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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Universal liquid sample device and process for high resolution transmission electron microscope imaging and multimodal analyses of liquid sample materials

Liquid sample imaging devices and processes are disclosed for high resolution TEM imaging and multimodal analyses of liquid sample materials in situ under high vacuum that are compatible with standard type TEM chip membranes and TEM sample holders allowing TEM liquid sample imaging to be performed wherever a TEM instrument is accessible and at a substantially reduced cost compared to prior art systems and approaches.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Multimodal Data Representation with Deep Learning for Extracting Cancer Characteristics from Clinical Text

This paper presents a multimodal data representation to improve the performance of deep learning models for extracting cancer key characteristics from unstructured text in pathology reports. Specifically, in addition to using the text as the input to deep learning models, we use concept unique identifiers (CUIs) as another source of information to the models. We analyze the performance of different text and CUI data representations, including word embeddings and bag of embeddings (BOE), with a convolutional neural network (CNN) and a fully connected multilayer perceptron neural network (MLP-NN). The high level document embeddings from text and CUI inputs are combined by concatenating them and then applying a classifier. The model is used for extracting cancer subsite and histology from pathology reports. These two classification tasks have a large number of labels, i.e. 317 for subsite and 556 for histology, with extreme class imbalance. We compare the performance of the developed DL models across the two tasks based on micro- and macro-F1 scores. The evaluation shows that a multi-channel DL model that utilizes text represented by word embeddings and CUIs represented by BOE outperforms other DL models. Also, this approach significantly improves the model performance on low prevalence classes.

Alawad, Mohammed↗
DOE OSTI2021

Multimodal communication system

A multimodal communication system includes a vehicle controller that controls one or more systems or subsystems of an unmanned vehicle. The communication controller manages communication and logical access to the unmanned vehicle. One or more sensors in communication with the communication controller detects and measures physical properties in proximity to the unmanned vehicle. Transceivers receive the unmanned vehicle commands that are transmitted simultaneously or concurrently and a monotonic generator generates a monotonic object each time the unmanned vehicle's operating state changes. The communication controller executes the first unmanned vehicle command received that has the correct cryptographic hash validating knowledge of the unmanned vehicle's current operating state.

Duncan, Andrew M.↗
DOE OSTI2023

Multimodal photon and neutron imaging devices with fast event-by-event multisampling readout and optimized particle type discrimination

A particle imaging method for distinguishing between types of incident particles, such as neutrons, photons, and alphas, and improving the position resolution of particle imaging devices with matrix readout. The method includes high frequency multisampling readout electronics that provides the sequences of multiple measurements for each detected event, resulting in recorded detailed waveform information describing the signals. Such detailed information is used to approximate each signal waveform with a parameterized function in which the extracted parameter sets determine the type of the incident particle in an optimized fashion. The detailed event-by-event multisampling information for each signal readout channel in the matrix readout of the radiation imaging devices improves and optimizes the position resolution for variable shapes of the signals. Such devices can be used in mixed radiation fields, creating a new class of multimodal photon and neutron imagers.

Degtiarenko, Pavel V.↗
DOE OSTI2023

Multimodal nonlinear optical imaging via evanescent wave excitation

A system for multimodal nonlinear optical imaging is provided. Each mode uses a high NA objective to cause total internal reflection excitation at a sample-substrate interface. The system has a femtosecond oscillator to generate pulses used for two beams. The objective receives at least one beam, redirects the received at least one beam through a dielectric substrate to cause the TIR and produces corresponding evanescent waves in a portion of the sample adjacent to the sample-substrate interface, and collects a backward-propagating beam of pulses of responsive light. The portion of the sample illuminated by the evanescent waves emits responsive light. Different modes or combinations of the distinct modalities may be selected to access complementary chemical and structural information for various chemical species near the sample-substrate interface. Each mode may have mode-specific control such as selective beam blocking, power ratios and filtering.

