STRUCTURAL ELUCIDATION OF DEPROTONATED ANALYTES VIA TANDEM MASS SPECTROMETRY BASED ON ION-MOLECULE REACTIONS AND COLLISION-ACTIVATED DISSOCIATION

Mass Spectrometry has emerged as a powerful analytical tool for the characterization of unknown molecules. Molecular weight information and chemical formulas of the unknowns can be derived by measuring the m/z value of the ionized analyte. In addition, structural information can be obtained via tandem mass spectrometry methods such as collision-activated dissociation (CAD). However, CAD does not always guarantee unambiguously assignment of chemical structures, therefore, additional tandem mass spectrometric methods such as ion-molecule reactions were developed

Room-Temperature Structures of Solid Hydrogen at High Pressures

By employing first-principles metadynamics simulations, we explore the 300 K structures of solid hydrogen over the pressure range 150-300 GPa. At 200 GPa, we find the ambient-pressure disordered hexagonal close-packed (hcp) phase transited into an insulating partially ordered hcp phase (po-hcp), a mixture of ordered graphene-like H2 layers and the other layers of weakly coupled, disordered H2 molecules. Within this phase, hydrogen remains in paired states with creation of shorter intra-molecular bonds, which are responsible for the very high experimental Raman peak above 4000 cm-1. At 275 GPa, our simulations predicted a transformation from po-hcp into the ordered molecular metallic Cmca phase (4 molecules/cell) that was previously proposed to be stable only above 400 GPa. Gibbs free energy calculations at 300 K confirmed the energetic stabilities of the po-hcp and metallic Cmca phases over all known structures at 220-242 GPa and >242 GPa, respectively. Our simulations highlighted the major role played by temperature in tuning the phase stabilities and provided theoretical support for claimed metallization of solid hydrogen below 300 GPa at 300 K.Comment: Accepted in Journal of Chemical Physic

Atomically phase-matched second-harmonic generation in a 2D crystal.

Second-harmonic generation (SHG) has found extensive applications from hand-held laser pointers to spectroscopic and microscopic techniques. Recently, some cleavable van der Waals (vdW) crystals have shown SHG arising from a single atomic layer, where the SH light elucidated important information such as the grain boundaries and electronic structure in these ultra-thin materials. However, despite the inversion asymmetry of the single layer, the typical crystal stacking restores inversion symmetry for even numbers of layers leading to an oscillatory SH response, drastically reducing the applicability of vdW crystals such as molybdenum disulfide (MoS2). Here, we probe the SHG generated from the noncentrosymmetric 3R crystal phase of MoS2. We experimentally observed quadratic dependence of second-harmonic intensity on layer number as a result of atomically phase-matched nonlinear dipoles in layers of the 3R crystal that constructively interfere. By studying the layer evolution of the A and B excitonic transitions in 3R-MoS2 using SHG spectroscopy, we also found distinct electronic structure differences arising from the crystal structure and the dramatic effect of symmetry and layer stacking on the nonlinear properties of these atomic crystals. The constructive nature of the SHG in this 2D crystal provides a platform to reliably develop atomically flat and controllably thin nonlinear media

DP-Image: Differential Privacy for Image Data in Feature Space

The excessive use of images in social networks, government databases, and industrial applications has posed great privacy risks and raised serious concerns from the public. Even though differential privacy (DP) is a widely accepted criterion that can provide a provable privacy guarantee, the application of DP on unstructured data such as images is not trivial due to the lack of a clear qualification on the meaningful difference between any two images. In this paper, for the first time, we introduce a novel notion of image-aware differential privacy, referred to as DP-image, that can protect user's personal information in images, from both human and AI adversaries. The DP-Image definition is formulated as an extended version of traditional differential privacy, considering the distance measurements between feature space vectors of images. Then we propose a mechanism to achieve DP-Image by adding noise to an image feature vector. Finally, we conduct experiments with a case study on face image privacy. Our results show that the proposed DP-Image method provides excellent DP protection on images, with a controllable distortion to faces