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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Contaminant Investigation and Pre‐Processing Opportunities for Textile‐To‐Textile Recycling

Millions of metric tons of textiles are landfilled or incinerated each year in the United States, with less than 1% of textiles recycled into new clothing or fabrics. To counter this trend, a growing number of companies and researchers are exploring how a circular economy can be applied to support textile‐to‐textile recycling. A significant barrier they face comes down to quickly and efficiently extracting pure feedstock material from post‐consumer garments that feature a mix of natural and synthetic fibers. Textile recyclers prefer pure feedstocks, as working with mixed sources typically means lower throughput, higher risk of equipment failure, and diminished business margins. To facilitate a circular economy for textiles, methods, and technologies are needed that can efficiently separate out materials and contaminants from end‐of‐life textiles to increase the flow of pure feedstocks to recyclers. This paper summarizes findings from interviews with a cross section of textile recyclers and from a review of literature to define basic feedstock requirements. In addition to our qualitative research, we deconstruct a bale of post‐consumer textiles and analyze them using computer‐vision imaging, Fourier transform infrared spectroscopy (FTIR), and machine learning. The resulting data are used to set system‐level design inputs for an automated contaminant removal system to process post‐consumer clothing into appropriate feedstocks for recycling. To set the system's levels for automated real‐time near‐infrared analysis, we identify the minimum percentage of primary material that any single garment in a load of used clothing must contain for the average of the full output stream to meet the target purity levels of recyclers. Here, the envisioned automated system can also address undesirable trace materials that might contaminate the processed stream by using imaging cameras coupled with artificial intelligence to identify sections of clothing for de‐trimming. Proof‐of‐concept machine learning algorithms are evaluated to locate and identify trims or garment areas with hidden contaminant materials. Integrating these methods into automated textile cutting systems can provide a cost‐effective means for increasing feedstock purity from used clothing, which can advance circularity for textiles by helping recyclers to reach production volumes and quality targets that were not possible solely with manual dismantling operations.

Parsons, Ryan [Rochester Institute of Technology, ↗
DOE OSTI2025

High‐Concentration Alcohol Generation in Bipolar Membrane CO Electrolyzer

Electrochemical reduction of carbon dioxide and carbon monoxide offers an electricity‐powered route to make multicarbon liquid products. However, in conventional systems employing anion exchange membranes (AEMs), significant liquid product crossover leads to dilute product streams, increasing separation costs; and also produces unwanted anodic oxidation, further decreasing overall efficiency. Here, we report a forward‐biased bipolar membrane (FB‐BPM) system that achieves <10% liquid product crossover while sustaining a highly alkaline environment near the cathode, suppressing ethylene and hydrogen and favoring liquid products. By tuning catalyst composition to modulate the adsorption of *H and *OH, we steer selectivity toward acetate and alcohols. Using the FB‐BPM system, we achieve >25 wt% acetate on CuZn and >15 wt% alcohols on CuSn directly from the cathode outlet stream.

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

Integrated Ammonia Capture and Exchange on Ion‐Exchanged 4A Zeolites

Ammonia poses a challenge in effluent gas streams due to its corrosive nature. However, in fusion settings tritiated ammonia can be formed, leading to both tritium loss in inventory and the generation of reactive species. Therefore, identifying pathways in which both tritium can be recovered, and the ammonia can be easily handled would be beneficial. One such way to do so would be to combine two useful techniques: ammonia sequestration and hydrogen exchange (ND 3 →NH 3 ). However, materials that can both adsorb ammonia and subsequently perform reactions on ammonia have not been well explored. Here, in this work, we present the development of ion-exchanged A-type zeolites to be utilized as a support material for platinum catalysts. In this way, the zeolite can adsorb ammonia and the platinum catalyst can facilitate hydrogen exchange allowing for bifunctional reactivity of the material to be achieved. A variety of elements were explored for their effect on A-type zeolites and resulted in an isotopic difference in the adsorption of ND 3 and NH 3 , noting the use of deuterium as a surrogate for tritium. Several platinum-impregnated zeolites were able to remove ND 3 from the gas stream, indicating that utilizing these materials in isotope recovery processes would improve accountability of these valuable hydrogen isotopes.

