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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A split ribozyme system for in vivo plant RNA imaging and genetic engineering

RNA plays a central role in plants, governing various cellular and physiological processes. Monitoring its dynamic abundance provides a discerning understanding of molecular mechanisms underlying plant responses to internal (developmental) and external (environmental) stimuli, paving the way for advances in plant biotechnology to engineer crops with improved resilience, quality and productivity. In general, traditional methods for analysis of RNA abundance in plants require destructive, labour-intensive and time-consuming assays. To overcome these limitations, we developed a transformative innovation for in vivo RNA imaging in plants. Specifically, we established a synthetic split ribozyme system that converts various RNA signals to orthogonal protein outputs, enabling in vivo visualisation of various RNA signals in plants. We demonstrated the utility of this system in transient expression experiments (i.e., leaf infiltration in Nicotiana benthamiana ) to detect RNAs derived from transgenes and tobacco rattle virus, respectively. Also, we successfully engineered a split ribozyme-based biosensor in Arabidopsis thaliana for in vivo visualisation of endogenous gene expression at the cellular level, demonstrating the feasibility of multi-scale (e.g., cellular and tissue level) RNA imaging in plants. Furthermore, we developed a platform for easy incorporation of different protein outputs, allowing for flexible choice of reporters to optimise the detection of target RNAs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Real-time single-element-detection structured illumination optical metrology for laser powder bed fusion

Laser powder bed fusion (LPBF) is a type of metal additive manufacturing which could benefit from improved process monitoring to improve quality control. We demonstrate, for the first time to our knowledge, the coaxial monitoring of melt track formation in steel powder with spatial frequency modulation imaging (SPIFI), an enhanced-resolution imaging technique which uses a photodiode to record one-dimensional images. Using a custom live-display software and a high-speed SPIFI geometry, we offset the SPIFI field of view from the fusing beam to monitor different regions of the LPBF melt pool and surrounding area. This demonstrates the potential of SPIFI to monitor spatial features within the melt pool in real-time with increased data efficiency.

Hunter, Scott (ORCID:0009000886150312)↗
DOE OSTI2025

Chloride-Based Volatility for Waste Reduction and/or Reuse of Metallic-, Oxide- and Salt-Based Reactor Fuels

The objective of the chloride based volatility project (CBV) was to demonstrate the ability to separate uranium from used fuel to enable process improvements resulting in 10x reduction of waste volume, while maintaining safeguards standards and global backend costs at $\$$1/MW-hr. Current industrial practices perform separations of used fuel using solvent media in the form of aqueous and molten salt processes, resulting in contaminated process waste. The CBV approach utilized solid state chemistry with no solvent media and was successful in chlorinating uranium and fission product oxides that sublimed into the gas phase and were collected in targeted condensation zones based on temperature gradients of chlorinated products. Recovery of better than 95% of initial uranium in the form of UCl4 was demonstrated when simulated used nuclear fuel was used. Laser induced breakdown spectroscopy, LIBS, and ultraviolet-visible spectroscopy, UV-Vis, were combined into a high temperature flow cell design and utilized as process monitoring techniques to observed chlorinated products leave the reaction vessel in real time.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Virtual Inspection of Advanced Manufacturing via Process-Scale Digital Twins (Abbreviated Report)

Inspection and certification comprise the most significant bottlenecks in advanced manufacturing for NNSA applications, often requiring far more time and resources than the fabrication of the parts themselves. Traditional methods, such as manual review and X-ray computed tomography, are not only slow and costly, but also struggle to provide a clear connection between manufacturing instructions and the final performance of critical components. This gap limits both the agility and assurance needed to support the modernization and safety of the United States nuclear stockpile. In response, our Strategic Initiative established a digital twin framework that integrates realtime process monitoring, automated data analysis, and immersive virtual reality collaboration into a unified inspection pipeline. By leveraging data from sensors, machine instructions, and imaging, we created high-fidelity virtual models of manufactured parts that could be rapidly analyzed and certified. This approach was first demonstrated with Direct Ink Write, and then extended to other manufacturing settings, including conventional (or “subtractive”) manufacturing and to predict the end of life performance of parts per the aging and lifetimes programs. The result is a transformational capability: inspection times have been reduced by a factor of 120,000 without loss of accuracy and while simultaneously improving traceability and confidence in part quality. This framework not only streamlines certification for critical applications, but also positions the national security enterprise to respond more flexibly to emerging challenges, supporting agile manufacturing and digital engineering practices across a broad range of mission-relevant domains.

