arXiv subjects

Andrew Barnum

Publications and source records attributed to Andrew Barnum.

4 recordsLinked to original sources

arXiv2026

Harnessing diamond surface features for dense and aligned NV ensembles

Controlling nitrogen doping in diamond is key to advancing nitrogen-vacancy (NV) center devices. We harness the hillock, a typically undesirable surface feature, to incorporate high densities of grown-in, aligned NV-centers on a (001)-oriented substrate. Enhanced cathodoluminescence at hillock sidewalls is correlated via nanoSIMS to up to 1000x greater nitrogen incorporation compared to the planar film. We find that these hillocks are associated with stacking faults and edge-type dislocations, consistent with an origin in surface preparation rather than substrate screw dislocations. Yet, the growth is orderly enough that each of the four hillock sidewalls hosts a distinct NV orientation. A 1.7-2% grown-in NV/substitutional nitrogen (P1) ratio, 4x higher than typical (001)-oriented growth, is measured via NV decoherence analysis. By revealing that spontaneously formed hillocks act as natural laboratories for dense, aligned NV formation, this work motivates systematic investigation of facet-dependent nitrogen incorporation and preferential NV alignment in (001) diamond.

cond-mat.mtrl-sci↗

arXiv2026

In situ Gas-Cell Electron Microscopy Reveals Pressure-Selected Restructuring Pathways in AuRu Ammonia Catalysts

Bimetallic catalysts provide new routes toward sustainable ammonia synthesis, but the nanoscale structural dynamics under reaction-relevant conditions remain poorly understood. Here, we combine in situ gas-cell and multimodal electron microscopy to determine how temperature, gas pressure, and chemistry select among distinct restructuring pathways in AuRu nanocrystal catalysts. Initially, the AuRu nanocrystals form polycrystalline face-centered cubic (FCC) alloys with Au/Ru intermixing. Elevated temperature ($\geq 350~^\circ$C) induces intraparticle phase segregation into distinct Au-rich (FCC) and Ru-rich hexagonal close-packed (HCP) domains that exhibit localized plasmonic modes. Atmospheric-pressure 3:1 H$_2$:N$_2$ gas unlocks a distinct restructuring regime absent at lower pressures, characterized by pronounced faceting and nanovoid formation. Systematic gas variation identifies hydrogen as the dominant driver. Density functional theory-trained machine-learning interatomic potentials and grand-canonical Monte Carlo simulations reveal that H-Ru interactions enhance the Au/Ru diffusivity mismatch, promoting vacancy accumulation and nanovoid formation. Together, these results show that, rather than simply accelerating the thermally driven phase segregation observed at lower pressures, atmospheric-pressure H$_2$:N$_2$ gas redirects restructuring toward faceting and nanovoid formation through a gas-mediated Kirkendall-type mechanism.

cond-mat.mtrl-sci↗

arXiv2026

Unsupervised segmentation and clustering workflow for efficient processing of 4D-STEM and 5D-STEM data

Four-dimensional scanning transmission electron microscopy (4D-STEM) enables mapping of diffraction information with nanometer-scale spatial resolution, offering detailed insight into local structure, orientation, and strain. However, as data dimensionality and sampling density increase, particularly for in situ scanning diffraction experiments (5D-STEM), robust segmentation of structurally consistent behavior across sequential measurements becomes essential for efficient and physically meaningful analysis. Here, we introduce a clustering framework that identifies crystallographically distinct domains from 4D-STEM datasets. By using local diffraction-pattern similarity as a metric, the method extracts closed contours delineating spatially contiguous regions. This approach produces cluster-averaged diffraction patterns that improve signal quality while reducing data volume by orders of magnitude, enabling rapid and accurate orientation, phase, and strain mapping. We demonstrate the applicability of this approach to in situ liquid-cell 4D-STEM data of gold nanoparticle growth. Our method provides a scalable and generalizable route for spatially coherent segmentation, data compression, and quantitative structure-strain mapping across diverse 4D-STEM modalities. The full analysis code and example workflows are publicly available to support reproducibility and reuse.

cond-mat.mtrl-sci↗

arXiv2025

Missing Wedge Inpainting and Joint Alignment in Electron Tomography through Implicit Neural Representations

Electron tomography is a powerful tool for understanding the morphology of materials in three dimensions, but conventional reconstruction algorithms typically suffer from missing-wedge artifacts and data misalignment imposed by experimental constraints. Recently proposed supervised machine-learning-enabled reconstruction methods to address these challenges rely on training data and are therefore difficult to generalize across materials systems. We propose a fully self-supervised implicit neural representation (INR) approach using a neural network as a regularizer. Our approach enables fast inline alignment through pose optimization, missing wedge inpainting, and denoising of low dose datasets via model regularization using only a single dataset. We apply our method to simulated and experimental data and show that it produces high-quality tomograms from diverse and information limited datasets. Our results show that INR-based self-supervised reconstructions offer high fidelity reconstructions with minimal user input and preprocessing, and can be readily applied to a wide variety of materials samples and experimental parameters.

eess.IV↗