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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Consequence analyses of sabotage-induced radiological releases in high-temperature helium-cooled prismatic microreactors

Here, this study analyzes the radiological dose consequences of sabotage-induced accidents at three high-temperature helium-cooled prismatic microreactors (HTPMs) with thermal power ratings of 1, 10, and 50 MWt. Each HTPM employs uranium oxycarbide tristructural isotropic fuel enriched to 19.75 wt% high-assay low-enriched uranium. Simulations were conducted to estimate reactor core inventory at the point of fuel discharge––when the effective multiplication factor reduced to less than 1––representing peak radionuclide inventory. Postulated sabotage scenarios leading to reactor shutdown were analyzed at two intervals: immediately post-shutdown (0 h) and 3 days after shutdown using the SCALE code for radionuclide inventories and the RASCAL tool for dose consequences. Results show that although HTPMs benefit from inherent safety features and robust fuel design, radiological consequences scale with reactor power because of increased source term inventories. Smaller microreactors exhibited proportionally lower dose consequences. To support the economic and regulatory feasibility of microreactor deployment, this study emphasizes the value of a risk-informed, performance-based approach, as supported by regulations like 10 CFR Parts 100 and 53 in the United States. Microreactor developers should perform site-specific assessments of potential sabotage or low-probability, high-consequence events, especially when considering minimal on-site or full off-site emergency response.

Consequence↗
DOE OSTI2024

A Case Study in Assessing a Potential Severity Framework for Incidents from a Decadal Sample

In this study, the primary objective of this case study is to determine the applicability and feasibility of a framework that leverages occupational incident details to prospectively identify “potential Serious Injury or Fatality” (pSIF) cases. This study comprehensively reviewed a random sample of 1,081 injury and illness cases across 21 generalized incident types spanning over a decade at Lawrence Livermore National Laboratory (LLNL), a U.S. Department of Energy research and development facility with more than 9,000 employees. The review applied a general framework that classified each case on information suitability, potential severity, and future incident mitigation. The findings from the study indicate that 86.6% of the cases had sufficient information to make a high-confidence determination on potential severity, underscoring the feasibility of applying this general framework. Additionally, cases with a higher pSIF score had, on average, a higher level of institutional response. Implementing a simplified methodology for incident classification that emphasizes incidents that pose high potential severity, regardless of incident type, can help LLNL prioritize resources and tailor responses to such incidents using a graded approach. LLNL has recognized the value of this capability and is integrating the framework into their injury and illness process in the 2024 calendar year.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2021

GEOPHIRES files for DDU techno-economic simulations

During 2017-2019, the U.S. Department of Energy funded six geothermal deep direct-use (DDU) projects to investigate feasibility of DDU for heating, cooling and thermal storage in the United States. In a follow-on study conducted at the National Renewable Energy Laboratory (NREL), findings of these six projects were reviewed and analyzed, and additional simulations were conducted using the simulator GEOPHIRES to explore technical performance and cost-competitiveness of DDU. The results of the NREL study were published in the paper "Evaluating the Feasibility of Geothermal Deep Direct-Use in the United States." The GEOPHIRES files developed in that study are included in this submission. The reference for the paper under review is below: Beckers KF, Kolker A, Pauling H, McTigue JD, and Kesseli D (2021) ?Evaluating the Feasibility of Geothermal Deep Direct-Use in the United States?, Submitted to Energy Conversion and Management, Under Review.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Techno-economic feasibility of borehole thermal energy storage system connected to geothermal heat pumps for seasonal heating load of two buildings in Fairbanks, Alaska

Borehole thermal energy storage (BTES) is an effective solution for managing imbalanced heating and cooling loads in cold regions. This study evaluated the long-term feasibility of a BTES system in the Fairbanks area, Alaska, through building energy modeling, resource characterization, and numerical modeling. The system was designed to store waste heat from a nearby coal power plant during summer and provide thermal energy during winter to geothermal heat pumps supplying heating loads in two buildings. Heating load profiles were modeled for the buildings using EnergyPlus, and the results indicated the annual heating load was 5.6 times greater than the cooling load. 40 borehole heat exchangers were pre-designed approximately 100 m away from the two buildings in terms of land availability and regulatorily optimized depth. The 20-year performance of the designed BTES system under two operational scenarios—one with a 5-year preheating period and one without—was numerically modeled using subsurface temperature and properties characterized through the literature review and thermal response tests. Both scenarios demonstrated that the BTES has the capacity to fully cover the heating loads in the two buildings throughout the 20-year lifetime. Production temperatures at central wells were 33 % higher on average than at outer wells in both scenarios. The 5-year preheating period increased subsurface and extraction temperatures, and correspondingly annual average and total thermal energy production was higher for 8 years than in the scenario without the preheating period. These results highlight the long-term reliability and sustainability of the BTES system in meeting heating demands over its lifetime, with the preheating period offering potential performance improvements. Implementing the BTES system in cold regions with high heating demand, such as Fairbanks, Alaska, could provide a long-term, sustainable energy solution for managing imbalanced heating and cooling loads.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Thermophilic biocatalysts for one-step conversion of citrus waste into lactic acid

