arXiv subjects

Colin Ophus

Publications and source records attributed to Colin Ophus.

At least 19 recordsLinked to original sources

arXiv2026

Real-space overlap is not enough: ambiguity in nanobeam iterative ptychography

High-resolution iterative ptychography typically relies on a well-aligned, high-convergence-angle electron probe. Here we explore whether it can instead be performed at small convergence angles, relaxing the need for probe correctors and enabling experiments at low accelerating voltages or with a de-excited objective lens, as in Lorentz mode. Through experiments and simulations, we show that once the convergence angle is small enough that no diffracted disks overlap, the resulting reconstruction is ambiguous, posing a significant challenge for robust interpretation of results. This challenge arises because of the lack of interference between Bragg disks in the recorded diffraction pattern intensity, leading to no phase information for each reflection. Reconstructions with these data lead to degenerate objects in which rigid translations of the lattice and reversals of contrast of the object produce the same error between experimental data and the ptychography forward model. Increasing the real-space overlap between probe positions does not lift this degeneracy. An amorphous substrate can supply the missing phase relationships by giving the Bragg beams support in the gaps between disks. This phasing is fragile, however, and survives only where the forward model matches the experiment. At fixed dose, either constraining the object to be a pure phase object or introducing thermal motion into the forward model is enough on its own to make the solution non-unique, highlighting why our experimental reconstructions below the overlap threshold are ambiguous despite ample dose and real-space redundancy. Most troublingly, the lattice spacing and orientation are always recovered correctly, so a non-unique reconstruction looks convincing and can be diagnosed only by repeating the reconstruction from different starting points.

cond-mat.mtrl-sci↗

arXiv2026

Probing three-dimensional structures of complex colloidal quantum dots at the single-atomic level

Colloidal quantum dots (QDs) are promising optoelectronic materials due to their size-tunable properties, yet their three-dimensional (3D) quantum confinement makes electronic states highly sensitive to structural and chemical heterogeneity, which critically impacts their optoelectronic performance. Accurately resolving the 3D atomic structure with sub-angstrom precision is thus essential for rational design. Here, we applied atomic electron tomography (AET) to determine, for the first time, the 3D atomic structure of complex core/shell QDs, resolving over 14,000 atoms per particle. Our reconstructions reveal surface morphology, eccentric cores, and nearly atomically abrupt heterovalent interfaces and identify anisotropic shell growth directed by twin boundaries. Utilizing an AET-derived atomic structure, we performed large-scale quantum mechanical calculations to uncover an orientation-dependent strain accommodation mechanism where the heterogeneous strain is compensated at interfaces and twin boundaries. Furthermore, our results reveal strain-induced localized states near the band edge, which contribute to the key features of the experimental ensemble absorption spectrum. This work sets a new benchmark for atomic-level characterization, establishing a powerful framework for the rational design of next-generation nanomaterials.

cond-mat.mtrl-sci↗

arXiv2026

Mapping Order in Semicrystalline Polymers using Machine Learning of Nanobeam Electron Diffraction

Organic mixed ionic electronic conductors (OMIECs) are a promising class of polymer materials for applications spanning neuromorphic computation to energy efficient electronics and bioelectronics. Despite being highly tunable, the relationship between structural features and key performance properties such as charge carrier mobility is poorly understood. Scanning nanodiffraction in the transmission electron microscope (TEM) is a powerful probe for elucidating this structure-property relationship, but produces large, noisy datasets that are difficult to interpret because polymer reflections exhibit several distinct morphologies. To address the complexity, we trained a machine learning (ML) model to detect these polymer diffraction peaks and their intensities from synthetic data. Compared to correlative peak detection algorithms, the conventional method for analyzing nanobeam 4D scanning transmission electron microscopy (4DSTEM) data, we show that the ML model is significantly faster and outperforms correlative algorithms in almost all cases, opening up the possibility of near-live visualization of 4DSTEM experiments.

cond-mat.mtrl-sci↗

arXiv2026

Hydration-controlled twist forms a moir\'e glass in charge-frustrated layered silicates

