arXiv subjects

James L Hart

Publications and source records attributed to James L Hart.

5 recordsLinked to original sources

arXiv2025

Enhanced Permittivity in Wurtzite ScAlN through Nanoscale Sc Clustering

ScN alloyed AlN (ScxAl1-xN, ScAlN) is a wurtzite semiconductor with attractive ferroelectric, dielectric, piezoelectric, and optical properties. Here, we show that ScAlN films (with x spanning 0.18 to 0.36) contain nanoscale Sc-rich clusters which maintain the wurtzite crystal structure. While both molecular beam epitaxy (MBE) and sputter deposited Sc0.3Al0.7N films show Sc clustering, the degree of clustering is significantly stronger for the MBE-grown film, offering an explanation for some of the discrepancies between MBE-grown and sputtered films reported in the literature. Moreover, the MBE-grown Sc0.3Al0.7N film exhibits a dispersive and anomalously large dielectric permittivity, roughly double that of sputtered Sc0.3Al0.7N. We attribute this result to the Sc-rich clusters locally reaching x ~ 0.5 and approaching the predicted ferroelectric-to-paraelectric phase transition, resulting in a giant (local) enhancement in permittivity. The Sc-rich clusters should similarly affect the piezoelectric, optical, and ferroelectric responses, suggesting cluster-engineering as a means to tailor ScAlNs functional properties.

cond-mat.mtrl-sci↗

arXiv2023

Embedding theory in ML toward real-time tracking of structural dynamics through hyperspectral datasets

In-situ Electron Energy Loss Spectroscopy (EELS) is an instrumental technique that has traditionally been used to understand how the choice of materials processing has the ability to change local structure and composition. However, more recent advances to observe and react to transient changes occurring at the ultrafast timescales that are now possible with EELS and Transmission Electron Microscopy (TEM) will require new frameworks for characterization and analysis. We describe a machine learning (ML) framework for the rapid assessment and characterization of in operando EELS Spectrum Images (EELS-SI) without the need for many labeled training datapoints as typically required for deep learning classification methods. By embedding computationally generated structures and experimental datasets into an equivalent latent space through Variational Autoencoders (VAE), we effectively predict the structural changes at latency scales relevant to closed-loop processing within the TEM. The framework described in this study is a critical step in enabling automated, on-the-fly synthesis and characterization which will greatly advance capabilities for materials discovery and precision engineering of functional materials at the atomic scale.

cond-mat.mtrl-sci↗

arXiv2023

In operando cryo-STEM of pulse-induced charge density wave switching in TaS$_2$

The charge density wave (CDW) material 1T-TaS$_2$ exhibits a pulse-induced insulator-to-metal transition, which shows promise for next-generation electronics such as memristive memory and neuromorphic hardware. However, the rational design of TaS$_2$ devices is hindered by a poor understanding of the switching mechanism, the pulse-induced phase, and the influence of material defects. Here, we operate a 2-terminal TaS$_2$ device within a scanning transmission electron microscope (STEM) at cryogenic temperature, and directly visualize the changing CDW structure with nanoscale spatial resolution and down to 300 μs temporal resolution. We show that the pulse-induced transition is driven by Joule heating, and that the pulse-induced state corresponds to nearly commensurate and incommensurate CDW phases, depending on the applied voltage amplitude. With our in operando cryo-STEM experiments, we directly correlate the CDW structure with the device resistance, and show that dislocations significantly impact device performance. This work resolves fundamental questions of resistive switching in TaS$_2$ devices critical for engineering reliable and scalable TaS$_2$ electronics.

cond-mat.mtrl-sci↗

arXiv2022

Emergence of Layer Stacking Disorder in c-axis Confined MoTe$_2$

The layer stacking order in 2D materials strongly affects functional properties and holds promise for next generation electronic devices. In bulk, octahedral MoTe$_2$ possesses two stacking arrangements, the Weyl semimetal T$_d$ phase, and the higher-order topological insulator 1T' phase; however, it remains unclear if thin exfoliated flakes of MoTe$_2$ follow the T$_d$, 1T', or an alternative stacking sequence. Here, we resolve this debate using atomic-resolution imaging within the transmission electron microscope. We find that the layer stacking in thin flakes of MoTe$_2$ is highly disordered and pseudo-random, which we attribute to intrinsic confinement effects. Conversely, WTe$_2$, which is isostructural and isoelectronic to MoTe$_2$, displays ordered stacking even for thin exfoliated flakes. Our results are important for understanding the quantum properties of MoTe$_2$ devices, and suggest that thickness may be used to alter the layer stacking in other 2D materials.

cond-mat.mtrl-sci↗

arXiv2019

A Synchrotron in the TEM: Spatially Resolved Fine Structure Spectra at High Energies

Fine structure analysis of core electron excitation spectra is a cornerstone characterization technique across the physical sciences. Spectra are most commonly measured with synchrotron radiation and X-ray spot sizes on the μm to mm scale. Alternatively, electron energy loss spectroscopy (EELS) in the (scanning) transmission electron microscope ((S)TEM) offers over a 1000 fold increase in spatial resolution, a transformative advantage for studies of nanostructured materials. However, EELS applicability is generally limited to excitations below ~2 keV, i.e., mostly to elements in just the first three rows of the periodic table. Here, using state-of-the-art EELS instrumentation, we present nm resolved fine structure EELS measurements out to an unprecedented 12 keV with signal-to-noise ratio rivaling that of a synchrotron. We showcase the advantages of this technique in exemplary experiments.

cond-mat.mtrl-sci↗