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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Applying Multimodal Mass Spectrometry to Image Tumors Undergoing Ferroptosis Following In Vivo Treatment with a Ferroptosis Inducer

Epithelial ovarian cancer (EOC) is the most common form of ovarian cancer. The poor prognosis generally associated with this disease has led to the search for improved therapies such as ferroptosis-inducing agents. Ferroptosis is a form of regulated cell death that is dependent on iron and is characterized by lipid peroxidation. Precise mapping of lipids and iron within tumors exposed to ferroptosis-inducing agents may provide insight into processes of ferroptosis in vivo and ultimately assist in the optimal deployment of ferroptosis inducers in cancer therapy. In this work, we present a method for combining matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) with secondary ion mass spectrometry (SIMS) to analyze changes in spatial lipidomics and metal composition, respectively, in ovarian tumors following exposure to a ferroptosis inducer. Tumors were obtained by injecting human ovarian cancer tumor-initiating cells into mice, followed by treatment with the ferroptosis inducer erastin. SIMS imaging detected iron accumulation in the tumor tissue, and sequential MALDI-MS imaging of the same tissue section displayed two chemically distinct regions of lipids. Additionally, one region was associated with the iron-rich area detected with SIMS, and the other region encompassed the remainder of the tissue section. Bulk lipidomics confirmed the lipid assignments putatively assigned from the MALDI-MS data. Overall, we demonstrate the ability of multimodal MSI to identify the spatial locations of iron and lipids in the same tissue section and associate these regions with clinical pathology.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

A robust and interpretable machine learning approach using multimodal biological data to predict future pathological tau accumulation

The early stages of Alzheimer’s disease (AD) involve interactions between multiple pathophysiological processes. Although these processes are well studied, we still lack robust tools to predict individualised trajectories of disease progression. Here, we employ a robust and interpretable machine learning approach to combine multimodal biological data and predict future pathological tau accumulation. In particular, we use machine learning to quantify interactions between key pathological markers (β-amyloid, medial temporal lobe atrophy, tau and APOE 4) at mildly impaired and asymptomatic stages of AD. Using baseline non-tau markers we derive a prognostic index that: (a) stratifies patients based on future pathological tau accumulation, (b) predicts individualised regional future rate of tau accumulation, and (c) translates predictions from deep phenotyping patient cohorts to cognitively normal individuals. Our results propose a robust approach for fine scale stratification and prognostication with translation impact for clinical trial design targeting the earliest stages of AD.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Efficient multimode Wigner tomography

Abstract Advancements in quantum system lifetimes and control have enabled the creation of increasingly complex quantum states, such as those on multiple bosonic cavity modes. When characterizing these states, traditional tomography scales exponentially with the number of modes in both computational and experimental measurement requirement, which becomes prohibitive as the system size increases. Here, we implement a state reconstruction method whose sampling requirement instead scales polynomially with system size, and thus mode number, for states that can be represented within such a polynomial subspace. We demonstrate this improved scaling with Wigner tomography of multimode entangled W states of up to 4 modes on a 3D circuit quantum electrodynamics (cQED) system. This approach performs similarly in efficiency to existing matrix inversion methods for 2 modes, and demonstrates a noticeable improvement for 3 and 4 modes, with even greater theoretical gains at higher mode numbers.

Science & Technology - Other Topics↗
DOE OSTI2025

Multimodal super-resolution: discovering hidden physics and its application to fusion plasmas

Understanding complex physical systems often requires integrating data from multiple diagnostics, each with limited resolution or coverage. We present a machine learning framework that reconstructs synthetic high-temporal-resolution data for a target diagnostic using information from other diagnostics, without direct target measurements during the inference. This multimodal super-resolution technique improves diagnostic robustness and enables monitoring even in case of measurement failures or degradation. Applied to fusion plasmas, our method targets edge-localized modes (ELMs), which can damage plasma-facing materials. By reconstructing super-resolution Thomson Scattering data from complementary diagnostics, we uncover fine-scale plasma dynamics and validate the role of resonant magnetic perturbations (RMPs) in ELM suppression through magnetic island formation. The approach provides new observation supporting the plasma profile flattening due to these islands. Our results demonstrate the framework’s ability to generate high-fidelity synthetic diagnostics, offering a powerful tool for ELM control development in future reactors like ITER. The approach is broadly transferable to other domains facing sparse, incomplete, or degraded diagnostic data, opening new avenues for discovery.

