Tin–Carbon Dual Buffer Layer to Suppress Lithium Dendrite Growth in All-Solid-State Batteries
Not Available
25 ENERGY STORAGE↗
Engineering topics
Scott, Mary C.
Publications and source records attributed to Scott, Mary C..
Size-Resolved Shape Evolution in Inorganic Nanocrystals Captured via High-Throughput Deep Learning-Driven Statistical Characterization
Precise size and shape control in nanocrystal synthesis is essential for utilizing nanocrystals in various industrial applications, such as catalysis, sensing, and energy conversion. However, traditional ensemble measurements often overlook the subtle size and shape distributions of individual nanocrystals, hindering the establishment of robust structure–property relationships. In this study, we uncover intricate shape evolutions and growth mechanisms in Co 3 O 4 nanocrystal synthesis at a subnanometer scale, enabled by deep-learning-assisted statistical characterization. By first controlling synthetic parameters such as cobalt precursor concentration and water amount then using high resolution electron microscopy imaging to identify the geometric features of individual nanocrystals, this study provides insights into the interplay between synthesis conditions and the sizedependent shape evolution in colloidal nanocrystals. Utilizing population-wide imaging data encompassing over 441,067 nanocrystals, we analyze their characteristics and elucidate previously unobserved size-resolved shape evolution. This high-throughput statistical analysis is essential for representing the entire population accurately and enables the study of the size dependency of growth regimes in shaping nanocrystals. Our findings provide experimental quantification of the growth regime transition based on the size of the crystals, specifically (i) for faceting and (ii) from thermodynamic to kinetic, as evidenced by transitions from convex to concave polyhedral crystals. Additionally, we introduce the concept of an “onset radius,” which describes the critical size thresholds at which these transitions occur. This discovery has implications beyond achieving nanocrystals with desired morphology; it enables finely tuned correlation between geometry and material properties, advancing the field of colloidal nanocrystal synthesis and its applications.
77 NANOSCIENCE AND NANOTECHNOLOGY↗
The 4D Camera: An 87 kHz Direct Electron Detector for Scanning/Transmission Electron Microscopy
We describe the development, operation, and application of the 4D Camera—a 576 by 576 pixel active pixel sensor for scanning/transmission electron microscopy which operates at 87,000 Hz. The detector generates data at ~480 Gbit/s which is captured by dedicated receiver computers with a parallelized software infrastructure that has been implemented to process the resulting 10–700 Gigabyte-sized raw datasets. The back illuminated detector provides the ability to detect single electron events at accelerating voltages from 30 to 300 kV. Through electron counting, the resulting sparse data sets are reduced in size by 10--300× compared to the raw data, and open-source sparsity-based processing algorithms offer rapid data analysis. The high frame rate allows for large and complex scanning diffraction experiments to be accomplished with typical scanning transmission electron microscopy scanning parameters.
47 OTHER INSTRUMENTATION↗
A robust synthetic data generation framework for machine learning in high-resolution transmission electron microscopy (HRTEM)
Machine learning techniques are attractive options for developing highly-accurate analysis tools for nanomaterials characterization, including high-resolution transmission electron microscopy (HRTEM). However, successfully implementing such machine learning tools can be difficult due to the challenges in procuring sufficiently large, high-quality training datasets from experiments. In this work, we introduce Construction Zone, a Python package for rapid generation of complex nanoscale atomic structures which enables fast, systematic sampling of realistic nanomaterial structures and can be used as a random structure generator for large, diverse synthetic datasets. Using Construction Zone, we develop an end-to-end machine learning workflow for training neural network models to analyze experimental atomic resolution HRTEM images on the task of nanoparticle image segmentation purely with simulated databases. Further, we study the data curation process to understand how various aspects of the curated simulated data—including simulation fidelity, the distribution of atomic structures, and the distribution of imaging conditions—affect model performance across three benchmark experimental HRTEM image datasets. Using our workflow, we are able to achieve state-of-the-art segmentation performance on these experimental benchmarks and, further, we discuss robust strategies for consistently achieving high performance with machine learning in experimental settings using purely synthetic data. Construction Zone and its documentation are available at https://github.com/lerandc/construction_zone.
36 MATERIALS SCIENCE↗
Uncovering the three-dimensional structure of upconverting core–shell nanoparticles with multislice electron ptychography
In photon upconverting core–shell nanoparticles, structure strongly dictates performance. Typical imaging in scanning transmission electron microscopy has sufficient resolution to probe the atomic structure of these nanoparticles, but contrast, dose, and projection limitations make conventional methods insufficient for fully characterizing these structures. Phase retrieval techniques provide a promising alternative imaging mode, and, in particular, multislice electron ptychography can recover depth-dependent information. Here, we study beam-sensitive photon upconverting core–shell nanoparticles with a multislice ptychography approach using a low electron dose to avoid damage. Large strain fields arise in these heterostructures due to the mismatch in lattice parameter between the core and the shell. We reconstruct both a nanoparticle that appears defect-free and one that has a large break in the side and map the distribution of strain in 3D by computing distortion fields from high-resolution potential images of each slice. In the defect-free nanoparticle, we observe twisting of the shell, while in the broken nanoparticle, we measure the 3D position of the crack, the core, and dislocations. These results highlight the advantage of multislice electron ptychography to recover 3D information from a single scan, even under strict electron dose requirements from beam-sensitive samples.
