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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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DOE OSTI2025

A Life Cycle Analysis Framework for Point Source Capture Systems

NETL studies the costs and benefits of PSC for electricity, industry, and mobile applications. Mobile point source capture (MPSC) and storage applied to freight modes captures emissions directly from exhaust. This poster presents a framework for conducting LCA of PSC systems applied to heavy-duty trucks, freight trains, and marine vessels. The framework defines a wheels-to-storage (gate-to-grave) boundary, including energy demands (electricity, heat, and cooling requirements), solvent use and cycling, onboard system components, carbon storage in a saline aquifer, and upstream manufacturing impacts for equipment, with a suggested functional unit of 1 tonne-km. Potential data sources for analysis include material, energy, and operational data from Oak Ridge National Laboratory, GREET (Greenhouse gases, Regulated Emissions, and Energy use in Technologies) model, and scientific literature. The suggested analytical approach includes comparison to publicly available business-as-usual systems without capture across all modes of transportation, sensitivity to composition of the capture solvent, and sensitivity to capture rate variation, all of which would support a wholistic PSC business case analysis. For future consideration, analysis can be augmented with consideration of different sources of electricity (e.g., nuclear), fuel substitution, deploying supportive infrastructure such as pipeline offloading points, and downstream applications like enhanced oil recovery (EOR).

life cycle analysis (LCA)↗

DOE OSTI2020

A Comprehensive Approach to Measure the Efficiency of Freight Transport: Freight Mobility Energy Productivity (F-MEP) Metric

Freight travel accounts for a major share of the energy consumed in the transportation sector in any country, and the United States is no exception. Understanding and modeling freight movement is critical, particularly in the context of capturing the impact of emerging technologies on freight travel and its externalities. The domain of freight modeling and forecasting is gaining pace in the recent years, but advancement in comprehensive freight performance metrics is still lagging. Conventional freight performance metrics such as truck-miles, ton-miles, or value-miles are unidimensional and aggregated in nature, making them unsuitable to accurately capture the impact of emerging transportation trends on the performance of freight systems. Addressing this research need, this paper presents the 'Freight Mobility Energy Productivity' metric to quantify freight performance of current as well as future freight systems, accounting for various costs associated with freight transport. The proposed metric was implemented using data from the Freight Analysis Framework along with other published sources, which shows intuitive results in quantifying freight performance. Further, a scenario analysis exercise was conducted to test the capability of the metric in tracking improvements in system-level freight efficiency as a result of vehicle powertrain technology improvement. Results of the scenario analysis reinforce confidence that the proposed metric can be used as a decision support tool in assessing the efficiency of existing as well as future freight trends and technologies.

25 ENERGY STORAGE↗

DOE OSTI2020

Comprehensive Approach to Measure the Mobility Energy Productivity of Freight Transport

Freight travel accounts for a major share of the energy consumed in the transportation sector in any country, and the United States is no exception. Understanding and modeling freight movement are critical, particularly in the context of capturing the impact of emerging technologies on freight travel and its externalities. The domain of freight modeling and forecasting has been gaining pace in recent years, but advancement in comprehensive freight performance metrics is still lagging. Conventional freight performance metrics such as truck-miles, ton-miles, or value-miles are unidimensional and aggregate in nature, making them unsuitable to accurately capture the impact of emerging transportation trends on the performance or productivity of freight systems. Addressing the research need, this paper presents the “Freight Mobility Energy Productivity” metric to quantify freight productivity of current as well as future freight systems, accounting for various costs associated with freight transport. The proposed metric was implemented using data from the Freight Analysis Framework along with other published sources, and shows intuitive results in quantifying freight productivity. Further, a scenario analysis exercise was conducted to test the capability of the metric in tracking improvements in system-level freight productivity as a result of vehicle electrification. The relative differences in Freight Mobility Energy Productivity scores help identify which zones benefit from the vehicle powertrain technology improvement. The results of the scenario analysis reinforce confidence that the proposed metric can be used as a decision support tool in assessing the productivity of existing as well as future freight trends and technologies.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

A Comprehensive Approach to Measure the Mobility Energy Productivity of Freight Transport: Preprint