Twisting layered materials produces moir\'e superlattices, but prescribed twist angles are usually obtained by demanding assembly procedures. Here we show that montmorillonite, an abundant swelling clay, forms tunable moir\'e superlattices naturally. Focal-series high-resolution transmission electron microscopy, geometric phase analysis, and molecular dynamics simulation reveal that its apparent rotational disorder is biased toward low-angle misorientations inherited from discrete hydration states. Multilayer stacks preferentially adopt twists near 1-2{\deg}, 4{\deg}, and 10{\deg}, producing long-wavelength moir\'es without long-range rotational order. We define this kinetically trapped state as a moir\'e glass, distinct from featureless turbostratic stacking. Simulations indicate that lattice-charge disorder stabilizes the angular preferences, whereas charge ordering promotes random stacking. Hydration screens interlayer interactions and lubricates twist, while dehydration arrests the resulting configurations in discrete steps. These results establish dynamic hydration as a macroscopic handle for programming twist in layered matter.

cond-mat.mtrl-sci↗

arXiv2026

Visualizing Degradation in Anode-Free High-Utilization Aqueous Batteries Across Cell Lifetime

Operando microscopy has unveiled key mechanistic insights in battery materials during early cycling, but long-term characterization to unveil material evolution, degradation, and failure remain limited. To address this gap, we develop a custom operando optical microscope that captures images across hundreds of cycles and hours using optically accessible, anode-free pouch cells. We image through-plane, bulk-representative electrodeposition behavior of aqueous tin metal anodes, which are promising due to their high energy density but whose reactivity limits practical cycle life. We show that substrate governs the morphology and stability of plated tin, particularly at high plated capacities. Specifically, copper substrates exhibit a multi-stage tin growth mode, which results in high overpotentials and irreversible active material loss at high plated capacities. In contrast, graphite substrates display a single-stage growth mode with slower kinetics. Using this insight, we balance performance and stability to demonstrate a high-utilization (70%, 630 mAh g$^{-1}_{Sn}$) porous graphite substrate Sn anode with high efficiency and long lifetime. Our results underscore the importance of material and device optimization guided by operando characterization across device lifetime with broad applicability to electrochemical systems.

cond-mat.mtrl-sci↗

arXiv2026

Plasmonic Photocatalysis Enables Selective Oxidative Coupling of Methane with Nitrous Oxide under Ambient Conditions

Methane (CH4) and nitrous oxide (N2O) are potent greenhouse gases that represent substantial chemical energy. Conversion of these abundant waste gases to high-value chemicals typically requires high temperatures up to 1000 C, producing substantial CO2 emissions and limited selectivity toward desirable multi-carbon products. Here we demonstrate a plasmonic photocatalyst that enables CH4 and N2O conversion under ambient conditions to form C2 and C3 hydrocarbons. By systematically tuning AuPd alloys on TiO2, we identify an optimal composition (AuPd0.05) where Au enhances light harvesting and Pd enables selective C-H activation and C-C coupling. Under visible-light illumination, this catalyst produces C2H4, C2H6, C3H6, and C3H8 with ~80% selectivity while suppressing CO2 formation. In-situ spectroscopy and hot-carrier calculations show that plasmon-generated carriers redistribute interfacial hydroxyl intermediates, shifting the hydrophilic center to suppress overoxidation. Ab-initio calculations further reveal the reduction in C-C coupling barriers from 2.7 eV to 0.7 eV under illumination. Our work illustrates how engineering interfacial electronic and adsorbate dynamics enables selective multicarbon formation.

cond-mat.mtrl-sci↗

arXiv2026

Crystallographic Challenges in Microscopy of Multidomain Spinel Materials

Electron microscopy techniques are instrumental in the characterization of energy storage materials, with atomic resolution images providing the detailed structural features that are needed to understand their properties. Atomically resolved electron microscopy techniques have been routinely used to study the microstructure in high performing Mn-based oxide cathodes, which often contain spinel-like ordering. Here, we evaluate STEM-HAADF imaging and subsequent Fourier filtering as tools for characterizing {\delta}-DRX spinel domains and their antiphase boundaries, which play a central role in the material's electrochemical performance. Using electron microscopy simulations and recent theoretical insight into the structural makeup of {\delta}-DRX, we attempt to characterize the crystallographic spinel variants which occur in its multi-domain structure. We show that each domain interface, arising from pairings among eight distinct variants, can be categorized into one of four Fourier filtered profiles, one of which leaves the boundary undetectable in atomically resolved electron microscopy when viewed along the preferred [110] zone axis. Our results also suggest that the appearance of seemingly disordered or layered-like regions might actually arise from low energy domain boundaries which are slanted relative to the [110] viewing direction. Our findings highlight the need for careful interpretation of atomic-resolution micrographs of phase transitions, where local reordering drives transformations from higher to lower symmetry structures while maintaining lattice coherence.