Jalalvand, Azarakhsh [Princeton Univ., NJ (United ↗
DOE OSTI2026

Multimodal framework for the joint analysis of single-cell RNA and T cell receptor sequencing data predicts T cell response to cancer immunotherapy

T cell states are prognostic in different cancer types. Recent technologies enable joint profiling of T cell RNA and T cell receptor (TCR) sequences at single-cell resolution. Here we present the TCR-RNA Integrating Model (TRIM), a multi-modal variational autoencoder framework that integrates RNA-TCR data and predicts T cell clonality and transcriptional states. TRIM learns a shared representation of the data conditioned on patient, tissue source, and treatment timepoint. We applied TRIM to three independent datasets that included T cells collected before and after checkpoint inhibitor treatment, sourced either from blood and tumor biopsies in patients with head and neck squamous cell carcinoma and colorectal cancer, or from tumor and adjacent tissue in a pan-cancer dataset. In all settings, TRIM accurately predicted intra-tumor T cell clonal expansion and transcriptional status based on T cells from blood or normal tissue before treatment, demonstrating its utility in modeling multimodal T cell data and predicting T cell response to treatment and disease progression.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Optimal 3D chemical imaging with multimodal electron tomography

Accurate mapping of nanoscale chemistry in three dimensions (3D) has been a longstanding challenge. Modern electron microscopy provides chemical images by electron energy loss spectroscopy (EELS) and energy dispersive x-ray spectrometry (EDX) but requires high fluences that damage specimens. In 3D, the requirements are worse; electron tomography demands many high-fluence chemical maps for reconstruction, creating a tradeoff between resolution, accuracy, and sample survival. Fused multimodal electron tomography (MM-ET) alleviates this requirement by leveraging lower-fluence high-angle annular dark-field (HAADF) images alongside a few chemical maps to dramatically improve chemical resolution. Here, experimental and computational parameter space is systematically explored to determine when MM-ET performs best. Ideal imaging conditions balance sample survival with resolution and chemical specificity; we recommend a tilt range of at least ± 70°, acquiring 40 equally spaced HAADF projections (signal-to-noise > 10), and 7 EELS/EDX maps of each chemistry (signal-to-noise > 4).

36 MATERIALS SCIENCE↗
DOE OSTI2023

Single-cell multimodal imaging uncovers energy conversion pathways in biohybrids

Microbe–semiconductor biohybrids, which integrate microbial enzymatic synthesis with the light-harvesting capabilities of inorganic semiconductors, have emerged as promising solar-to-chemical conversion systems. Improving the electron transport at the nano–bio interface and inside cells is important for boosting conversion efficiencies, yet the underlying mechanism is challenging to study by bulk measurements owing to the heterogeneities of both constituents. Here we develop a generalizable, quantitative multimodal microscopy platform that combines multi-channel optical imaging and photocurrent mapping to probe such biohybrids down to single- to sub-cell/particle levels. We uncover and differentiate the critical roles of different hydrogenases in the lithoautotrophic bacterium Ralstonia eutropha for bioplastic formation, discover this bacterium’s surprisingly large nanoampere-level electron-uptake capability, and dissect the cross-membrane electron-transport pathways. Furthermore, this imaging platform, and the associated analytical framework, can uncover electron-transport mechanisms in various types of biohybrid, and potentially offers a means to use and engineer R. eutropha for efficient chemical production coupled with photocatalytic materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Multimodal characterization of Te inclusions in Cd 1−x Zn x Te and Cd 1−x Zn x Te 1−y Se y for gamma and X-ray detectors