Doughty, Benjamin L.↗
DOE OSTI2025

Towards Content Authenticity: Multimodal Fake News Detection and AI-Generated Text Identification

In today’s digital world, the spread of fake news and the rise of AI-generated text have become major threats to content authenticity and public trust. This thesis addresses both challenges through two complementary research directions: detecting fake news using multimodal features, and identifying AI-generated text using semantic and structural reasoning. The first part of the work focuses on fake news detection by introducing a novel model that combines text and image features through a unique rotational attention mechanism. Unlike traditional attention methods, this approach rotates the roles of query, key, and value across modalities to capture deeper interactions. Additionally, the model incorporates external domain information by linking news posts to top-ranked websites from Google search results, which helps assess the credibility of content based on its broader web context. This results in a more reliable and accurate fake news detection system that outperforms existing state-of-the-art methods. The second part presents SGG-ATD, a new framework for detecting AI-generated text. It uses masked language modeling to measure sentence coherence, followed by constructing a graph where keywords—both original and predicted—are connected based on semantic and contextual similarity. A Graph Convolutional Network (GCN) is then used to learn structural relationships within the text for final classification. Experimental results demonstrate that SGG-ATD achieves high F1-scores and consistently outperforms strong baselines. This method contributes to robust AI text detection, supporting accountability and resilience against AI-driven misinformation.

Gupta, Nidhi↗
DOE OSTI2026

Universal monitored dynamics in multimode bosonic systems

We propose a route to study monitored many-body dynamics in multimode bosonic systems using circuit quantum electrodynamics. In this experimental setting, we construct several bosonic models comprising brickwork circuits built from beam-splitter gates, local parity measurements, and optional on-site Hubbard interactions, and diagnose their monitored dynamics via ancilla purification and a learnability-based probe. Under parity measurements, generic gate sets exhibit behavior that is largely consistent with a conventional measurement-induced phase transition, while a special class of beam-splitter circuits shows an apparent critical-like high-measurement regime in which purification times scale linearly with system size. We show that for realistic noise, gate, and measurement rates, these signatures are observable with near-term circuit QED hardware.

Patel, Shivam [Rutgers U., Piscataway]↗
DOE OSTI2023

3D and Multimodal X‐Ray Microscopy Reveals the Impact of Voids in CIGS Solar Cells

Small voids in the absorber layer of thin-film solar cells are generally suspected to impair photovoltaic performance. They have been studied on Cu(In,Ga)Se 2 cells with conventional laboratory techniques, albeit limited to surface characterization and often affected by sample-preparation artifacts. Here, synchrotron imaging is performed on a fully operational as-deposited solar cell containing a few tens of voids. By measuring operando current and X-ray excited optical luminescence, the local electrical and optical performance in the proximity of the voids are estimated, and via ptychographic tomography, the depth in the absorber of the voids is quantified. Besides, the complex network of material-deficit structures between the absorber and the top electrode is highlighted. Despite certain local impairments, the massive presence of voids in the absorber suggests they only have a limited detrimental impact on performance.

14 SOLAR ENERGY↗
DOE OSTI2025

Predicting High‐Resolution Spatial and Spectral Features in Mass Spectrometry Imaging with Machine Learning and Multimodal Data Fusion

Recent advancements in molecular Mass Spectrometry Imaging have sparked interest in integrating high spatial resolution methods with molecular mass-spectrometry-based chemical imaging. Fusion-based algorithms have proven effective in generating high spatial-resolution molecular mass spectra. However, a significant challenge stems from the differing physical mechanisms underlying image generation and data upsampling techniques, potentially leading to discrepancies in integrated information channels. Integrating physical constraints into data processing workflows is essential to tackle this issue. In this study, we propose an innovative approach that merges data from Fourier transform ion cyclotron resonance (FTICR), time-of-flight matrix-assisted laser desorption/ionization, and time-of-flight secondary ion mass spectrometry imaging techniques. By leveraging FT-ICR's unparalleled spectral resolution and ToF-SIMS's exceptional spatial resolution, we achieve submicron spatial resolution, enabling the observation of intact molecular species with remarkable spectral precision. Canonical correlation analysis is employed to incorporate physical constraints. Through sophisticated image processing and machine learning techniques, the results of this fusion hold significant promise for advancing our comprehension of complex systems and unveiling concealed molecular intricacies.