Freight travel accounts for a major share of the energy consumed in the transportation sector in any country, and the United States is no exception. Understanding and modeling freight movement are critical, particularly in the context of capturing the impact of emerging technologies on freight travel and its externalities. The domain of freight modeling and forecasting is gaining pace in the recent years, but advancement in comprehensive freight performance metrics is still lagging. Conventional freight performance metrics such as truck-miles, ton-miles, or value-miles are unidimensional and aggregate in nature, making them unsuitable to accurately capture the impact of emerging transportation trends on the performance or productivity of freight systems. Addressing the research need, this paper presents the “Freight Mobility Energy Productivity” metric to quantify freight productivity of current as well as future freight systems, accounting for various costs associated with freight transport. The proposed metric was implemented using data from the Freight Analysis Framework along with other published sources, which shows intuitive results in quantifying freight productivity. Further, a scenario analysis exercise was conducted to test the capability of the metric in tracking improvements in system-level freight productivity as a result of vehicle electrification. The relative differences in Freight Mobility Energy Productivity scores help identify which zones benefit from the vehicle powertrain technology improvement. The results of the scenario analysis reinforce confidence that the proposed metric can be used as a decision support tool in assessing the productivity of existing as well as future freight trends and technologies.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

Ultra-selective membrane composed of charge-stabilized fixed carrier and amino acid-based ionic liquid mobile carrier for highly efficient carbon capture

Membrane technology has been extensively studied for CO 2 capture applications, especially for flue gas sources. In the past decades, facilitated transport membranes (FTMs) have made breakthroughs overcoming the permeance-selectivity trade-off upper bound restricting traditional CO 2 separation membranes, but are still facing challenges towards practical applications, including limited performance, such as insufficient CO 2 /N 2 selectivity to achieve 95 % CO 2 dry-base purity by one step separation, and long-term stability. Herein, we designed and fabricated a novel FTM structure containing an ionic liquid (1-ethyl-3-methylimidazolium aminoacetate, [Emim][Gly]) as mobile CO 2 -carrier and a polymeric amine (polyethyleneimine, PEI) as fixed CO 2 -carrier. In this study, the fixed carrier is confined within a carbon nanotube (CNT) framework of 230 nm thickness via electrostatic forces adjusted by a polyelectrolyte (polystyrene sulfonate, PSS), while the mobile carrier diffuses freely within the CNT framework. After optimization of the membrane recipe and spray-coating fabrication procedure, following our previous work, the resulting CNT-PSS-PEI ~ IL membranes demonstrated an ultra-high CO 2 /N 2 selectivity up to 1,000 with CO 2 permeance up to 2,400 GPU (Gas Permeation Unit, 1 GPU = 3.348 × 10 -10 mol·s -1 ·m -2 ·Pa -1 ) under vacuum operation condition. Furthermore, one 100-cm 2 flat sheet membrane sample was prepared and exhibited one-stage CO 2 enrichment from 15 % to 95 % purity (dry-base) for the first time amongst all reported CO 2 separation membranes. The membrane retained a stable performance over 50-h operation period under vacuum condition. The extraordinary CO 2 separation performance illustrates the great potential of the CNT-PSS-PEI ~ IL membranes for flue gas carbon capture application.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Strategies for community-sourced biocuration in bioinformatics: a case study on MIBiG 4.0

Biocuration is essential to transform molecular sequence data into standardized, machine-readable resources. Such curated datasets enable comparative analysis, predictive modeling, and data integration across bioinformatics platforms. While professional biocuration is resource-intensive and usually limited to institutional settings, community-driven approaches can mobilize large-scale annotation of specialized datasets and are more resilient to disruptions in scientific funding. Here, we present a model for community-powered curation applied to the Minimum Information about a Biosynthetic Gene Cluster (MIBiG) repository. Through a framework of workflows for metadata capture, annotation validation, and contributor coordination, the MIBiG 4.0 initiative recruited 267 scientists across 178 institutions from 33 countries, volunteering an estimated 4000 h of work. These efforts expanded the MIBiG repository by 22% and enhanced its usability in downstream molecular data analyses in comparative genomic analyses, natural product discovery, and machine learning applications. We provide strategies and actionable lessons for adopting this model, supporting the sustainability of curated bioinformatics resources central to nucleic acid research and related fields.

biocuration↗

DOE OSTI2021

Understanding collective human movement dynamics during large-scale events using big geosocial data analytics