While CdZnTe (CZT) and CdZnTeSe (CZTS) semiconductors have emerged as compounds for room-temperature gamma and X-ray detection materials, they continue to be constrained by the formation of Te-inclusion defects generated during the growth and post-growth phases of the material, which adversely affect the detector performance. We demonstrate the utility of multimodal microscopic imaging and analysis for the characterization of the optical and electronic properties of Te inclusions in CZT and CZTS crystals at both micron and nanometer length scales. Having first identified regions with micron-scale Te inclusions using confocal Raman microscopy techniques, optically coupled infrared scattering near-field optical microscopic mapping was performed to map the distribution of these inclusions with nanometer spatial resolution and correlate the presence of Te inclusions in the matrix with other properties. Kelvin probe force microscopy was then utilized to characterize the variations of the work function associated with the presence of Te inclusions. Here, we observe an increase of ~ 240 mV in the work function associated with Te inclusions compared to the bulk CZT/CZTS crystals. Additionally, we observe that individual bulk grains in CZT can exhibit slight potential variations. Our findings develop a portrait of the charge trapping mechanisms in CZT and CZTS that act to degrade detector performance, while the demonstration of these combined microscopy techniques provides a new analytical tool that can be utilized for further optimization of the detector performance for these semiconducting compounds.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Elucidating a dissolution–deposition reaction mechanism by multimodal synchrotron X-ray characterization in aqueous Zn/MnO 2 batteries

Aqueous Zn/MnO 2 batteries with their environmental sustainability and competitive cost, are becoming a promising, safe alternative for grid-scale electrochemical energy storage. Here, presented as a promising design principle to deliver a higher theoretical capacity, this work offers fundamental understanding of the dissolution–deposition mechanism of Zn/β-MnO 2 . A multimodal synchrotron characterization approach including three operando X-ray techniques (powder diffraction, absorption spectroscopy, and fluorescence microscopy) is coupled with elementally resolved synchrotron X-ray nano-tomography. Together they provide a direct correlation between structural evolution, reaction chemistry, and 3D morphological changes. Operando synchrotron X-ray diffraction and spectroscopy show a crystalline-to-amorphous phase transition. Quantitative modeling of the operando data by Rietveld refinement for X-ray diffraction and multivariate curve resolution (MCR) for X-ray absorption spectroscopy are used in a complementary fashion to track the structural and chemical transitions of both the long-range (crystalline phases) and short-range (including amorphous phases) ordering upon cycling. Scanning X-ray microscopy and full-field nano-tomography visualizes the morphology of electrodes at different electrochemical states with elemental sensitivity to spatially resolve the formation of the Zn- and Mn-containing phases. Overall, this work critically indicates that for Zn/MnO 2 aqueous batteries, the reaction pathways involving Zn–Mn complex formation upon cycling become independent of the polymorphs of the initial electrode and sheds light on the interplay among structural, chemical, and morphological evolution for electrochemically driven phase transitions.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Multimodal quantification of degradation pathways during extreme fast charging of lithium-ion batteries

Enabling fast charging of Li-ion batteries will be a key step towards realizing the technology's full potential in electric vehicles. Currently, fast charging is limited by a variety of processes that reduce cell capacity upon extended cycling. Using a multimodal approach combining incremental capacity analysis (dQ/dV), high energy X-ray diffraction (HEXRD), and mass spectrometry titration (MST), we identify specific degradation mechanisms—including Li plating, dead Li x C 6 formation, Li 2 C 2 formation, solid carbonate solid-electrolyte interphase (SEI) deposition, and loss of positive electrode active material (LAM PE )—that occur during extended fast charge cycling. We find that Li plating is the major source of capacity loss in cells cycled at 6C, while non-carbonate SEI species deposition on the graphite anode is the main source of capacity loss when cycled at 4C. We also study local degradative phenomena by examining specific ~1–5 cm 2 regions of the cells using HEXRD and MST. Here, we find that plated Li is often collocated with dead Li x C 6 , Li 2 C 2 , and solid carbonate SEI species, and these additional species cumulatively account for ~20% of the capacity lost during 6C cycling. Finally, in a cell with an anomalously high amount of LAM PE (quantified via dQ/dV), we find that regions of cathode degradation were accompanied by non-carbonate SEI products on the adjacent region of the anode. We postulate that this phenomenon arises due to crosstalk between the electrodes, wherein soluble electrolyte oxidation products formed at the delithiated cathode migrate to the graphite anode and are ultimately deposited on the graphite surface. This work demonstrates the utility of combining multiple characterization techniques to reveal a more holistic understanding of degradative phenomena that occur across multiple length scales during fast charge.