Agri-food residues offer significant potential as a raw material for the production of L-lactic acid through microbial fermentation. Weizmannia coagulans, previously known as Bacillus coagulans, is a spore-forming, lactic acid-producing, gram-positive, with known probiotic and prebiotic properties. This study aimed to evaluate the feasibility of utilizing untreated citrus waste as a sustainable feedstock for the production of L-lactic acid in a one-step process, by using the strain W. coagulans MA-13. By employing a thermophilic enzymatic cocktail (Cellic CTec2) in conjunction with the hydrolytic capabilities of MA-13, biomass degradation was enhanced by up to 62%. Moreover, batch and fed-batch fermentation experiments demonstrated the complete fermentation of glucose into L-lactic acid, achieving a concentration of up to 44.8 g/L. These results point to MA-13 as a microbial cell factory for one-step production of L-lactic acid, by combining cost-effective saccharification with MA-13 fermentative performance, on agri-food wastes. Moreover, the potential of this approach for sustainable valorization of agricultural waste streams is successfully proven.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Towards an Integrated Process for CO 2 Capture and Utilization: Cultivation of Scenedesmus acutus Using Gaseous CO 2 and NH 3

Integrating CO 2 scrubbing from flue gas with its utilization in algae cultivation represents a potential means of lowering the cost of CO 2 capture. Towards this goal, this study sought to assess the feasibility of using gaseous a CO 2 /NH 3 stream, derived from CO 2 capture using aqueous ammonia, as a C- and N-source for algae cultivation. Scenedesmus acutus was cultured in 800 mL photobioreactors using gaseous CO 2 /NH 3 in mole ratios varying from 7 to 18. Excellent growth of Scenedesmus acutus was observed, the average growth rate for CO 2 /NH 3 = 10 of 0.171 ± 0.015 g/L·day exceeding that obtained using 1% CO 2 /N 2 and urea as the N-source (0.099 ± 0.28 g/L·day). Under optimal growth conditions (CO 2 /NH 3 mole ratio of 10), CO 2 utilization ranged from 57% to 72%, while the NH 3 utilization was >90%. The CO 2 /NH 3 feed rate was also found to exert a significant effect on algae productivity, excessive feed rates leading to accumulation of NH 3 in the culture at concentrations that were toxic to the algae. Consequently, to avoid the toxic effects of high NH 3 concentrations (>2.0 mM), it proved necessary to balance the NH 3 supply with the algae growth rate so that excessive NH 3 accumulation was prevented. This indicates that for practical applications, a CO 2 /NH 3 feed control strategy would be required that takes into account the ammonium ion concentration in solution and the pH so as to avoid significant concentrations of free NH 3 . Furthermore, analysis of the harvested biomass revealed a high protein (≥ 47 wt%) and a low ash content (< 3.6 wt%), suggesting it would be well suited for use as animal feed or as a feedstock for the production of bioplastics.

03 NATURAL GAS↗
DOE OSTI2025

Techno-economic implications and cost of forecasting errors in solar PV power production using optimized deep learning models

Accurate solar Photovoltaic (PV) power forecasting is important for enhancing both the performance and economic feasibility of PV systems. This study evaluates several deep learning models, including Dense Neural Networks (DNN), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and a hybrid LSTMCNN model, for predicting PV power production one day in advance. Prior to optimization, the models exhibited relatively high errors, with the best model (DNN) achieving a Root Mean Square Error (RMSE) of 31.13 kW and a coefficient of determination (R 2 ) of 62.15 %. After employing Bayesian optimization, the LSTM-CNN model demonstrated the best performance, with the RMSE reduced to 9.79 kW and R 2 improved to 97.62 %, showcasing significant enhancement in predictive accuracy. Here, the economic evaluation considered three cases: rewards for underestimation (0.08 USD/kWh), no rewards, and penalties for both over-and underestimation (120 % of the utility tariff). In the rewards scenario, the LSTM-CNN model reduced the Levelized Cost of Electricity (LCOE) by 4 %, while in the penalty scenario, a backup diesel generator would have increased the LCOE by 49 %. Additionally, the LSTM-CNN model minimized financial losses, achieving the lowest penalties and maximizing net cash flow compared to other models, demonstrating its overall technical and economic superiority.

