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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 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Elemental analysis of air-sensitive frozen molten salt samples using an inert transfer chamber for LIBS/LA-ICP-TOF-MS analysis

A novel inert sample transfer system was developed and employed to enable, for the first time, the analysis of air-sensitive salt samples in a two-volume ablation cell using simultaneous laser-induced breakdown spectroscopy (LIBS) and laser ablation (LA)-inductively coupled plasma (ICP)-time-of-flight (TOF)-mass spectrometry (MS) analysis. Molten salts are of growing interest as a medium for advanced nuclear reactors and nuclear fuel reprocessing technologies continue to be developed around their use. However, compositional analysis of molten salt samples can be challenging because of their air-sensitive nature and varying solubilities leading to inaccurate measurements when digested. LA-based analysis provides an alternate method to digestion and can provide rapid elemental information with little sample preparation. In this study, LIBS and LA-ICP-TOF-MS were used to analyze the Ce content in frozen salt samples taken from a series of electrochemical experiments. Calibrations were built for each technique, and the resulting limits of detection for Ce were estimated to be 107 and 58 µg g −1 for LIBS and LA-ICP-TOF-MS, respectively. Test samples from the electrochemical experiments were analyzed using these calibrations. The results matched bulk digestion-based ICP-optical emission spectroscopy values, and daily trends in Ce concentration changes were identified. Additionally, the LIBS and LA-ICP-TOF-MS analysis was demonstrated for identifying microgram per gram levels of components and detecting trace contaminants. The impurities detected by LIBS included Al, Mg, Ca, and Na. The impurities detected by LA-ICP-TOF-MS included W, Ag, Al, Fe, Ni, Mo, Nd, Sm, Th, and U.

Andrews, Hunter B. [Oak Ridge National Laboratory ↗
DOE OSTI2023

Versatile recognition of graphene layers from optical images under controlled illumination through green channel correlation method

In this study, a simple yet versatile method is proposed for identifying the number of exfoliated graphene layers transferred on an oxide substrate from optical images, utilizing a limited number of input images for training, paired with a more traditional number of a few thousand well-published Github images for testing and predicting. Two thresholding approaches, namely the standard deviation-based approach and the linear regression-based approach, were employed in this study. The method specifically leverages the red, green, and blue color channels of image pixels and creates a correlation between the green channel of the background and the green channel of the various layers of graphene. This method proves to be a feasible alternative to deep learning-based graphene recognition and traditional microscopic analysis. The proposed methodology performs well under conditions where the effect of surrounding light on the graphene-on-oxide sample is minimum and allows rapid identification of the various graphene layers. Here, the study additionally addresses the functionality of the proposed methodology with nonhomogeneous lighting conditions, showcasing successful prediction of graphene layers from images that are lower in quality compared to typically published in literature. In all, the proposed methodology opens up the possibility for the non-destructive identification of graphene layers from optical images by utilizing a new and versatile method that is quick, inexpensive, and works well with fewer images that are not necessarily of high quality.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Designing Unmanned Aerial Survey Monitoring Program to Assess Floating Litter Contamination

Monitoring marine contamination by floating litter can be particularly challenging since debris are continuously moving over a large spatial extent pushed by currents, waves, and winds. Floating litter contamination have mostly relied on opportunistic surveys from vessels, modeling and, more recently, remote sensing with spectral analysis. This study explores how a low-cost commercial unmanned aircraft system equipped with a high-resolution RGB camera can be used as an alternative to conduct floating litter surveys in coastal waters or from vessels. The study compares different processing and analytical strategies and discusses operational constraints. Collected UAS images were analyzed using three different approaches: (i) manual counting (MC), using visual inspection and image annotation with object counts as a baseline; (ii) pixel-based detection, an automated color analysis process to assess overall contamination; and (iii) machine learning (ML), automated object detection and identification using state-of-the-art convolutional neural network (CNNs). Our findings illustrate that MC still remains the most precise method for classifying different floating objects. ML still has a heterogeneous performance in correctly identifying different classes of floating litter; however, it demonstrates promising results in detecting floating items, which can be leveraged to scale up monitoring efforts and be used in automated analysis of large sets of imagery to assess relative floating litter contamination.