Conventional approaches for modeling human mobility pattern often focus on human activity and movement dynamics in their regular daily lives and cannot capture changes in human movement dynamics in response to large-scale events. With the rapid advancement of information and communication technologies, many researchers have adopted alternative data sources (e.g., cell phone records, GPS trajectory data) from private data vendors to study human movement dynamics in response to large-scale natural or societal events. Big geosocial data such as georeferenced tweets are publicly available and dynamically evolving as real-world events are happening, making it more likely to capture the real-time sentiments and responses of populations. However, precisely-geolocated geosocial data is scarce and biased toward urban population centers. In this research, we developed a big geosocial data analytical framework for extracting human movement dynamics in response to large-scale events from publicly available georeferenced tweets. The framework includes a two-stage data collection module that collects data in a more targeted fashion in order to mitigate the data scarcity issue of georeferenced tweets; in addition, a variable bandwidth kernel density estimation(VB-KDE) approach was adopted to fuse georeference information at different spatial scales, further augmenting the signals of human movement dynamics contained in georeferenced tweets. To correct for the sampling bias of georeferenced tweets, we adjusted the number of tweets for different spatial units (e.g., county, state) by population. To demonstrate the performance of the proposed analytic framework, we chose an astronomical event that occurred nationwide across the United States, i.e., the 2017 Great American Eclipse, as an example event and studied the human movement dynamics in response to this event. Finally, this analytic framework can easily be applied to other types of large-scale events such as hurricanes or earthquakes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Infrastructure-Based Cooperative Perception at a Traffic Intersection: Overview and Challenges: Preprint

Recent advancement in autonomous driving vehicles and V2X communication has attracted increasing attention towards Intelligent Transportation Systems to build a safe and reliable traffic intersection. However, most of the systems are still at the initial stages and require significant progress to become a reality. This paper presents an overview of NREL Infrastructure Perception and Control (IPC) framework which is an open-source track-data fusion engine which takes input from infrastructure-based perception sensors and cooperatively shared messages from Connected Autonomous Vehicles (CAVs) and Connected Vehicle (CVs) and the challenges associated with deploying such cooperative perception framework at a four-way traffic intersection in the city of Colorado Springs, CO, USA. The sensor data is collected by deploying two radars and two LiDAR sensors on the IPC mobile lab and two radars on diagonally opposite traffic poles at the proposed intersection. The sensor output results imply the need for rapid sensor calibration to bring the collective perception to a common coordinate frame, the importance of time synchronization between the sensors in order to capture accurate spatial and temporal alignment of the objects, and the need for a health monitoring system with fail safe closed-loop detection model for real-time deployment.

camera↗

DOE OSTI2021

Transportation Hub Infrastructure Expansion: Decision Support Under Uncertainty

The Athena project (www.athena-mobility.org) has worked to investigate the relationship between the Dallas-Fort Worth Airport (DFW) and the greater Dallas area in order to better understand and therefore better inform future decision-making regarding the critical infrastructure that influence mobility between the airport and the city. Through this work, infrastructure related to curbside pickup and drop-off, parking, public transit, and the road network congestion were identified as critical to the operation of the DFW transportation hub. The infrastructure analysis and expansion aspect of the Athena project is focused on the restructuring of the CTA curb as a hierarchical curb and the building or repurposing of parking infrastructure as the interplay between these two areas. Many sources of uncertainty exist that may impact future airport and transportation hub operations, such as passenger volume growth, population demographic changes over time, electric vehicle (EV) adoption rates, and autonomous vehicle (AV) adoption rates. Due to these sources of uncertainty, we have selected for our research a modeling framework that can capture various types of uncertainty and hedge against those uncertainties in the optimization process. We analyze road network and curb congestion, the rise of transportation networking companies, trends in parking usage, existing policies around this infrastructure, airport revenue streams, and other contributing factors to enable infrastructure decision making with less uncertainty. To accomplish this wholistic analysis, we have developed a novel multi-stage, multi-period stochastic optimization model which considers the airport's decisions from 2025-2045 under different possible future macro trajectories and day-to-day variations in operational conditions captured as "annual representation of operations" scenarios with respective probabilities. This model has also been designed to leverage the outputs of various efforts under the Athena project to create a combined decision framework for infrastructure decisions. These various efforts include the route optimization model, the ASPIRES simulation, the mode choice model, and the SUMO traffic simulation. Our computational experiments of this system at scale have resulted in a working version of our infrastructure model which enables the explicit representation and consideration of various sources of uncertainty in the decision process to enable robust, flexible decision-making. This model has been effectively run on NREL's HPC system, Eagle, with large numbers of stochastic scenarios and shows promise as a scalable tool for robust consideration of uncertainties in airport planning. We have tested our model using 30,240 operational circumstances in total, resulting in a problem with more 200 million variables. This model was solved in several different configurations, and a workflow to simulate the performance of the infrastructure model results was developed and deployed. In general, our results indicate that a combination of remote parking, remote curb infrastructure, and dynamic pricing can generate revenue, reduce emissions, accommodate emerging technologies such as AVs and EVs, and manage airport passenger growth over time. We note the success of the proposed strategy depends on the data collection and forecasting abilities of DFW. We have also seen that the AV adoption by TNCs might necessitate larger amounts of remote curb. The results of this work inform strategies for airport infrastructure decision making, as well as demonstrate the value of an adaptable model, but also indicate that there are avenues remaining where further research would be of value.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2019