42 ENGINEERING↗
DOE OSTI2026

Enhanced Preparation for Intelligent Cybermanufacturing Systems (EPICS)

Opportunities exist for realizing transformative advances in productivity and reductions in energy footprint through ubiquitous sensing in manufacturing environments. Enhanced Preparation for Intelligent Cybermanufacturing Systems (EPICS) is a 21-month (4 academic semesters, plus one summer) experience for graduate students that focuses on scaling the knowledge, understanding and leadership skills in the cyber manufacturing area. Masters students (8/year, 32 total) complete 2-year projects on industrially-driven project topics, rotating to internships in summer semester to work on scoping and implementation at project partners. Students complete academic training in embedded systems, process modeling, data science, and cloud-based systems design. Their projects are targeted toward sensor retrofit, process monitoring, root cause analysis, and sensor fusion.

Advanced Manufacturing↗
DOE OSTI2024

7 Innovations in high-rate composite manufacturing: integrating additive manufacturing with compression molding process

Advanced composites play a pivotal role in modern engineering, offering exceptional strength-to-weight ratios and tailored properties, essential for various industries. High-rate composite manufacturing techniques have rapid production capabilities, which are essential for meeting the demands of industries requiring cost-saving, efficiency, and quick turnaround times. This chapter explores the Additive Manufacturing- Compression Molding (AM-CM) system developed by Oak Ridge National Laboratory (ORNL) for advanced composites manufacturing. The AM-CM system integrates additive manufacturing with compression molding, facilitating the production of polymer composite parts with superior mechanical properties and meticulously controlled microstructures. This innovative system not only ensures precise material deposition but also operates as a fast composite manufacturing process, enhancing productivity and performance, which are needed attributes across industrial applications. Through comprehensive mechanical testing and microstructural analysis, AM-CM promotes remarkable fiber alignment and reduced porosity in composite parts compared to alternative thermoplastic high-rate composite manufacturing methods. Furthermore, AM-CM enables overmolding reinforcement using continuous carbon fiber and supports selective reinforcement through customizable toolpaths. It also facilitates the production of hybrid materials to achieve tailored mechanical properties. Future advancements in AM-CM technology aim to enhance process efficiency, broaden material versatility, and improve part performance. This involves exploring novel materials, advancing process monitoring, implementing automation technologies, and integrating artificial intelligence (AI) and machine learning (ML) for predictive modeling and real-time optimization in composite manufacturing. These developments will establish the AM-CM system as a transformative technology in composite manufacturing, driving innovation across industries.

Hassen, Ahmed [ORNL] (ORCID:0000000328521222)↗
DOE OSTI2024

Online Monitoring of Catalytic Processes by Fiber-Enhanced Raman Spectroscopy

An innovative solution for real-time monitoring of reactions within confined spaces, optimized for Raman spectroscopy applications, is presented. This approach involves the utilization of a hollow-core waveguide configured as a compact flow cell, serving both as a conduit for Raman excitation and scattering and seamlessly integrating into the effluent stream of a cracking catalytic reactor. The analytical technique, encompassing device and optical design, ensures robustness, compactness, and cost-effectiveness for implementation into process facilities. Notably, the modularity of the approach empowers customization for diverse gas monitoring needs, as it readily adapts to the specific requirements of various sensing scenarios. As a proof of concept, the efficacy of a spectroscopic approach is shown by monitoring two catalytic processes: CO 2 methanation (CO 2 + 4H 2 → CH 4 + 2H 2 O) and ammonia cracking (2NH 3 → N 2 + 3H 2 ). Leveraging chemometric data processing techniques, spectral signatures of the individual components involved in these reactions are effectively disentangled and the results are compared to mass spectrometry data. This robust methodology underscores the versatility and reliability of this monitoring system in complex chemical environments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Low Power, Radiation Resilient Synchronous Edge Processing for Remote Monitoring