74 ATOMIC AND MOLECULAR PHYSICS↗
Multicolor Inks of Black Phosphorus for Midwave‐Infrared Optoelectronics
Abstract Black phosphorus (bP) based ink with a bulk bandgap of 0.33 eV ( λ = 3.7 µm) has recently been shown to be promising for large‐area, high performance mid‐wave infrared (MWIR) optoelectronics. However, the development of multicolor bP inks expanding across the MWIR wavelength range has been challenging. Here a multicolor ink process based on bP with spectral emission tuned from 0.28 eV ( λ = 4.4 µm) to 0.8 eV ( λ = 1.5 µm) is demonstrated. Specifically, through the reduction of bP particle size distribution (i.e., lateral dimension and thickness), the optical bandgap systematically blueshifts, reaching up to 0.8 eV. Conversely, alloying bP with arsenic (bP 1− x As x ) induces a redshift in the bandgap to 0.28 eV. The ink processed films are passivated with an infrared‐transparent epoxy for stable infrared emission in ambient air. Utilizing these multicolor bP‐based inks as an infrared light source, a gas sensing system is demonstrated that selectively detects gases, such as CO 2 and CH 4 whose absorption band varies around 4.3 and 3.3 µm, respectively. The presented ink formulation sets the stage for the advancement of multiplex MWIR optoelectronics, including spectrometers and spectral imaging using a low‐cost material processing platform.
Kim, Jae Ik↗
Oxygen Transport through Amorphous Cathode Coatings in Solid-State Batteries
Not Available
25 ENERGY STORAGE↗
Quantitative Mid-infrared Photoluminescence Characterization of Black Phosphorus–Arsenic Alloys
Black phosphorus (bP) is a promising material for mid-infrared (mid-IR) optoelectronic applications, exhibiting high performance light emission and detection. Alloying bP with arsenic extends its operation toward longer wavelengths from 3.7 μm (bP) to 5 μm (bP 3 As 7 ), which is of great practical interest. Quantitative optical characterizations are performed to establish black phosphorus-arsenic (bPAs) alloys optoelectronic quality. Anisotropic optical constants (refractive index, extinction coefficient, and absorption coefficient) of bPAs alloys from near-infrared to mid-IR (0.2-0.9 eV) are extracted with reflection measurements, which helps optical device design. Quantitative photoluminescence (PL) of bPAs alloys with different As concentrations are measured from room temperature to 77 K. PL quantum yield measurements reveal a 2 orders of magnitude decrease in radiative efficiency with increasing As concentration. An optical cavity is designed for bP 3 As 7 , which allows for up to an order of magnitude enhancement in the quantum yield due to the Purcell effect. In conclusion, our comprehensive optical characterization provides the foundation for high performance mid-IR optical device design using bPAs alloys.
36 MATERIALS SCIENCE↗
Generalization Across Experimental Parameters in Neural Network Analysis of High-Resolution Transmission Electron Microscopy Datasets
Neural networks are promising tools for high-throughput and accurate transmission electron microscopy (TEM) analysis of nanomaterials, but are known to generalize poorly on data that is “out-of-distribution” from their training data. Given the limited set of image features typically seen in high-resolution TEM imaging, it is unclear which images are considered out-of-distribution from others. Here, we investigate how the choice of metadata features in the training dataset influences neural network performance, focusing on the example task of nanoparticle segmentation. We train and validate neural networks across curated, experimentally collected high-resolution TEM image datasets of nanoparticles under various imaging and material parameters, including magnification, dosage, nanoparticle diameter, and nanoparticle material. Overall, we find that our neural networks are not robust across microscope parameters, but do generalize across certain sample parameters. Additionally, data preprocessing can have unintended consequences on neural network generalization. Our results highlight the need to understand how dataset features affect deployment of data-driven algorithms.
42 ENGINEERING↗
Much more to explore with an oxidation state of nearly four: Pr valence instability in intermetallic m -Pr 2 Co 3 Ge 5
For some intermetallic compounds containing lanthanides, structural transitions can result in intermediate electronic states between trivalency and tetravalency; however, this is rarely observed for praseodymium compounds. The dominant trivalency of praseodymium limits potential discoveries of emergent quantum states in itinerant 4$f$ 1 systems accessible using Pr 4+ -based compounds. Here, we use in situ powder x-ray diffraction and in situ electron energy-loss spectroscopy (EELS) to identify an intermetallic example of a dominantly Pr 4+ state in the polymorphic system Pr 2 Co 3 Ge 5 . The structure-valence transition from a nearly full Pr 4+ electronic state to a typical Pr 3+ state shows the potential of Pr-based intermetallic compounds to host valence-unstable states and provides an opportunity to discover previously unknown quantum phenomena. In addition, this work emphasizes the need for complementary techniques like EELS when evaluating the magnetic and electronic properties of Pr intermetallic systems to reveal details easily overlooked when relying on bulk magnetic measurements alone.
36 MATERIALS SCIENCE↗