25 ENERGY STORAGE↗
DOE OSTI2023

Spatially resolved multimodal vibrational spectroscopy under high pressures

In this perspective, we discuss the potential impact on in situ studies under controlled environments of a novel multimodal spectroscopic technique, optical photothermal infrared + Raman spectroscopy, which enables the simultaneous collection of infrared and Raman scattering spectra, along with hyperspectral imaging and chemical imaging with wavelength-independent sub-500 nm spatial resolution. A brief review of the current literature regarding the O-PTIR technique is presented along with recent work from our own lab on determining the crystallinity of soft and inorganic materials. The results highlight the possibility of resolving differences in the crystallinity of soft materials associated with changes in material processing. Here, we also demonstrate the first reported use of a diamond anvil cell with simultaneous infrared and Raman measurements that showcases, using a high energy material as an example, the potential use of O-PTIR spectroscopy in diamond anvil cell techniques.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Edge AI-Enhanced Traffic Monitoring and Anomaly Detection Using Multimodal Large Language Models

This paper addresses the challenge of traffic monitoring and incident detection in remote areas, utilizing multimodal large language models (LLMs) deployed on edge AI devices. The key novelty of the LLM is to convert real-time video streams into descriptive texts, enabling low-bandwidth transmissions and reliable detection of anomalies and incidents in environments of intermittent connectivity. The model is developed based on fine-tuning open-source LLMs and extending it with multi-modal capabilities to analyze video frames. Our work also involves deploying this model on edge devices such as Nvidia IGX Orin and is planned to be tested in realistic environments in future work. The methodology includes data set curation, iterative model fine-tuning and compression, and hardware-based optimization. This approach aims to enhance traffic safety and response speed in remote areas, marking a significant advancement in the application of AI for traffic monitoring and safety management.

Peruski, Ryan [University of Tennessee, Knoxville ↗
DOE OSTI2020

In situ multimodal imaging for nanoscale visualization of tribofilm formation

A third of the energy from fuel combustion in passenger car gasoline engines is lost due to friction. Carefully designed engine lubricants can recover some of these losses by reducing friction and wear by forming a nanometer-scale chemico-physico tribofilm between surfaces. Accordingly, attention has focused on developing oil formulations that form low-friction tribofilms. However, analyses of resultant tribofilms are typically conducted after tribo-tests with conventional characterization tools and do not offer insights into tribofilm formation and evolution, precluding information critical to tuning tribofilm properties. In this work, we developed a unique multimodal methodology based on Atomic Force Microscopy (AFM) with local probe heating for in situ tribological studies that activates friction modifiers and simultaneously captures the evolution of friction and surface roughness, with nanometer resolution. As a platform to demonstrate the ability of this methodology to visualize dynamics of tribofilm formation in situ, we apply it to molybdenum-based friction modifiers to distinguish key factors in their functionality and correlate nanoscale AFM and Friction Force Microscopy data to bench tribo-tests used in the industry. To decode the formation mechanisms observed in situ and underlying chemistry of tribofilms, we performed ab initio Molecular Dynamics (AIMD) simulations at comparable conditions. AIMD simulations confirmed both nanoscale and bench tribo-test results and showed deviations in molecular organization in tribofilms that are formed from different molybdenum-based friction modifiers that corroborates with surface functionality. With this innovative methodology, we demonstrate proof-of-principle in situ formation of molybdenum-based tribofilms directly on steel surfaces that could be applied generally to studying tribofilm formation.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Multimode two-dimensional vibronic spectroscopy. II. Simulating and extracting vibronic coupling parameters from polarization-selective spectra