Deep learning↗
DOE OSTI2025

Comparative performance assessment of air-source and ground-source heat pumps using CO₂ and R-410A with water well integration: A simulation study

This study investigates the performance and feasibility of heat pump systems for residential space heating in cold climates, with a particular focus on ground source heat pumps (GSHPs) with water wells. Four configurations are modeled and compared, a CO 2 air-source heat pump (ASHP), an R-410A ASHP, a CO 2 GSHP with water well integration, and an R-410A ground-source heat pump with water well integration. System simulations are conducted at both equipment and whole-building levels, followed by a nationwide analysis across ten representative cities using EnergyPlus. Results show that water-well-coupled GSHPs maintain approximately 87 % of their efficiency and 85.5 % of heating capacity as ambient temperature drops from 47 °F/8.3 °C to −15 °F/-26.1 °C, whereas ASHPs retain only 55 % efficiency and 44.5 % capacity. R-410A systems achieve higher efficiency, while CO 2 systems provide significant environmental advantages, including reduced risks of groundwater contamination from refrigerant leakage. On average, the CO 2 GSHPs deliver 35–45 % heating energy savings relative to ASHPs and demonstrate strong potential as a replacement for gas furnaces in cold climates. This work represents a systematic comparative assessment of CO 2 and R-410A air-source and ground-source heat pumps with water well integration. In conclusion, the findings highlight the technical viability, environmental benefits and deployment potential of CO 2 GSHPs, offering a pathway toward efficient and sustainable residential heating solutions in diverse U.S. climates.

CO 2 GSHP↗
DOE OSTI2022

Radiation effects induced by the energetic protons in 8x8x32 mm3 CdZnTe detectors

We report CdZnTe (CZT) detectors have been used in the past and are being actively considered for future gamma-ray space telescopes. One of the challenges of operating CZT detectors in low Earth orbit is radiation damage caused by energetic protons. Previous studies concluded that fluences of 10 8 -10 9 p/cm 2 are sufficient to cause shifting of peak positions and degradations of energy resolution. Degradation of detectors after proton irradiation has been extensively investigated to determine their radiation resistance limits and performance recovering procedures for their applications in space and nuclear nonproliferation. Here we present the study of radiation effects induced by 100 MeV protons in 3-cm long CdZnTe detectors that we recently proposed for the gamma-ray telescope GECCO. The goal this study is to evaluate the feasibility of using such long CZT detectors in future space missions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Pilot microbial electrolysis cell closes the hydrogen loop for hydrothermal wet waste conversion to jet fuel

The global shift toward net-zero emissions necessitates resource recovery from wet waste. In this study, we demonstrate the first feasibility of combining pilot-scale microbial electrolytic cells (MECs) with hydrothermal liquefaction (HTL) for simultaneous post-hydrothermal liquefaction wastewater (PHW) treatment and efficient hydrogen (H₂) production to meet biocrude upgrading requirements. Long-term single reactor operation revealed that fixed anode potential enabled rapid startup, and low catholyte pH and high salinity were effective in suppression of cathodic methanogenesis and acetogenesis – resulting in high current density of 16.6 A m –2 and 9.3 A m –2 when feeding synthetic wastewater and PHW respectively. Additionally, the anode biofilm exhibited spatial variations in response to local environmental conditions. In conclusion, onsite parallel or serial operations of multiple MECs showed good performance using actual PHW with a record-high H 2 production rate of 0.5 L L R day –1 for MEC over 10 liters scale, and the optimal chemical oxygen demand (COD)-to-H 2 yield reached 0.127 kg-H 2 per kg-COD, supporting a self-sufficient, closed-loop upgrade to jet fuel.