Koch, Christopher J. [Savannah River National Labo↗
DOE OSTI2024

Reducing Ohmic Resistances in Membrane Capacitive Deionization Using Micropatterned Ion-Exchange Membranes, Ionomer Infiltrated Electrodes, and Ionomer-Coated Nylon Meshes

Membrane capacitive deionization (MCDI) is an emerging water desalination platform that is compact, electrified, and does not require high-pressure piping. Herein, highly conductive poly(phenylene alkylene) ion-exchange membranes (IEMs) are micropatterned with different surface geometries for MCDI. The micropatterned membranes increase the interfacial area with the liquid stream leading to a 700 mV reduction in cell voltage when operating at constant current (2 mA cm -2 ; 2000 ppm NaCl feed) while improving the energy normalized adsorbed salt (ENAS) value by 1.4 times. Combining the micropatterned poly(phenylene alkylene) IEMs with poly(phenylene alkylene) ionomer-filled electrodes reduces the cell voltage by 1000 mV and improves the ENAS values by 2.3 times relative to the base case. This reduction in cell voltage allows for higher current density operation (i.e., 3–4 mA cm -2 ) . The reduction in cell voltage is ascribed to the ameliorating ohmic resistances related to ion transport at the membrane-process stream interface and in the carbon cloth electrode. Finally, porous ionic conductors are implemented into the spacer channel with flat and micropatterned IEM configurations and ionomer infiltrated electrodes. For the configuration with flat IEMs, the porous ionic conductor improves ENAS values across the current density regime (2–4 mA cm -2 ), while for micropatterned IEMs it gets improved only at 4 mA cm -2 .

42 ENGINEERING↗
DOE OSTI2024

Out-of-Distribution Detection and Radiological Data Monitoring Using Statistical Process Control

Abstract Machine learning (ML) models often fail with data that deviates from their training distribution. This is a significant concern for ML-enabled devices as data drift may lead to unexpected performance. This work introduces a new framework for out of distribution (OOD) detection and data drift monitoring that combines ML and geometric methods with statistical process control (SPC). We investigated different design choices, including methods for extracting feature representations and drift quantification for OOD detection in individual images and as an approach for input data monitoring. We evaluated the framework for both identifying OOD images and demonstrating the ability to detect shifts in data streams over time. We demonstrated a proof-of-concept via the following tasks: 1) differentiating axial vs. non-axial CT images, 2) differentiating CXR vs. other radiographic imaging modalities, and 3) differentiating adult CXR vs. pediatric CXR. For the identification of individual OOD images, our framework achieved high sensitivity in detecting OOD inputs: 0.980 in CT, 0.984 in CXR, and 0.854 in pediatric CXR. Our framework is also adept at monitoring data streams and identifying the time a drift occurred. In our simulations tracking drift over time, it effectively detected a shift from CXR to non-CXR instantly, a transition from axial to non-axial CT within few days, and a drift from adult to pediatric CXRs within a day—all while maintaining a low false positive rate. Through additional experiments, we demonstrate the framework is modality-agnostic and independent from the underlying model structure, making it highly customizable for specific applications and broadly applicable across different imaging modalities and deployed ML models.

Zamzmi, Ghada↗
DOE OSTI2026

Deacetylation and Mechanical Refining Pathway for the Bioconversion of Sugarcane Bagasse

