by unfolding the iterations of the fixed-point continuation (FPC) algorithm with one-sided $\ell_2$-norm (FPC-$\ell_2$). The DeepFPC-$\ell_2$ method shows higher signal reconstruction accuracy and convergence speed than the traditional FPC-$\ell_2$ algorithm. Furthermore, we compare its robustness to noise with the previously proposed DeepFPC network---which stemmed from unfolding the FPC-$\ell_1$ algorithm---for different signal to noise ratio (SNR) and sign-flipped ratio (flip ratio) scenarios. We show that the proposed network has better noise immunity than the previous DeepFPC method. This result indicates that the robustness of a deep-unfolded neural network is related with that of the algorithm it stems from.

cs.LG↗

arXiv2019

Fracturing of polycrystalline MoS$_2$ nanofilms

The possibility of tailoring the critical strain of 2D materials will be crucial for the fabrication of flexible devices. In this paper, the fracture in polycrystalline MoS2 films with two different grain orientations is studied at the micro- and nanoscale using electron microscopy. The critical uniaxial strain is determined to be approximately 5% and independent of the sample morphology. However, electron beam irradiation is found to enhance the interaction between the MoS2 and the PDMS substrates, leading to an increased critical strain that can exceed 10%. This enhancement of strain resistance was used to fabricate a mechanically robust array of lines 1 mm in length. Finally, nanoscale crack propagation studied by transmission electron microscopy showed that cracks propagate along the grain boundaries as well as through the grains, preferentially along van der Waals bonding. These results provide insight regarding the fracture of polycrystalline 2D materials and a new method to tailor the critical strain and nanofabrication of ultra-thin MoS2 devices using well-developed tools, which will be of great interest to the flexible electronics industry.

physics.app-ph↗