The isotropic to nematic liquid crystal (LC) phase transition is used to create organized assemblies of CdSe/ ZnS core/shell quantum dots (QDs). Under controlled conditions, well dispersed QDs are expelled from the ordered domains of nematic LC into the remaining isotropic domains. The final LC phase produces three dimensional QD assemblies that are situated at the defect points in the LC volume. Through the luminescence of the QDs we are able to track the movement of the nanoparticles as the phase is formed as well as spectrally probe the resulting QD assemblies. Forster resonance energy transfer (FRET) measurements, combined with small angle X-ray scattering (SAXS) data reveal that the QD assemblies have a consistent inter-particle spacing of approximately 7.6 nm. Additionally, the location of the assemblies is shown to be controllable by utilizing beads as defect nucleation points.

Xi-cam is an extensible platform for data management, analysis and visualization. Xi-cam aims to provide a flexible and extensible approach to synchrotron data treatment as a solution to rising demands for high-volume/high-throughput processing pipelines. The core of Xi-cam is an extensible plugin-based graphical user interface platform which provides users with an interactive interface to processing algorithms. Plugins are available for SAXS/WAXS/GISAXS/GIWAXS, tomography and NEXAFS data. With Xi-cam's `advanced' mode, data processing steps are designed as a graph-based workflow, which can be executed live, locally or remotely. Remote execution utilizes high-performance computing or de-localized resources, allowing for the effective reduction of high-throughput data. Xi-cam's plugin-based architecture targets cross-facility and cross-technique collaborative development, in support of multi-modal analysis. Xi-cam is open-source and cross-platform, and available for download on GitHub.

A current goal in nanotechnology focuses on the assembly of different nanoparticle types into 3D organized structures. In this paper we report the use of a liquid crystal host phase in a new process for the generation of micron-scale vesicle-like nanoparticle shells stabilized by ligand-ligand interactions. The constructs formed consist of a robust, thin spherical layer, composed of closely packed quantum dots (QDs) and stabilized by local crystallization of the mesogenic ligands. Ligand structure can be tuned to vary QD packing within the shell and made UV cross-linkable to allow for intact shell extraction into toluene. The assembly method we describe could be extended to other nanoparticle types (metallic, magnetic etc.), where hollow shell formation is controlled by thermally sorting mesogen-functionalized nanoparticles in a liquid crystalline host material at the isotropic to nematic transition. This process represents a versatile method for making non-planar 3D nano-assemblies.

Mesogenic ligands have the potential to provide control over the dispersion and stabilization of nanoparticles in liquid crystal (LC) phases. The creation of such hybrid materials is an important goal for the creation of soft tunable photonic devices, such as the LC laser. Herein, we present a comparison of isotropic and mesogenic ligands attached to the surface of CdSe (core‐only) and CdSe/ZnS (core/shell) quantum dots (QDs). The mesogenic ligand′s flexible arm structure enhances ligand alignment, with the local LC director promoting QD dispersion in the isotropic and nematic phases. To characterize QD dispersion on different length scales, we apply fluorescence microscopy, X‐ray scattering, and scanning confocal photoluminescent imaging. These combined techniques demonstrate that the LC‐modified QDs do not aggregate into the dense clusters observed for dots with simple isotropic ligands when dispersed in liquid crystal, but loosely associate in a fluid‐like droplet with an average interparticle spacing >10 nm. Embedding the QDs in a cholesteric cavity, we observe comparable coupling effects to those reported for more closely packed isotropic ligands.

Abstract

Investment in brighter sources and larger and faster detectors has accelerated the speed of data acquisition at national user facilities. The accelerated data acquisition offers many opportunities for discovery of new materials, but it also presents a daunting challenge. The rate of data acquisition far exceeds the current speed of data quality assessment, resulting in less than optimal data and data coverage, which in extreme cases forces recollection of data. Herein, we show how this challenge can be addressed through development of an approach that makes routine data assessment automatic and instantaneous. Through extracting and visualizing customized attributes in real time, data quality and coverage, as well as other scientifically relevant information contained in large datasets is highlighted.Deployment of such an approach not only improves the quality of data but also helps optimize usage of expensive characterization resources by prioritizing measurements of highest scientific impact. We anticipate our approach to become a starting point for a sophisticated decision-tree that optimizes data quality and maximizes scientific content in real time through automation. With these efforts to integrate more automation in data collection and analysis, we can truly take advantage of the accelerating speed of data acquisition.

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