Advancing lignocellulose biorefining is imperative for the deployment of cellulosic (2G) biofuels. This work investigates the tailoring of the alkaline deacetylation and mechanical refining (DMR) pathway for the bioconversion of sugarcane bagasse. Experiments are conducted at laboratory and pilot scales, varying the pretreatment conditions (70–92 °C; 48–100 g NaOH /kg) and the mechanical refining technologies (PFI and disk refining). The pretreatments selectively solubilize acetyl groups (> 86%) and lignin (10–63%) while mostly preserving structural carbohydrates in the solid phase. Enzymatic hydrolysis generates hydrolysates of clean sugars (glucose and xylose), with sugar yields increasing up to 81% for glucose and 89% for xylose in response to delignification and mechanical refining. Biochemical methane potential assays reveal specific methane productions of up to 568 NmL CH₄ gVS⁻¹ for alkaline liquor monodigestion and 344 NmL CH₄ gVS⁻¹ for co-digestion with sugarcane vinasse from the conventional (1G) sugarcane ethanol, indicating a strong potential for bioenergy recovery from this process stream. Synergies are identified in integrating 1G ethanol, 2G DMR processing of bagasse, and anaerobic co-digestion of 1G vinasse and 2G DMR alkaline liquor. This technology enables sugarcane biorefineries to enhance the co-production of ethanol, methane, and concentrated streams of CO 2 .

09 BIOMASS FUELS↗
DOE OSTI2026

Microbial valorization of lignin to malic acid by Aspergillus niger

Lignin is the largest renewable source of aromatic carbon, yet its heterogeneity and recalcitrance limit its use in higher-value bioconversion processes. In this study, Aspergillus niger was engineered to enable the bioconversion of lignin-derived aromatics and base-catalyzed depolymerized (BCD) lignin streams into malic acid, a value-added C4 dicarboxylic acid with broad industrial relevance. Overexpression of the C4 dicarboxylate transporter C4T318 from Aspergillus oryzae enhanced malic acid secretion, while medium optimization under buffered conditions further improved the production. The engineered strain efficiently assimilated representative lignin-derived aromatics, including 4-hydroxybenzoic acid and p-coumaric acid, producing up to 3.9 g/L malic acid. Conversion of BCD lignin liquors from poplar and sorghum demonstrated effective utilization of heterogeneous aromatic mixtures, generating up to 0.82 g/L malic acid. This work demonstrates direct fungal conversion of real lignin streams into malic acid and establishes A. niger as a promising platform for sustainable lignin valorization.

Aromatic bioconversion↗
DOE OSTI2024

Selective phosphate removal with manganese oxide composite anion exchange membranes in membrane capacitive deionization

The discharge of excessive phosphorous into water bodies can lead to serious eutrophication threatening aquatic ecosystem. Membrane capacitive deionization (MCDI) is an effective platform for deionizing aqueous streams; however, conventional MCDI is unable to selectively remove targeted ions from a liquid mixture. Here, in this work, we fabricated manganese oxide composite anion exchange membranes (AEMs) for MCDI to enhance phosphate removal selectivity from sodium chloride-sodium dihydrogen phosphate (10:1 M ratio) aqueous mixtures. We systematically investigated several critical factors, such as constant current or voltage operation, applied voltage amount, process stream pH, and manganese oxide (Mn 2 O 3 ) content in the AEM, on phosphate removal efficiency and phosphate selectivity. A trade-off was observed between phosphate removal and selectivity when increasing the cell voltage. Under the best conditions, a MCDI unit with a 20 wt% Mn 2 O 3 composite AEM and a bipolar membrane facilitated high phosphate removal efficiency of ≥ 31.8 % and a phosphate over chloride selectivity of 1.1 while showing stability for at least 30 cycles. To help understand how Mn 2 O 3 composite AEM boosts phosphate selectivity, static electronic structure calculations were performed, and they revelated that hydrogen phosphate absorption on Mn 2 O 3 composite AEM was 314 kcal/mol more exothermic than that on pristine AEM while chloride adsorption on Mn 2 O 3 composite AEM was 2.2 kcal/mol less exothermic than that on a pristine AEM. Overall, this work presents an effective strategy for selectively removing phosphate from model wastewater solutions and the mechanistic understanding that governs ion selectivity in composite ion-exchange membranes used in MCDI.