We report experimental demonstrations of polarization-selection two-dimensional Vibrational-Electronic (2D VE) and 2D Electronic-Vibrational (2D EV) spectroscopies aim to map the magnitudes and spatial orientations of coupled electronic and vibrational coordinates in complex systems. The realization of that goal depends on our ability to connect spectroscopic observables with molecular structural parameters. In this paper, we use a model Hamiltonian consisting of two anharmonically coupled vibrational modes in electronic ground and excited states with linear and bilinear vibronic coupling terms to simulate polarization-selective 2D EV and 2D VE spectra. We discuss the relationships between the linear vibronic coupling and two-dimensional Huang–Rhys parameters and between the bilinear vibronic coupling term and Duschinsky mixing. We develop a description of the vibronic transition dipoles and explore how the Hamiltonian parameters and non-Condon effects impact their amplitudes and orientations. Using simulated polarization-selective 2D EV and 2D VE spectra, we show how 2D peak positions, amplitudes, and anisotropy can be used to measure parameters of the vibronic Hamiltonian and non-Condon effects. This paper, along with the first in the series, provides the reader with a detailed description of reading, simulating, and analyzing multimode, polarization-selective 2D EV and 2D VE spectra with an emphasis on extracting vibronic coupling parameters from complex spectra.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Initial evaluation of a multimodal diamond sensor for simultaneous temperature and radiation measurements

Several experimental disciplines require simultaneous neutron flux and temperature monitoring. This paper provides the groundwork on a diamond sensor concept that could be used in harsh environments where the limits of current technologies are exceeded. Here, we present initial results on the functionality of a multimodal diamond sensor, which can simultaneously sense the presence of alpha particles at a few counts per second at temperatures up to 494 K. Such a sensor may also have applications where both charged particles or neutron detection and temperature measurement are simultaneously desired.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Growing neoclassical tearing modes seeded via transient-induced-multimode interactions

Nonlinear extended magnetohydrodynamics simulations demonstrating seeding of neoclassical tearing modes (NTMs) via magnetohydrodynamics-transient-induced multimode interactions are presented. Simulations of NTMs are enabled by two recent NIMROD code developments: the implementation of heuristic neoclassical stresses and the application of transient magnetic perturbations (MPs) at the boundary. NTMs are driven unstable by the inherently pressure driven kinetic bootstrap current, which arises due to collisional viscosity between passing and trapped electrons. These simulations use heuristic closures that model the neoclassical electron and ion stresses. NTM growth requires a seed island, which is generated by a transiently applied MP in simulations. The capability is demonstrated using kinetic-based reconstructions with flow of a DIII-D ITER Baseline Scenario discharge (La Haye et al., in Proceedings IAEA FEC, 2020). The applied MP seeds a 2/1 NTM that grows in two phases: a slow growth phase followed by a faster robust growth phase. Additionally, an evolving sequence of higher order core modes are excited at first. Power transfer analysis shows that nonlinear interactions between the core modes and the 2/1 helps drive the initial slow growth. Once the induced 2/1 magnetic island reaches a critical width, the NTM transitions to faster robust growth, which is well described by the nonlinear modified Rutherford equation. Furthermore, this work highlights the role of nonlinear mode coupling in seeding NTMs.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Spectral dispersion of the linewidth enhancement factor and four wave mixing conversion efficiency of an InAs/GaAs multimode quantum dot laser

The spectral dependence of the linewidth enhancement factor (αH-factor) of a multimode InAs/GaAs quantum dot laser is analyzed. Amplified spontaneous and high-frequency modulation methods are used to experimentally retrieve the αH-factor of each longitudinal mode below and above the threshold. A dispersion of the αH-factor is unlocked across the entire optical spectrum, which is further illustrated in the context of four wave mixing experiments. The results show that the induced conversion efficiency is increased at lasing wavelengths where the linewidth enhancement is lower. These results highlight the importance of carefully monitoring the linewidth enhancement factor in quantum dot lasers especially for frequency combs and mode-locking applications in future optical communication systems.

Ding, Shihao (ORCID:0000000346416944)↗