Charge Movement and Structural Changes in the Gas-Phase Unfolding of Multimeric Protein Complexes Captured by Native Top-Down Mass Spectrometry

The extent to which noncovalent protein complexes retain native structure in the gas phase is highly dependent on experimental conditions. Energetic collisions with background gas can cause structural changes ranging from unfolding to subunit dissociation. Additionally, recent studies have highlighted the role of charge in such structural changes, but the mechanism is not completely understood. In this study, native top down (native TD) mass spectrometry was used to probe gas-phase structural changes of alcohol dehydrogenase (ADH, 4mer) under varying degrees of in-source activation. Changes in covalent backbone fragments produced by electron capture dissociation (ECD) or 193 nm ultraviolet photodissociation (UVPD) were correlated to structural changes of the ADH 4mer. ECD fragments indicated unfolding started at the N-terminus, and the charge states of UVPD fragments enabled monitoring of charge migration to the unfolded regions. Interestingly, UVPD fragments also indicated that the charge at the “unfolding” N-terminus of ADH decreased at high in-source activation energies after the initial increase. We proposed a possible “refolding-after-unfolding” mechanism, as further supported by monitoring hydrogen elimination from radical a-ions produced by UVPD at the N-terminus of ADH. However, “refolding-after-unfolding” with increasing in-source activation was not observed for charge-reduced ADH, which likely adopted compact structures that are resistant to both charge migration and unfolding. Combined, these results support a charge-directed unfolding mechanism for protein complexes. Overall, an experimental framework was outlined for utilizing native TD to generate structure-informative mass spectral signatures for protein complexes that complement other structure characterization techniques, such as ion mobility spectrometry and computational modeling.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

How Proton Incorporation Reshapes Lattice Dynamics In BaSnO 3 ‐Type Proton Conductors

Proton conduction in acceptor-doped perovskites is fundamentally a vibronic process: mobile H + and D + do not move independently, but dynamically co-vibrate with the surrounding oxygen–metal framework. Direct experimental evidence for this behavior is presented using in situ 119⁢ 𝑆⁢𝑛 nuclear resonance vibrational spectroscopy (NRVS) on hydrated, deuterated, and dry 𝐵⁢𝑎⁢𝑆⁢𝑛 1−𝑥⁢ 𝑌 𝑥 ⁢𝑂 3−𝛿 . Hydration induces systematic redistributions in the Sn-projected phonon density of states (PDOS), including an upshift of the first spectral moment by about 0.4 meV, indicating a stiffening of the extended Sn–O network. H/D isotopic substitution leaves the Sn-projected PDOS largely unchanged, with only subtle isotope-dependent spectral reweighting, demonstrating that protonic degrees of freedom are not localized oscillators but are embedded in collective lattice modes. These results are rationalized using a classical coupled proton–phonon oscillator model that links the observed PDOS variations to changes in effective force constants and vibrational mass terms. The model captures how H + and D + participate in cooperative lattice dynamics rather than forming isolated OH/OD entities. Overall, NRVS probes proton–lattice coupling in ceramic proton conductors and quantitatively describes how protonic defects modulate host lattice dynamics to enable phonon-assisted long-range proton transport.

36 MATERIALS SCIENCE↗