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

Nanocrystal Geometry Governs Phase Transformation Pathways in Palladium Hydride

Pathways and structural dynamics of phase transformations impact performance of materials in energy and information storage technologies. Palladium hydride ($\mathrm{PdH}_x$) nanocrystals are an ideal model system for studying solute-induced phase transformations, where elastic energy from lattice mismatch between $\alpha$-$\mathrm{PdH}_x$ and $\beta$-$\mathrm{PdH}_x$ phases is often considered a key to determining the transformation pathways. $\alpha/\beta$-$\mathrm{PdH}_x$ interfacial elastic energy is affected by the confined geometry of a nanocrystal. However, how nanocrystal geometry influences phase transformation pathways is largely unknown. Using in situ liquid phase transmission electron microscopy, we directly visualize hydrogenation in Pd nanocrystals with two geometries -- a nanocube and a hexagonal nanoplate. Both follow similar sequences of an initially curved nucleus, interface flattening, and reverse-stage nucleation; however, their evolving $\alpha/\beta$-$\mathrm{PdH}_x$ interfaces exhibit geometry-dependent crystallographic alignments. In nanocubes, $\{100\}$-aligned configurations conform to static elastic energy ordering, representing a pathway that maintains a local mechanical equilibrium, whereas nanoplates display both $\{110\}$- and $\{211\}$-aligned interfaces. Theoretical simulations show that geometry determines the accessibility of alternative phase transformation pathways as the system is driven far from equilibrium during hydrogenation. These findings identify geometry as a fundamental parameter for directing phase transformation pathways, offering design principles for accessing atypical configurations and improving properties of intercalation-based devices.

cond-mat.mes-hall↗

arXiv2025

A Gaussian Parameterization for Direct Atomic Structure Identification in Electron Tomography

Atomic electron tomography (AET) enables the determination of 3D atomic structures by acquiring a sequence of 2D tomographic projection measurements of a particle and then computationally solving for its underlying 3D representation. Classical tomography algorithms solve for an intermediate volumetric representation that is post-processed into the atomic structure of interest. In this paper, we reformulate the tomographic inverse problem to solve directly for the locations and properties of individual atoms. We parameterize an atomic structure as a collection of Gaussians, whose positions and properties are learnable. This representation imparts a strong physical prior on the learned structure, which we show yields improved robustness to real-world imaging artifacts. Simulated experiments and a proof-of-concept result on experimentally-acquired data confirm our method's potential for practical applications in materials characterization and analysis with Transmission Electron Microscopy (TEM). Our code is available at https://github.com/nalinimsingh/gaussian-atoms.

eess.IV↗

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↗

arXiv2025

Deep generative priors for robust and efficient electron ptychography

Electron ptychography enables dose-efficient atomic-resolution imaging, but conventional reconstruction algorithms suffer from noise sensitivity, slow convergence, and extensive manual hyperparameter tuning for regularization, especially in three-dimensional multislice reconstructions. We introduce a deep generative prior (DGP) framework for electron ptychography that uses the implicit regularization of convolutional neural networks to address these challenges. Two DGPs parameterize the complex-valued sample and probe within an automatic-differentiation mixed-state multislice forward model. Compared to pixel-based reconstructions, DGPs offer four key advantages: (i) greater noise robustness and improved information limits at low dose; (ii) markedly faster convergence, especially at low spatial frequencies; (iii) improved depth regularization; and (iv) minimal user-specified regularization. The DGP framework promotes spatial coherence and suppresses high-frequency noise without extensive tuning, and a pre-training strategy stabilizes reconstructions. Our results establish DGP-enabled ptychography as a robust approach that reduces expertise barriers and computational cost, delivering robust, high-resolution imaging across diverse materials and biological systems.

eess.IV↗

arXiv2025

Programmable Beam Control for Electron Energy-Loss Spectroscopy and Ptychography

Programmable electron-beam scanning offers new opportunities to improve dose efficiency and suppress scan-induced artifacts in scanning transmission electron microscopy. Here, we systematically benchmark the impact of non-raster trajectories, including spiral and multi-pass sequential patterns, on two dose sensitive techniques: electron energy-loss spectroscopy (EELS) and ptychography. Using DyScO3 as a model perovskite, we compare spatial resolution, spectral fidelity, and artifact suppression across scan modes. Ptychographic phase reconstructions consistently achieve atomic resolution and remain robust to large jumps in probe position. In contrast, atomic-resolution EELS maps show pronounced sensitivity to probe motion, with sequential and spiral scans introducing non-uniform elemental contrast. Finally, spiral scanning applied under cryogenic conditions in BaTiO3 thin films improves dose uniformity and mitigates drift related distortions. These results establish practical guidelines for the implementation of programmable scan strategies in low-dose 4D-STEM and highlight the inherent resilience of ptychography to trajectory-induced artifacts.

