arXiv subjects

Jennifer Dionne

Publications and source records attributed to Jennifer Dionne.

8 recordsLinked to original sources

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

Temperature bandgaps and engineered thermal state access in driven nanophotonic resonators

We show that strong thermo-optic feedback in nanophotonic resonators creates forbidden steady state temperatures that are unreachable under any static excitation. This temperature bandgap opens when thermo-optic feedback gain overcomes optical and thermal dissipation, splitting an otherwise continuous thermal landscape into disconnected accessible bands. We experimentally map this temperature band structure using quasi-bound states in the continuum resonances in silicon metasurfaces. Continuous wavelength scans exploit spectrally accumulated thermal energy to access the forbidden interval, reaching up to ~88{\deg}C higher temperature than static excitation, both at the same wavelength and maximum power. A characteristic three-stage temperature rise near the band edge confirms the -1/2 critical exponent of saddle-node bifurcations and enables quantitative extraction of band-edge wavelengths. Combining external thermal bias with optical excitation drives programmable interband transitions, enabling nearly 8.5-fold amplification of temperature rise and wavelength-selective switching between thermal states separated by just 1 nm in excitation wavelength. The bandgap is fully designable through metasurface geometry and hybrid material integration, which tune optical absorption, confinement and resonance linewidth. These results establish the temperature bandgap as a designable degree of freedom in driven nanophotonic systems, opening routes toward all-optical thermal logic and programmable photothermal control of chemical and biological processes.

physics.optics↗

arXiv2023

Targeted materials discovery using Bayesian algorithm execution

Rapid discovery and synthesis of new materials requires intelligent data acquisition strategies to navigate large design spaces. A popular strategy is Bayesian optimization, which aims to find candidates that maximize material properties; however, materials design often requires finding specific subsets of the design space which meet more complex or specialized goals. We present a framework that captures experimental goals through straightforward user-defined filtering algorithms. These algorithms are automatically translated into one of three intelligent, parameter-free, sequential data acquisition strategies (SwitchBAX, InfoBAX, and MeanBAX). Our framework is tailored for typical discrete search spaces involving multiple measured physical properties and short time-horizon decision making. We evaluate this approach on datasets for TiO$_2$ nanoparticle synthesis and magnetic materials characterization, and show that our methods are significantly more efficient than state-of-the-art approaches.

cond-mat.mtrl-sci↗

arXiv2021

Interpretable Classification of Bacterial Raman Spectra with Knockoff Wavelets

Deep neural networks and other sophisticated machine learning models are widely applied to biomedical signal data because they can detect complex patterns and compute accurate predictions. However, the difficulty of interpreting such models is a limitation, especially for applications involving high-stakes decision, including the identification of bacterial infections. In this paper, we consider fast Raman spectroscopy data and demonstrate that a logistic regression model with carefully selected features achieves accuracy comparable to that of neural networks, while being much simpler and more transparent. Our analysis leverages wavelet features with intuitive chemical interpretations, and performs controlled variable selection with knockoffs to ensure the predictors are relevant and non-redundant. Although we focus on a particular data set, the proposed approach is broadly applicable to other types of signal data for which interpretability may be important.

eess.SP↗

arXiv2020

Driving energetically-unfavorable dehydrogenation dynamics with plasmonics

Nanoparticle surface structure and geometry generally dictate where chemical transformations occur, with the low-coordination-number, high-radius-of-curvature sites being energetically-preferred. Here, we show how optical excitation of plasmons enables spatially-controlled chemical transformations, including access to sites which, without illumination, would be energetically-unfavorable. We design a crossed-bar Au-PdHx antenna-reactor system that localizes electromagnetic enhancement away from the innately reactive PdHx nanorod tips. Using optically-coupled in situ environmental transmission electron microscopy, we track the dehydrogenation of individual antenna-reactor pairs with varying optical illumination intensity, wavelength, and hydrogen pressure. Our in situ experiments show that plasmons enable new catalytic sites, including hydrogenation dissociation at the nanorod faces. Molecular dynamics simulations confirm that these new nucleation sites are energetically unfavorable in equilibrium and only accessible via tailored plasmonic excitation.

physics.app-ph↗

arXiv2019

Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning

Rapid identification of bacteria is essential to prevent the spread of infectious disease, help combat antimicrobial resistance, and improve patient outcomes. Raman optical spectroscopy promises to combine bacterial detection, identification, and antibiotic susceptibility testing in a single step. However, achieving clinically relevant speeds and accuracies remains challenging due to the weak Raman signal from bacterial cells and the large number of bacterial species and phenotypes. By amassing the largest known dataset of bacterial Raman spectra, we are able to apply state-of-the-art deep learning approaches to identify 30 of the most common bacterial pathogens from noisy Raman spectra, achieving antibiotic treatment identification accuracies of 99.0$\pm$0.1%. This novel approach distinguishes between methicillin-resistant and -susceptible isolates of Staphylococcus aureus (MRSA and MSSA) as well as a pair of isogenic MRSA and MSSA that are genetically identical apart from deletion of the mecA resistance gene, indicating the potential for culture-free detection of antibiotic resistance. Results from initial clinical validation are promising: using just 10 bacterial spectra from each of 25 isolates, we achieve 99.0$\pm$1.9% species identification accuracy. Our combined Raman-deep learning system represents an important proof-of-concept for rapid, culture-free identification of bacterial isolates and antibiotic resistance and could be readily extended for diagnostics on blood, urine, and sputum.

q-bio.QM↗

arXiv2018

Active Polarization Control with a Parity-Time Symmetric Plasmonic Resonator

Control of the polarization state of light is essential for many technologies, but is often limited by weak light-matter interactions that necessitate long device path lengths or significantly reduce the signal intensity. Here, we investigate a nanoscale plasmonic aperture capable of modifying the polarization state of far-field transmitted light without loss in the probe signal. The aperture is a coaxial resonator consisting of a dielectric ring embedded within a metallic film; parity-time ($\mathcal{PT}$) symmetric inclusions of loss and gain within the dielectric ring enable polarization control. Since the coaxial aperture enables near-thresholdless $\mathcal{PT}$ symmetry breaking, polarization control is achieved with realistic levels of loss and gain. Exploiting this sensitivity, we show that the aperture can function as a tunable waveplate, with the transmitted ellipticity of circularly polarized incident light changing continuously with the dissipation coefficient from $π/2$ to 0 (i.e. linear polarization). Rotation of linearly polarized light with unity efficiency is also possible, with a continuously-tunable degree of rotation. This compact, low-threshold, and reconfigurable polarizer may enable next-generation, high-efficiency displays, routers, modulators, and metasurfaces.

physics.optics↗

arXiv2017

Broadband and Wide-Angle Nonreciprocity with a Nonhermitian Metamaterial

We theoretically demonstrate a non-hermitian metamaterial exhibiting broadband and wide-angle nonreciprocity. The metamaterial consists of planar metal-dielectric layers with a Parity-Time (PT) symmetric distrubtion of loss and gain. With increasing loss and gain, the bandstructure and bandgap are strongly modified; further, the PT potential leads to distinct internal field distributions when illuminated from different sides. Including nonlinearities arising from natural loss and gain saturation leads to nonreciprocal transmission in the visible over a 50 nm wavelength and 60$^{\circ}$ degree angular range.

physics.optics↗