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Manipulations with open quantum systems (such as qubits) are fundamental for any quantum technology. They are the focus of studies involving optimal control theory. Usually, control is achieved through the use of time-dependent external fields when driven system evolution is simulated employing the Davies construction (second-order Markov quantum master equation formulation). As a weak (second order) coupling scheme, the Davies construction is limited in its ability to account for bath-induced coherences. To overcome the limitation, we utilize the nonequilibrium Green’s function method and demonstrate that accounting for the coherences makes a qualitative impact on quantum control studies. We find that accounting for the coherences is especially important when dealing with system evolution involving mixed states. 

The development of experimental techniques at the nanoscale has enabled the performance of spectroscopic measurements on single-molecule current-carrying junctions. These experiments serve as a natural intersection for the research fields of optical spectroscopy and molecular electronics. We present a pedagogical comparison between the perturbation theory expansion of standard nonlinear optical spectroscopy and the (non-self-consistent) perturbative diagrammatic formulation of the nonequilibrium Green’s functions method (which is widely used in molecular electronics), highlighting their similarities and differences. By comparing the two approaches, we argue that the optical spectroscopy of open quantum systems must be analyzed within the more general Green’s function framework. 

We report here the synthesis, characterization, and crystal structures of three perfluoropropylated dibenzo[ a , c ]phenazine constitutional isomers, in which the only difference among them was the positions of the perfluoropropyl substituents. The crystal structures of these perfluropropylated dibenzo[ a , c ]phenazine isomers indicated that the stereo-electronic effect of the perfluoropropyl group on the dibenzo[ a , c ]phenazine molecule plays a crucial role in determining the crystal-packing motif in the solid state. Our results from both X-ray crystallography and computational approaches revealed that the positions of the perfluoropropyl groups on the dibenzo[ a , c ]phenazine ring significantly affected the electrostatic potential distribution along the aromatic ring surface, resulting in drastic changes in the molecular packing in the solid state, from herringbone to lamellar crystal packing, among these three constitutional isomers. Simple topological consideration of the molecular packing in the solid state was coincidently cooperative with the changes in the electrostatic potential distributions, where localized partial positive and partial negative charges perhaps dominated the intermolecular interactions between the aromatic rings. Together, the perfluoropropylation on the dibenzo[ a , c ]phenazine ring provided us with a fortunate scenario, wherein the molecular topological structure and electrostatic potential worked together to facilitate the formation of the desired lamellar π–π stacked crystal packing. Meanwhile, electrochemistry, UV-visible absorption and emission spectra, and the computational chemistry results pointed out that there were only minor to moderate changes in the electronic properties of the molecules upon changing the position of the perfluoroalkylation on the dibenzo[ a , c ]phenazine core. While controlling the solid-state structure of aromatics by design still has a long way to go, we hope that our work will ignite a spark that can potentially spread into the field of the design of organic solid-state materials. 

Abstract Single cell data integration methods aim to integrate cells across data batches and modalities, and data integration tasks can be categorized into horizontal, vertical, diagonal, and mosaic integration, where mosaic integration is the most general and challenging case with few methods developed. We propose scMoMaT, a method that is able to integrate single cell multi-omics data under the mosaic integration scenario using matrix tri-factorization. During integration, scMoMaT is also able to uncover the cluster specific bio-markers across modalities. These multi-modal bio-markers are used to interpret and annotate the clusters to cell types. Moreover, scMoMaT can integrate cell batches with unequal cell type compositions. Applying scMoMaT to multiple real and simulated datasets demonstrated these features of scMoMaT and showed that scMoMaT has superior performance compared to existing methods. Specifically, we show that integrated cell embedding combined with learned bio-markers lead to cell type annotations of higher quality or resolution compared to their original annotations.