08 HYDROGEN↗
DOE OSTI2026

Altermagnetism Induced Surface Chern Insulator

We propose a new pathway to the quantized anomalous Hall effect (QAHE) by coupling an altermagnet to a topological crystalline insulator (TCI). The former gaps the topological surface states of the TCI, thereby realizing the QAHE in a robust and switchable platform with near-vanishing magnetization. We demonstrate the feasibility of this approach by studying a slab of the TCI SnTe coupled to an altermagnetic RuO 2 layer. Our first-principles calculations reveal that the d-wave altermagnetism in RuO 2 induces a 7 meV gap to the Dirac surface states on the (110) surface of SnTe, producing a finite anomalous Hall effect. Our approach generalizes to broader classes of altermagnetic materials and TCIs, thereby providing a family of topological altermagnetic heterostructures with small or vanishing magnetization that support nontrivial Chern numbers. In conclusion, our results highlight a promising new topological platform with great tunability and applications to spintronics.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Design principles for the ultimate gas deliverable capacity material: nonporous to porous deformations without volume change

Understanding the fundamental limits of gas deliverable capacity in porous materials is of critical importance as it informs whether technical targets (e.g., for on-board vehicular storage) are feasible. High-throughput screening studies of rigid materials, for example, have shown they are not able to achieve the original ARPA-E methane storage targets, yet an interesting question remains: what is the upper limit of deliverable capacity in flexible materials? In this work we develop a statistical adsorption model that specifically probes the limit of deliverable capacity in intrinsically flexible materials. The resulting adsorption thermodynamics indicate that a perfectly designed, intrinsically flexible nanoporous material could achieve higher methane deliverable capacity than the best benchmark systems known to date with little to no total volume change. Density functional theory and grand canonical Monte Carlo simulations identify a known metal–organic framework (MOF) that validates key features of the model. Therefore, this work (1) motivates a continued, extensive effort to rationally design a porous material analogous to the adsorption model and (2) calls for continued discovery of additional high deliverable capacity materials that remain hidden from rigid structure screening studies due to nominal non-porosity.

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

Strategically designed metal-free deep-ultraviolet birefringent crystals with superior optical properties

Finding new birefringent materials with deep-ultraviolet (DUV, λ < 200 nm) transparency is urgent, as current commercial materials cannot meet the rapidly growing demands in related application fields. Herein, three guanidinium-based compounds, C(NH 2 ) 3 CH 3 SO 3 , β-C(NH 2 ) 3 Cl, and γ-C(NH 2 ) 3 Cl, all featuring [C(NH 2 ) 3 ·X] ∞ (X = CH 3 SO 3 and Cl) pseudo layers, were designed through structural motif tailoring. Theoretical calculations indicate that these metal-free compounds all possess broad bandgaps (6.49–6.71 eV, HSE06) and remarkable birefringence (cal. 0.166–0.211 @ 1064 nm). Centimeter-sized C(NH 2 ) 3 CH 3 SO 3 crystals have been grown using a feasible aqua-solution method. Subsequently, to further optimize the properties, β/γ-C(NH 2 ) 3 Cl was remolded by further tailoring the [C(NH 2 ) 3 ] + cationic unit and the acceptor Cl - anion, and then the fourth compound NH 2 COF was theoretically constructed. Interestingly, NH 2 COF exhibits the desired coexistence of a wider bandgap (7.87 eV, HSE06) and giant birefringence (cal. 0.241 @ 1064 nm) attributed to its higher density of well-aligned birefringence-active groups (BAGs). Furthermore, among these four designed compounds, C(NH 2 ) 3 CH 3 SO 3 has been experimentally synthesized and exhibits a short UV cutoff edge. Centimeter-sized crystals have been grown using a feasible aqueous solution method. This study provides an effective strategy to optimize the density of BAGs for large birefringence and offers valuable insights into the strategic design of metal-free DUV birefringent crystals.