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

Catalytic resonance theory for the kinetics of photon-promoted catalysis

The illumination of catalytic surfaces with a continuous or pulsed stream of photons dynamically modulates surface chemistry for faster rates, non-equilibrium conversion, or product selectivity control. To establish fundamental principles of dynamic photon-modulated catalysis, the photocatalytic conversion of a generic surface reaction was simulated using the kinetic Monte Carlo method to understand the kinetic implications of an independent stream of photons that promotes surface product desorption. The time-averaged photocatalytic rate at differential conditions for varying photon flux and temperatures indicated three kinetic regimes described by product thermal desorption control, surface reaction control, and an intermediate kinetic regime with a zero slope Arrhenius plot, consistent with a degree of rate control dominated by the photon arrival frequency (i.e., per-site photon flux). Here, the maximum photocatalytic rate occurred orders of magnitude above the Sabatier limit at the resonance frequency, identified as the photon arrival frequency matching the surface reaction rate constant.

Canavan, Jesse R. [University of Minnesota, Minnea↗
DOE OSTI2026

Local reduced-order modeling for electrostatic plasmas by physics-informed solution manifold decomposition

Despite advancements in high-performance computing and modern numerical algorithms, computational cost remains prohibitive for multi-query kinetic plasma simulations. Here, in this work, we develop data-driven reduced-order models (ROMs) for collisionless electrostatic plasma dynamics, based on the kinetic Vlasov-Poisson equation. Our ROM approach projects the equation onto a linear subspace defined by the proper orthogonal decomposition (POD) modes. We introduce an efficient tensorial method to update the nonlinear term using a precomputed third-order tensor. We capture multiscale behavior with a minimal number of POD modes by decomposing the solution manifold into multiple time windows and creating temporally local ROMs. We consider two strategies for decomposition: one based on the physical time and the other based on the electric field energy. Applied to the 1D1V Vlasov–Poisson simulations, that is, prescribed E-field, Landau damping, and two-stream instability, we demonstrate that our ROMs accurately capture the total energy of the system both for parametric and time extrapolation cases. The temporally local ROMs are more efficient and accurate than the single ROM. In addition, in the two-stream instability case, we show that the energy-windowing reduced-order model (EW-ROM) is more efficient and accurate than the time-windowing reduced-order model (TW-ROM). With the tensorial approach, EW-ROM solves the equation approximately 90 times faster than Eulerian simulations while maintaining a maximum relative error of 7.5% for the training data and 11% for the testing data.

Electrostatic plasmas↗
DOE OSTI2025

Commercial building HVAC demand flexibility with model predictive control: Field demonstration and literature insights

Model Predictive Control (MPC) for building Heating Ventilation and Air Conditioning (HVAC) systems is beginning to gain traction in the market, with a few controls companies incorporating it into their product offerings. However, it remains difficult to assess whether the energy cost savings are enough to justify the cost of MPC implementation for a particular building, given the limited number of reported demonstrations. For small commercial and residential buildings with relatively uniform systems, standardized approaches can help lower implementation costs. In contrast, for large buildings or district systems, the potential magnitude of cost savings could justify more customized solutions. Estimating the cost-effectiveness of MPC becomes more challenging for medium and large commercial buildings, where a one-size-fits-all solution may not be suitable, and the potential energy cost savings may be insufficient to justify a customized solution. To make MPC technology more appealing, incorporating additional value streams beyond energy efficiency alone can significantly increase its attractiveness. One such revenue stream is demand flexibility, in response to dynamic electricity prices, where MPC can leverage the thermal mass of the building to shift the load and support the grid. Building on an extensive literature review of MPC field studies focused on cost savings and demand flexibility, this paper presents the results of implementing MPC control in a large office building HVAC system in Berkeley, CA. Four different dynamic electricity price profiles were integrated into the MPC objective function to shift building demand while maintaining comfort, and field testing was performed with each price profile across four seasons. The results show potential for 40–65 % demand decrease percentage and up to 61 % annual cost savings compared to the existing rule-based control strategy, under the tested dynamic price scenarios. This paper also presents a sensitivity analysis on the cost savings with respect to the price profile variability, discusses the implementation effort for the price-responsive MPC, and compares the cost savings found in this study to those found in literature on the basis of dynamic price variability, or so-called Electricity Price Relative Standard Deviation.