cond-mat.mtrl-sci↗

arXiv2025

High-current p-type transistors from precursor-engineered synthetic monolayer WSe$_2$

Monolayer tungsten diselenide (WSe$_2$) is a leading candidate for nanoscale complementary logic. However, high defect densities introduced during thin-film growth and device fabrication have limited p-type transistor performance. Here, we report a combined strategy of precursor-engineered chemical vapor deposition and damage-minimizing fabrication to overcome this limitation. By converting tungsten trioxide and residual oxyselenides into reactive suboxides before growth, and precisely regulating selenium delivery during deposition, we synthesize uniform, centimeter-scale monolayer WSe$_2$ films with charged defect densities as low as $5 \times 10^{9}$ cm$^{-2}$. Transistors fabricated from these films achieve record p-type on-state current up to $888 \mu$A$\cdot\mu$m$^{-1}$ at $V_{\mathrm{DS}}=-1$ V, matching leading n-type devices. This leap in material quality closes the p-type performance gap without exotic doping or contact materials, marking a critical step towards complementary two-dimensional semiconductor circuits.

cond-mat.mtrl-sci↗

arXiv2025

3D Strain Field Reconstruction by Inversion of Dynamical Scattering

Strain governs not only the mechanical response of materials but also their electronic, optical, and catalytic properties. For this reason, the measurement of the 3D strain field is crucial for a detailed understanding and for further developments of material properties through strain engineering. However, measuring strain variations along the electron beam direction has remained a major challenge for (scanning-) transmission electron microscopy (S/TEM). In this article, we present a method for 3D strain field determination using 4D-STEM. The method is based on the inversion of dynamical diffraction effects, which occur at strain field variations along the beam direction. We test the method against simulated data with a known ground truth and demonstrate its application to an experimental 4D-STEM dataset from an inclined pseudomorphically grown Al$_{0.47}$Ga$_{0.53}$N layer.

cond-mat.mtrl-sci↗

arXiv2025

Beyond Contrast Transfer: Spectral SNR as a Dose-Aware Metric for STEM Phase Retrieval

The contrast transfer function (CTF) is widely used to evaluate phase retrieval methods in scanning transmission electron microscopy (STEM), including center-of-mass imaging, parallax imaging, direct ptychography, and iterative ptychography. However, the CTF reflects only the maximum usable signal, neglecting the effects of finite electron fluence and the Poisson-limited nature of detection. As a result, it can significantly overestimate practical performance, especially in low-dose regimes. Here, we employ the spectral signal-to-noise ratio (SSNR), as a dose-aware statistical framework to evaluate the recoverable signal as a function of spatial frequency. Using numerical reconstructions of white-noise objects, we show that center-of-mass, parallax, and direct ptychography exhibit dose-independent SSNRs, with close-form analytic expressions. In contrast, iterative ptychography exhibits a surprising dose dependence: at low fluence, its SSNR converges to that of direct ptychography; at high fluence, it saturates at a value consistent with the maximum detective quantum efficiency predicted by recent quantum Fisher information bounds. The results highlight the limitations of CTF-based evaluation and motivate SSNR as a more accurate, dose-aware metric for assessing STEM phase retrieval methods.

physics.optics↗

arXiv2025

Relaxing Direct Ptychography Sampling Requirements via Parallax Imaging Insights

Direct ptychography enables the retrieval of information encoded in the phase of an electron wave passing through a thin sample by deconvolving the interference effects of a converged probe with known aberrations. Under the weak phase object approximation, this permits the optimal transfer of information using non-iterative techniques. However, the achievable resolution of the technique is traditionally limited by the probe step size -- setting stringent Nyquist sampling requirements. At the same time, parallax imaging has emerged as a dose-efficient phase-retrieval technique which relaxes sampling requirements and enables scan-upsampling. Here, we formulate parallax imaging as a quadratic approximation to part of the direct ptychography kernel and use this insight to enable upsampling in direct ptychography. We validate our analytical results numerically using simulated and experimental reconstructions.

physics.optics↗