Next-generation space remote sensing systems may be equipped with imaging arrays that sense data at a rate that outstrips the processing capability of any computing hardware that can operate within a satellite’s power budget. This project developed novel convolutional and recurrent neural networks to detect and estimate point-like events amid clutter, and investigated their efficient and accurate implementation on analog in-memory computing systems that are 10-1000× more energy-efficient than digital processors. This project leveraged two memory devices at different levels of technological maturity: a large-scale analog computing prototype using commercial SONOS charge-trap memory, and electrochemical memory (ECRAM) with intrinsic radiation hardness. We experimentally demonstrated end-to-end analog processing of our neural networks on SONOS and characterized the radiation response of both SONOS and ECRAM. We advanced the state-of-the-art in ECRAM precision and reliability, and developed co-design methods to enable accurate long-term operation of SONOS analog accelerators in space radiation environments.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Application of Ocean Thermal Energy Conversion (OTEC) Systems for Powering Safety Monitoring Systems of Offshore Oil and Gas Operations. OESI 2.0 M-1 T-1-P1.1 – Objective 1 Interim Report: Summary of Offshore Oil and Gas Well Monitoring Systems and Process Power Requirements

This report is part of a comprehensive research initiative aimed at evaluating the technoeconomic feasibility of deploying greenhouse gas (GHG) emissions monitoring systems powered by small-scale renewable energy sources on the U.S. Outer Continental Shelf (OCS). The study has three primary objectives: summarizing available GHG monitoring systems for the OCS, modeling a renewable marine energy source (specifically ocean thermal energy conversion, or OTEC), and modeling the collocation of OTEC power sources with offshore oil and gas activities to offset power demand.

02 PETROLEUM↗
DOE OSTI2026

Non-Intrusive Load Monitoring of EBW Processes A Second Study [Slides]

The Objective is to Determine if induced off-nominal welding disturbances: (1) Beam arc out (weld on high vapor pressure aluminum plate); (2) Pulsed beam voltage; (3) Pulsed beam current; and (4) Pulsed beam focus current can be observed in the input power to the high-voltage cabinet powering the EB gun, ie Non-Instrusive Load Monitoring (NILM).

36 MATERIALS SCIENCE↗
DOE OSTI2026

Monitored Fluctuating Hydrodynamics

We introduce a hydrodynamic framework for describing monitored classical stochastic processes. We study the conditional ensembles for these monitored processes—i.e., we compute spacetime correlation functions conditioned on a fixed, typical measurement record. In the presence of global symmetries we show that these conditional ensembles can undergo measurement-induced “sharpening” phase transitions as a function of the monitoring rate; moreover, even weak monitoring can give rise to novel critical phases, derived entirely from a classical perspective. We give a simple hydrodynamic derivation of the known “charge-fuzzy phase” for weakly monitored diffusive many-body quantum systems. We show that although the unmonitored symmetric and asymmetric exclusion processes are in different universality classes of transport, the fluctuations in their conditional ensembles flow to the same fixed point with emergent relativistic invariance under monitoring. On the other hand, weakly monitored systems with non-Abelian symmetries enter a novel strongly coupled fixed point with nontrivial dynamical exponent, which we characterize. Our formalism naturally accounts for monitoring general observables, such as currents or density gradients, and allows for a direct calculation of information-theoretic diagnostics of sharpening transitions, including the Shannon entropy of the measurement record.

classical statistical mechanics↗
DOE OSTI2024

Defining Golden Batches in Biomanufacturing Processes From Internal Metabolic Activity to Detect Process Changes That May Affect Product Quality

ABSTRACT Cellular metabolism plays a role in the observed variability of a drug substance's Critical Quality Attributes (CQAs) made by biomanufacturing processes. Therefore, here we describe a new approach for monitoring biomanufacturing processes that measures a set of metabolic reaction rates (named Critical Metabolic Parameters (CMP) in addition to the macroscopic process conditions currently being used as Critical Process Parameters (CPP) for biomanufacturing. Constraint‐based systems biology models like Flux Balance Analysis (FBA) are used to estimate metabolic reaction rates, and metabolic rates are used as inputs for multivariate Batch Evolution Models (BEM). Metabolic activity was reproducible among batches and could be monitored to detect a deliberately induced macroscopic process shift (i.e., temperature change). The CMP approach has the potential to enable “golden batches” in biomanufacturing processes to be defined from the internal metabolic activity and to aid in detecting process changes that may impact the quality of the product. Overall, the data suggested that monitoring of metabolic activity has promise for biomanufacturing process control.