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

Large-scale deep learning for metastasis detection in pathology reports

Objectives No existing algorithm can reliably identify metastasis from pathology reports across multiple cancer types and the entire US population. In this study, we develop a deep learning model that automatically detects patients with metastatic cancer by using pathology reports from many laboratories and of multiple cancer types. Materials and Methods We use 60 471 unstructured pathology reports from 4 Surveillance, Epidemiology, and End Results (SEER) registries. The reports were coded into 1 of 3 labels: metastasis negative, metastases positive, or metastasis undetermined. We utilize a task-specific deep neural network trained from scratch and compare its performance with a widely used large language model (LLM). Results Our deep learning architecture trained on task-specific data outperforms a general-purpose LLM, with a recall of 0.894 compared to 0.824. We quantified model uncertainty and used it to defer reports for human review. We found that retaining 72.9% of reports increased recall from 0.894 to 0.969. Discussion A smaller deep learning architecture trained on task-specific data outperforms a general LLM. Equally critical to model performance is the incorporation of uncertainty quantification, achieved here through an abstention mechanism. Conclusions This study’s finding demonstrate the feasibility of developing algorithms to automatically identify metastatic cancer cases from unstructured pathology reports.

machine learning↗
DOE OSTI2025

Particle production by 𝛾−𝛾 interactions in future electron-ion colliders

The particle production in photon-photon (𝛾⁢𝛾) interactions present in electron-ion collisions is investigated. We present calculations for the total cross sections and event rates related to the production of light mesons [𝜂,𝜂′,𝑓 0 and 𝑓 2 ], charmonium [𝜂 𝑐 and 𝜒 𝑐 ], and charmoniumlike [𝑋⁡(3915), 𝑋⁡(3940), 𝑋⁡(4140), and 𝑋⁡(6900)] states, considering the Electron - Ion Collider, Electron - ion collider in China, Large Hadron electron Collider, and Future Circular Collider - electron hadron energies. Our predictions demonstrate that experimental studies of these processes are feasible and useful to constrain the properties of light mesons and quarkonium states and shed some light on the configuration of the considered charmoniumlike states.

Exotic mesons↗
DOE OSTI2024

Entanglement and Bell inequalities with boosted t t ¯

The Large Hadron Collider provides a unique opportunity to study quantum entanglement and violation of Bell inequalities at the highest energy available today. In this paper, we will investigate these quantum correlations with top quark pair production, which represents a system of two-qubits. The semileptonic top pair channel has a factor of 6 increase in statistics and easier reconstruction with respect to the dileptonic channel. Although measuring the spin polarization of the hadronic top quark is known to be challenging, our study indicates that it is feasible to reconstruct the spin density matrix of the two-qubit system using an optimal hadronic polarimeter. This is achieved with the aid of jet substructure techniques and NN-inspired reconstruction methods, which improve the mapping between subjets and quarks. We find that entanglement can already be observed at more than the 5 σ level with existing data, and violation of Bell inequalities may be probed above the 4 σ level at the HL-LHC with 3 ab − 1 of data. Published by the American Physical Society 2024

Astronomy & Astrophysics↗
DOE OSTI2024

Rapid measurement of soluble xylo-oligomers using near-infrared spectroscopy (NIRS) and multivariate statistics: calibration model development and practical approaches to model optimization

Rapid monitoring of biomass conversion processes using techniques such as near-infrared (NIR) spectroscopy can be substantially quicker and less labor-, resource-, and energy-intensive than conventional measurement techniques such as gas or liquid chromatography (GC or LC) due to the lack of solvents and preparation methods, as well as removing the need to transfer samples to an external lab for analytical evaluation. The purpose of this study was to determine the feasibility of rapid monitoring of a biomass conversion process using NIR spectroscopy combined with multivariate statistical modeling, and to examine the impact of (1) subsetting the samples in the original dataset by process location and (2) reducing the spectral range used in the calibration model on model performance. We develop multivariate calibration models for the concentrations of soluble xylo-oligosaccharides (XOS), monomeric xylose, and total solids at multiple points in a biomass conversion process which produces and then purifies XOS compounds from sugar cane bagasse. A single model using samples from multiple locations in the process stream showed acceptable performance as measured by standard statistical measures. However, compared to the single model, we show that separate models built by segregating the calibration samples according to process location show improved performance. We also show that combining an understanding of the sample spectra with simple multivariate analysis tools can result in a calibration model with a substantially smaller spectral range that provides essentially equal performance to the full-range model. We demonstrate that real-time monitoring of soluble xylo-oligosaccharides (XOS), monomeric xylose, and total solids concentration at multiple points in a process stream using NIR spectroscopy coupled with multivariate statistics is feasible. Segregation of sample populations by process location improves model performance. Models using a reduced spectral range containing the most relevant spectral signatures show very similar performance to the full-range model, reinforcing the importance of performing robust exploratory data analysis before beginning multivariate modeling.

09 BIOMASS FUELS↗