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DOE OSTI2021

Pressure Induced Assembly and Coalescence of Lead Chalcogenide Nanocrystals

We report here pressure induced nanocrystal coalescence of ordered lead chalcogenide nanocrystal arrays into one dimensional (1D) and 2D nanostructures. In particular, atomic crystal phase transitions and mesoscale coalescence of PbS and PbSe nanocrystals have been observed and monitored in-situ respectively by wide- and small-angle synchrotron X-ray scattering techniques. At the atomic scale, both nanocrystals underwent reversible structural transformations from cubic to orthorhombic at significantly higher pressures than those for the corresponding bulk materials. At the mesoscale, PbS nanocrystal arrays displayed a superlattice transformation from face-centered cubic to lamellar structures, while no clear mesoscale lattice transformation was observed for PbSe nanocrystal arrays. Intriguingly, transmission electron microscopy (TEM) showed that the applied pressure forced both spherical nanocrystals to coalesce into single crystalline 2D nanosheets and 1D nanorods. Our results confirm that pressure can be used as a straightforward approach to manipulate the interparticle spacing and engineer nanostructures with specific morphologies, and therefore provide insights into the design and functioning of new semiconductor nanocrystal structures under high-pressure conditions.

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DOE OSTI2025

Vertically Self-Oriented, Ultrafast 1D ZnO:Li Nanorods as Scintillators for Thermal Neutron Detection

Detection of special nuclear materials (SNMs) is of vital importance in the prevention of nuclear terrorism and to secure states’ national security. Neutron detection is a particularly useful tool to identify SNM, and neutron-sensitive scintillators have many promising properties, such as ease of use, good time resolution, and high detection efficiency. In this work, we develop highly stable, self-oriented, ultrafast 1D ZnO:Li (and codoped with Al, Ga, and In) nanorods (NRs) as thermal neutron-sensitive scintillators. Lithium-6 has high thermal neutron cross section for the (n, α) reaction in ZnO:Li scintillators which have a vertical nano array design greatly increasing the effective surface area and scintillation efficiency. Cost-effective low-temperature (95 °C) hydrothermal growth is used to obtain highly crystalline ZnO:Li nano scintillators by combining nuclear range data and electron transport mechanisms. Among the studies using low-temperature hydrothermal synthesis and a relatively low annealing temperature (≈350 °C) along with optimized NRs (length ≈ 5–8 μm, mean diameter ≈ 700 nm) for thermal neutron detection, this study reports the shortest scintillation decay time (≈ 470 ps) so far to the best of our knowledge. In conclusion, this nano array scintillator combines the advantages of a low-cost growth technique with environmentally friendly and widely available materials.

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DOE OSTI2024

Machine learning-driven descriptions of protein dynamics at solid-liquid interfaces

This chapter has described how ML has enabled quantitative analysis of HS-AFM data to discover the physical phenomena governing protein dynamics and ordering at solid-liquid interfaces. The research detailed in this chapter modeled the rotation models of protein nanorods, the discovery of which would otherwise not be possible. By tracking the trajectories of individual protein rods from frame to frame, it was possible to model Brownian type motion and behaviors and Levy-flight dynamics that had not previously been shown. We also described the application of the Python package AtomAI, which has been developed specifically to analyze and extract physical phenomena, providing exemplar code for training an ensemble of deep neural networks to produce the semantic segmentation of AFM data and functions for encoding and decoding local environments. We last described a combinatorial approach to analyze very noisy data with a densely covered substrate where the emergence of order for the protein liquid crystals could be elucidated. By combining the methods from Case 1 and 2, it was possible to obtain the center of mass and angle for each rod in the images and track the assembly of the rods over time into a 2D liquid crystal array on the surface of mica.

protein dynamics, solid-liquid interfaces, atomic ↗

DOE OSTI2024

Superhydrophobic Array Devices for the Enhanced Formation of 3D Cancer Models

During the metastatic cascade, cancer cells travel through the bloodstream as circulating tumor cells (CTCs) to a secondary site. Clustered CTCs have greater shear stress and treatment resistance, yet their biology remains poorly understood. We therefore engineered a tunable superhydrophobic array device (SHArD). The SHArD-C was applied to culture a clinically relevant model of CTC clusters. Using our device, we cultured a model of cancer cell aggregates of various sizes with immortalized cancer cell lines. These exhibited higher E-cadherin expression and are significantly more capable of surviving high fluid shear stress-related forces compared to single cells and model clusters grown using the control method, helping to explain why clustering may provide a metastatic advantage. Additionally, the SHArD-S, when compared with the AggreWell 800 method, provides a more consistent spheroid-forming device culturing reproducible sizes of spheroids for multiple cancer cell lines. Overall, we designed, fabricated, and validated an easily tunable engineered device which grows physiologically relevant three-dimensional (3D) cancer models containing tens to thousands of cells.

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