Unsupervised learning approaches to characterizing heterogeneous samples using X-ray single-particle imaging

One of the outstanding analytical problems in X-ray single-particle imaging (SPI) is the classification of structural heterogeneity, which is especially difficult given the low signal-to-noise ratios of individual patterns and the fact that even identical objects can yield patterns that vary greatly when orientation is taken into consideration. Proposed here are two methods which explicitly account for this orientation-induced variation and can robustly determine the structural landscape of a sample ensemble. The first, termed common-line principal component analysis (PCA), provides a rough classification which is essentially parameter free and can be run automatically on any SPI dataset. The second method, utilizing variation auto-encoders (VAEs), can generate 3D structures of the objects at any point in the structural landscape. Both these methods are implemented in combination with the noise-tolerant expand–maximize–compress (EMC) algorithm and its utility is demonstrated by applying it to an experimental dataset from gold nanoparticles with only a few thousand photons per pattern. Both discrete structural classes and continuous deformations are recovered. These developments diverge from previous approaches of extracting reproducible subsets of patterns from a dataset and open up the possibility of moving beyond the study of homogeneous sample sets to addressing open questions on topics such as nanocrystal growth and dynamics, as well as phase transitions which have not been externally triggered.

Imaging, understanding, and control of nanoscale materials transformations

The development of liquid cells for transmission electron microscopy has enabled breakthroughs in our ability to follow nanoscale structural, morphological, or chemical changes during materials growth and applications. Time-resolved high-resolution imaging and chemical analysis through liquids opened the opportunity to capture nanoscale dynamic processes of materials, including reaction intermediates and the transformation pathways. In this article, a series of work is highlighted with topics ranging from liquid cell developments to in situ studies of nanocrystal growth and transformations, dendrite formation, and suppression of lithium dendrites through in situ characterization of the solid–electrolyte interphase chemistry. The understanding garnered is expected to accelerate the discovery of novel materials for applications in energy storage, catalysis, sensors, and other functional devices.

Diffusion dynamics controlled colloidal synthesis of highly monodisperse InAs nanocrystals

Highly monodisperse colloidal InAs quantum dots (QDs) with superior optoelectronic properties are promising candidates for various applications, including infrared photodetectors and photovoltaics. Recently, a synthetic process involving continuous injection has been introduced to synthesize uniformly sized InAs QDs. Still, synthetic efforts to increase the particle size of over 5 nm often suffer from growth suppression. Secondary nucleation or interparticle ripening during the growth accompanies the inhomogeneity in size as well. In this study, we propose a growth model for the continuous synthetic processing of colloidal InAs QDs based on molecular diffusion. The experimentally validated model demonstrates how precursor solution injection reduces monomer flux, limiting particle growth during synthesis. As predicted by our model, we control the diffusion dynamics by tuning reaction volume, precursor concentration, and injection rate of precursor. Through diffusion-dynamics-control in the continuous process, we synthesize the InAs QDs with a size over 9.0-nm (1Smax of 1600 nm) with a narrow size distribution (12.2%). Diffusion-dynamics-controlled synthesis presented in this study effectively manages the monomer flux and thus overcome monomer-reactivity-originating size limit of nanocrystal growth in solution.

Dynamics of the nanocrystal structure and composition in growth solutions monitored by in situ lab-scale X-ray diffraction

Nanocrystal growth dynamics are investigated by a novel approach: real-time observation of nanocrystals in growth solutions using lab-scale in situ X-ray diffraction. The method reveals the evolution of crystal phase, size, shape, and composition.