Zanetti, Ettore↗
DOE OSTI2024

Hydrogen underground storage for grid electricity storage: An optimization study on techno-economic analysis

Here, this study performs a techno-economic analysis of hydrogen underground storage systems for grid electricity storage, evaluating their economic viability at the plant scale using dynamic optimization. It explores the feasibility of various system configurations and revenue models in the context of volatile electricity prices and the necessity for multiple revenue streams. The hypothesis tested is that large-scale hydrogen storage, despite its low round-trip efficiency, can be economically viable with the right mix of revenue streams. This study uses scenario-based analysis to assess the impacts of different system configurations, including engaging in time-shifting arbitrage, ancillary service markets and blending hydrogen with natural gas. Results indicate potential annual net cash flows of up to $\$$1.5 million from ancillary services integration and $\$$5.2 million from natural gas blending, contingent on specific system sizes. The study concludes that hydrogen underground storage for grid electricity storage can be profitable, and emphasizes that proper system design and precise electricity price forecasting are crucial for optimizing system performance and economic returns. This research sets the stage for further investigations into the scalability of hydrogen storage systems and their broader implications for grid electricity storage and energy market dynamics.

25 ENERGY STORAGE↗
DOE OSTI2025

Monitoring river flow status using low-cost wildlife camera and image segmentation artificial intelligence

Continuous measurement and monitoring of surface water coverage in non-perennial streams are essential for understanding the exchange fluxes between surface and subsurface waters under both inundated and non-inundated conditions. In this study, a wildlife camera photo-based framework was developed to monitor small stream water inundation, depth, discharge, and velocity. Two advanced machine learning models, YOLOv8 and Mask2Former, were utilized to efficiently analyze images captured by wildlife cameras. The accuracy of the framework was validated against on-site depth measurements at six sites in the Yakima River Basin, along with the gage height, discharge, and velocity data from four USGS sites. This approach facilitates long-term, continuous monitoring and quantification of river intermittency and water availability with high precision and low cost, thereby advancing river ecosystem research and management.

machine learning↗
DOE OSTI2024

Isotope exchange of ND 3 on Pt catalyst-loaded 13X molecular sieve

During D-T fusion operations the capture, purification, and recycling of unburned tritium will be crucial, as the formation of tritium containing molecules require additional processing. Here, removing the tritium can require costly processing to be unbound from the tritium-containing molecules and improvements to these processes will be necessary moving forward. Existing techniques using sorbent material beds to remove tritium-containing molecules from process gas streams undergo repeated high-heat cycling which leads to diminished bed lifespans, necessitating replacement and associated downtime. This work demonstrates the capture and isotopic exchange technique of deuterated ammonia (ND3), used as a surrogate for tritium, at ambient temperature using a Pt catalyst-loaded 13X molecular sieve. Unmodified 13X molecular sieve is capable of adsorbing and retaining the ND3 however, incorporation of a catalyst facilitates the isotopic exchange of the hydrogen isotopes. The effluent gas streams were analyzed in conjunction with desorbed ammonia isotopologues post-exchange to verify these results. Isotopically exchanging and removing heavier hydrogen isotopes using this technique provides an alternative to traditional removal methods.

08 HYDROGEN↗
DOE OSTI2024

One-sweep moment-based semi-implicit-explicit integration for gray thermal radiation transport

Thermal radiation transport (TRT) is a time dependent, high dimensional partial integro-differential equation. In practical applications such as inertial confinement fusion, TRT is coupled to other physics such as hydrodynamics, plasmas, etc., and the timescales one is interested in capturing are often much slower than the radiation timescale. As a result, TRT is treated implicitly, and due to its stiffness and high dimensionality, is often a dominant computational cost in multiphysics simulations. Here we develop a new approach for implicit-explicit (IMEX) integration of gray TRT in the deterministic SN setting, which requires only one sweep per stage, with the simplest first-order method requiring only one sweep per time step. The partitioning of equations is done via a moment-based high-order low-order formulation of TRT, where the streaming operator and first two moments are used to capture the asymptotic stiff regimes of the streaming limit and diffusion limit. Absorption-reemission is treated explicitly, and although stiff, is sufficiently damped by the implicit solve that we achieve stable accurate time integration without incorporating the coupling of the high order and low order equations implicitly. Due to nonlinear coupling of the high-order and low-order equations through temperature-dependent opacities, to facilitate IMEX partitioning and higher-order methods, we use a semi-implicit integration approach amenable to nonlinear partitions. In conclusion, results are demonstrated on thick Marshak and crooked pipe benchmark problems, demonstrating orders of magnitude improvement in accuracy and wallclock compared with the standard first-order implicit integration typically used.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Impact of mixed grain size sediment input on braided river Morphodynamics: Insights from experimental and numerical modeling