canonical correlation analysis↗
DOE OSTI2022

Multimodal sensor fusion framework for residential building occupancy detection

For several years now, smart building energy systems have been a research area of intensive activity. In light of the increasing need for sustainable buildings and energy systems, this trend motivates an increasing need for a solution to reduce carbon dioxide emissions and improve energy efficiency. This work proposes a high-performing and transferable occupancy detection framework that combines sensor data from different data modalities, including time series environmental data (temperature, humidity, and illuminance), image data, and acoustic energy data using ensemble method. To draw out the best prediction performance in each modality, the proposed framework was developed, including various models that were designed to learn the occupancy patterns reflected in the physical data streams. To tackle the time series environmental data, we designed two variants of an occupancy detection spatiotemporal pattern network (Occ-STPN) that performs both feature level and decision level fusion, respectively. We also propose a new metric; the fading memory mean square error (FMMSE), that provides a fair evaluation and penalization of delayed occupancy predictions. Multiple open-sourced datasets, including the Electricity Consumption and Occupancy and the University of California, Irvine's (UCI) building occupancy detection dataset, along with our own real data collected from six different houses, were used to validate the algorithms' performance. The experimental results presented herein break down the performance for each sensing modality, and a detailed analysis of the performance is also discussed.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

A photosensitive sustainable lignin nanoplatform for multimodal image-guided mitochondria-targeted photodynamic and photothermal therapy

Photoactivated nanocarriers exhibit significant potential for anticancer therapy, but complex design strategies, unsustainable substrates, and short wavelengths limit their practical application. Here, we designed a new lignin-derived photoactivated nanomaterial that exploits the sensitivity of the β-O-4 bond of lignin to singlet oxygen. This sustainable product was loaded with mitochondria-targeting chlorin e6 and black phosphorus quantum dots (BPQDs) to produce BPQDs@N-LgC NPs, which were used for mitochondria-targeted fluorescence/photoacoustic-guided photothermal and photodynamic therapy. When irradiated at 808 nm, the BPQDs in BPQDs@N-LgC NPs exhibited good photothermal conversion, which allowed photoacoustic imaging and inhibited tumor growth. When irradiated at 660 nm, the BPQDs@N-LgC NPs generated fluorescence and reactive oxygen species, which allowed photoluminescence imaging and further inhibited tumor growth. Cleavage of the β-O-4 bond of lignin by photo-triggered reactive oxygen species degraded the NPs and released the BPQDs, facilitating rapid excretion of the therapeutic nanomaterials. Our rationally designed BPQDs@N-LgC NPs exhibited good therapeutic efficacy, both in vitro and in vivo.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

A comparative study of multimodal data fusion strategies for planetary spectroscopy

Integrating heterogeneous data sources can improve scientific inference when different modalities capture complementary information, but doing so is challenging in high-dimensional, small-sample settings. In spectroscopy for planetary exploration, Laser-Induced Breakdown Spectroscopy (LIBS), Raman Spectroscopy (Raman), Visible Infrared Spectroscopy (VISIR), and Mid-Infrared Spectroscopy (MIR) each examine different aspects of composition and mineralogy, raising fundamental questions about when and how data fusion improves predictive performance. Using a Mars-relevant set of geologic standards with measurements from all four modalities, we present a rigorous systematic evaluation of four data fusion strategies: low-level (data) fusion, mid-level (feature) fusion, high-level (decision) fusion, and residual-boosting (sequential) fusion. We assess performance in predicting oxide composition via nested cross-validation and corrected significance testing to evaluate whether data fusion improves upon single-modality baselines. We show that data fusion does not uniformly improve accuracy, and that observed gains are modest, oxide-dependent, and sensitive to modality and model structure. To move beyond aggregate accuracy metrics, we use model coefficients, permutation importance, and residual gain analysis to examine how the fusion models weight individual modalities and to identify patterns of apparent complementarity or redundancy. Though focused on spectroscopy for planetary exploration, our framework for data fusion evaluation and interpretation extends to other scientific domains with heterogeneous and scarce data and provides a principled approach evaluating data fusion strategies, interpreting modality contributions, and understanding tradeoffs among data fusion strategies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Community Charging: Emerging Multifamily, Curbside, and Multimodal Practices

Approximately 44 million households—31% of the United States total—are in multifamily housing. This encompasses both rented and owned homes in apartment buildings, condominiums, townhouses, and mixed-use developments. Regardless of where they live, nearly one-third of Americans do not drive. As U.S. transportation is electrified, it will be imperative to support car-free or car-light households, as well as residents living in multifamily and rental housing, with tailored solutions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Continuous multimodal technologies in industrial microbiology: potential for achieving high process performance and agility