Biotechnology & Applied Microbiology↗
DOE OSTI2025

Characterization and prediction of the electromechanical wear of contact tips during wire arc additive manufacturing of 316L stainless steel

Here, this study seeks to better understand the degradation of the contact tip with respect to WAAM for a 316L wire electrode as well as explore methods of monitoring the contact tip state from process data. The contact tip, a consumable component, positions the wire and serves as the electrical contact surface between the wire electrode and the welding power supply. The wear of the contact tip was characterized in terms of material loss and material contamination for a set of tips worn to discrete levels as measured by the amount of wire fed or arc time. Geometrical characterization found a 49% increase in the bore exit area at 180 meters of wire fed. Machine learning models were developed to predict the relative bore exit area of the contact tip from arc-based process data and a random forest classifier exhibited favorable performance with a cross-validated f1-score of 0.84. The regression architecture implemented a multi-layer perceptron with the ability to predict the relative exit area with an $R^2$ score of 0.75. Key features used in the prediction include the standard deviation of the voltage and the time between shorts.

Contact tip wear↗
DOE OSTI2026

Review of Ultrasonic Methods for Monitoring, Damage Detection, and Processing of Lithium-Ion Batteries Throughout Their Life Cycle

Lithium-ion batteries (LIBs) are the leading technology used in consumer electronics, electric vehicles, and grid-level electrochemical energy storage applications. The ever-increasing use of LIBs has highlighted a gap in understanding of their behavior throughout their life cycle. Current monitoring systems rely on electrical and sometimes temperature measurements to assess the internal state which limits information about complex electrochemical processes. In response, ultrasonic testing (UT) has shown promise for non-invasive assessment due to its ease of use and sensitivity to mechanical changes which are correlated with electrochemical changes within the battery. We summarize the research in UT methods applied to LIBs throughout their life cycle. We also discuss physics-based and data-driven modeling approaches used to interpret ultrasonic signals in the context of LIBs, with an emphasis on the existing challenge of establishing rigorous links between electrochemical behavior and elastic and poroelastic wave physics to gain insight regarding physical changes in the LIB that can be directly measured using UT. Finally, we discuss the challenges of implementing UT across the LIB life cycle and identify opportunities for further research. This review aims to provide helpful guidance to researchers and practitioners of UT in the growing field of UT for electrochemical battery systems.

25 ENERGY STORAGE↗
DOE OSTI2025

rmon (Resource monitor) [SWR-24-128]

The resource monitor application provides monitoring, collection, and visualization of resource utilization in compute nodes. This package contains utilities to monitor system resource utilization (CPU, memory, disk, network). Here are the ways you can use it: -Monitor resource utilization for a compute node for a given set of resource types and process IDs. -Start a process and monitor its resource utilization. -Monitor resource utilization for a compute node asynchronously with the ability to dynamically change the resource types and process IDs being monitored. -Produce JSON reports of aggregated metrics. -Produce interactive HTML plots of the statistics.

Thom, Daniel [National Renewable Energy Laboratory↗
DOE OSTI2025

Permanent Source - Distributed Acoustic Sensing (PESO-DAS) v1

The SOV/DAS software program focuses on seismic monitoring and data processing of permanent seismic sources Surface Orbital Vibrators (SOVs) and fiber-optics sensing Distributed Acoustic Sensing (DAS). Its key features include automated data processing of the continuous seismic monitoring data acquired with DAS, and output of processed shot gathers and QC plots. The software supports timelapse seismic for long-term reservoir monitoring, and is used for monitoring of geological carbon storage sites, geothermal reservoirs, and oil and gas, and in general subsurface resource management.

dos Santos Maia Correa, Julia [Lawrence Berkeley N↗