The rate of sediment supply has significant impacts on river morphology, making it crucial to understand the geomorphic changes and grain size distribution dynamics in rivers. However, the effects of varying grain size sediment input on morphological changes in braided channels remain poorly understood. This study is the first to investigate the bar development and sediment sorting processes in braided channels with non-uniform sediment inputs using both numerical and experimental approaches. We applied a two-dimensional numerical model, Nays2DH to confirm and generalize experimental results. The model reproduced key experiment results, including 1) stream elevation changes, and 2) grain size distribution. Using this validated model, we explored the morphological changes and sorting process in a braided river with sediment inputs. The numerical experiments demonstrate that sediment input controls the elevation of the stream bed and the grain size distribution. Notably, both the elevation and grain-size distribution become relatively stable in downstream of the channel. Additionally, the simulation results suggest that an increased sediment supply leads to greater channel complexity, with bed surface armoring decreasing.

Nays2DH↗
DOE OSTI2026

A systematic analytical framework for multi-source municipal solid waste characterization for energy recovery

Advancing municipal solid waste (MSW) management from disposal-oriented practices toward circular, value-driven systems requires standardized methodologies capable of identifying material composition and resource recoverable potential at the point of generation. Despite extensive research, MSW characterization remains fragmented due to inconsistences in sampling methodologies, waste sorting categories, and temporal coverage across previous studies which limit cross-site comparability, reproducibility, and constrain the reliable evaluation of potential resource recovery pathways. This lack of consistency has hindered the development of a unified framework for MSW characterization and resource assessment. This study introduces a standardized, field-validated protocol for MSW sampling and composition analysis that ensures consistent, traceable data across diverse waste sources. The protocol integrates randomized spatial sampling, systematic material sorting, and controlled subsampling for multi-site and multi-season field campaigns. Validation included MSW collection from residential, grocery, restaurant, and school MSW streams across five U.S. states, including Maryland, Idaho, Virginia, Ohio, and Mississippi, to demonstrate the protocol’s ability to identify source-based composition patterns relevant to resource recovery applications. Grocery and restaurant streams were dominated by food waste and high-moisture organics, while school waste contained higher paper content and residential waste showed greater heterogeneity. Aggregation into energy-relevant fractions highlighted practical recovery pathways via anaerobic digestion or gasification, supporting data-driven planning, policy, and circular economy strategies for sustainable waste management across waste sources.

09 BIOMASS FUELS↗
DOE OSTI2025

Compressibility and permeability of particulated non-recyclable municipal solid waste

Biofuels from non-recyclable municipal solid waste (NMSW) stand at the forefront of energy sustainability. However, their widespread adoption is hampered by persistent material handling issues stemming from the variability in NMSW material properties. An enhanced understanding of particulated NMSW properties, particularly compressibility and permeability, is essential to address the feedstock handling challenges and optimize the handling equipment. This study measures the compressibility and gas permeability of five streams of NMSW materials (i.e., rigid plastics, cardboard, thin film, paper, and foam) and their mixtures under different stress conditions. The results highlight the significant variability in compressibility and gas permeability among different streams, as well as the impacts of particle sizes. A semi-empirical model capable of predicting the gas permeability of NMSW mixtures is established and validated. Here, the results also highlight that reduced NMSW particle size helps promote consistency in NMSW feedstock’s physical and mechanical properties, which is favored for handling equipment design in waste-to-energy recovery facilities.

09 BIOMASS FUELS↗