The field of industrial microbiology has great potential to tackle the most pressing challenges facing society, but today, this entails much more than classical fermentation technology followed by product upgrading. Methods are needed to confer unit-operation-level functionality and whole-system-level robustness, as well as flexibility. As examples of this concept, we focus here on methodologies often deployed at industrial scale as discrete processes—such as biological, chemical, and electrochemical unit operations—that are also capable of substantial complementarity when combined sequentially and operated continuously. A key and relatively new type of conversion step is made possible by catalyst immobilization methods at both the enzymatic and cellular level. Cases drawn from electro-fermentation for acetoin production and syngas-mediated microbial electrosynthesis of ethanol exemplify the synergy of combined operational approaches. Because modular processes can be treated as a series of reactor elements, considerable flexibility is possible. As discussed, however, not all processes are amenable to this intensification.

09 BIOMASS FUELS↗
DOE OSTI2025

Bridging multimodal microscopy for advanced characterization on nuclear fuel using machine learning

Uranium dioxide (UO 2 ), widely used as driver fuel in light water reactors, experiences microstructure and property change by nuclear fission reactions. This paper bridges the characterization of fresh UO 2 fuel at different length scales, serving as a baseline for future post irradiation examination of irradiated UO 2 fuel. To characterize the microstructural change of nuclear fuel, modern approaches cover a wide range of length scales through different characterization techniques, such as mm scale for Synchrotron-based X-ray computed tomography (SXCT) and microscale for focused ion beam (FIB) and scanning electron microscopy (SEM). It is challenging to bridge the data and knowledge of the same sample in different length scales. This paper proposed a deep learning framework leveraging transfer learning to detect microstructural defects, trained from a sparse FIB, SEM, and SXCT images. The proposed model achieved superior performance in defect segmentation on multiscale microscopic data compared to four of the latest deep learning models.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Machine Learning-Enabled Correlation and Modeling of Multimodal Response of Thin Film to Environment on Macro and Nanoscale Using “Lab-on-a-Crystal”

To close the feedback loop between artificial intellegence-controlled materials synthesis and characterization, material functionality must be rapidly tested. A platform for high-throughput multifunctional materials characterization is developed using a quartz crystal microbalance with auxiliary in-plane electrodes and a custom gas/vapor flow cell, enabling simultaneous scanning probe microscopy and electrical, optical, gravimetric, and viscoelastic characterization on the same film under controlled environment. The lab-on-a-crystal in situ multifunctional output allows direct correlations between the gravimetric/viscoelastic, electrical, and optical responses of polymer film in response to environment. Finally, when multiple film properties are used to augment the training set for machine learning regression, prediction of material response to the environment improves by a factor of 13 when <5% of the total dataset is used for model training.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Multiscale and Multimodal Characterization of 2D Titanium Carbonitride MXene

Here, a comprehensive study on the prototype solid solution phase carbonitride MXene Ti 3 CN is conducted using nuclear magnetic resonance, electron spin resonance, total and quasi-elastic neutron scattering, combined with density functional theory-based electronic structure and molecular dynamic calculations. The combination of experiment and theory lead toward rational atomic structural models of Ti 3 CN. The remnant Al ions from the etching process significantly tune the interlayer spacing, distinct from the more typical MXene, Ti 3 C 2 , prepared similarly. Neutron scattering indicates the surface terminations of Ti 3 CN display high oxygen and fluorine concentrations and rather low hydroxyl and hydrogen concentrations. Calculations show that the structure including both the residual Al ions and mixed surface terminations give the best agreement with the measurements. The water molecules in Ti 3 CN are highly immobile, in strong contrast to those in Ti 3 C 2 . The analysis of the electronic structure suggests that the nitride MXene displays higher conductivity than the carbides. Finally, the absence of hydroxyl groups in terminations, the solid-solution in the anion sites, the remnants within layers, and immobile water altogether make the carbonitrides a unique series in the MXene family, implying a further exploration of their exotic properties and applications in energy storage.

36 MATERIALS SCIENCE↗