Almeida, Sílvia (ORCID:0000000320636826)↗
DOE OSTI2021

Optimization of TAM16, a Benzofuran That Inhibits the Thioesterase Activity of Pks13; Evaluation toward a Preclinical Candidate for a Novel Antituberculosis Clinical Target

With increasing drug resistance in tuberculosis (TB) patient populations, there is an urgent need for new drugs. Ideally, new agents should work through novel targets so that they are unencumbered by preexisting clinical resistance to current treatments. Benzofuran 1 was identified as a potential lead for TB inhibiting a novel target, the thioesterase domain of Pks13. Although, having promising activity against Mycobacterium tuberculosis, its main liability was inhibition of the hERG cardiac ion channel. This article describes the optimization of the series toward a preclinical candidate. Despite improvements in the hERG liability in vitro, when new compounds were assessed in ex vivo cardiotoxicity models, they still induced cardiac irregularities. Further series development was stopped because of concerns around an insufficient safety window. However, the demonstration of in vivo activity for multiple series members further validates Pks13 as an attractive novel target for antitubercular drugs and supports development of alternative chemotypes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Semi-supervised Machine Learning Enables the Robust Detection of Multireference Character at Low Cost

Multireference (MR) diagnostics are common tools for identifying strongly correlated electronic structure that makes single-reference (SR) methods (e.g., density functional theory or DFT) insufficient for accurate property prediction. However, MR diagnostics typically require computationally demanding correlated wave function theory (WFT) calculations, and diagnostics often disagree or fail to predict MR effects on properties. To overcome these challenges, we introduce a semi-supervised machine learning (ML) approach with virtual adversarial training (VAT) of an MR classifier using 15 WFT and DFT MR diagnostics as inputs. In semi-supervised learning, only the most extreme SR or MR points are labeled, and the remaining point labels are learned. The resulting VAT model outperforms the alternatives, as quantified by the distinct property distributions of SR- and MR-classified molecules. Additionally, to reduce the cost of generating inputs to the VAT model, we leverage the VAT model’s robustness to noisy inputs by replacing WFT MR diagnostics with regression predictions in an MR decision engine workflow that preserves excellent performance. We demonstrate the transferability of our approach to larger molecules and those with distinct chemical composition from the training set. This MR decision engine demonstrates promise as a low-cost, high-accuracy approach to the automatic detection of strong correlation for predictive high-throughput screening.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Accelerating phase field simulations through a hybrid adaptive Fourier neural operator with U-net backbone

Prolonged contact between a corrosive liquid and metal alloys can cause progressive dealloying. For one such process as liquid-metal dealloying (LMD), phase field models have been developed to understand the mechanisms leading to complex morphologies. However, the LMD governing equations in these models often involve coupled non-linear partial differential equations (PDE), which are challenging to solve numerically. In particular, numerical stiffness in the PDEs requires an extremely refined time step size (on the order of 10 -12 s or smaller). This computational bottleneck is especially problematic when running LMD simulation until a late time horizon is required. This motivates the development of surrogate models capable of leaping forward in time, by skipping several consecutive time steps at-once. In this paper, we propose a U-shaped adaptive Fourier neural operator (U-AFNO), a machine learning (ML) based model inspired by recent advances in neural operator learning. U-AFNO employs U-Nets for extracting and reconstructing local features within the physical fields, and passes the latent space through a vision transformer (ViT) implemented in the Fourier space (AFNO). We use U-AFNOs to learn the dynamics of mapping the field at a current time step into a later time step. We also identify global quantities of interest (QoI) describing the corrosion process (e.g., the deformation of the liquid-metal interface, lost metal, etc.) and show that our proposed U-AFNO model is able to accurately predict the field dynamics, in spite of the chaotic nature of LMD. Most notably, our model reproduces the key microstructure statistics and QoIs with a level of accuracy on par with the high-fidelity numerical solver, while achieving a significant 11, 200 × speed-up on a high-resolution grid when comparing the computational expense per time step. Finally, we also investigate the opportunity of using hybrid simulations, in which we alternate forward leaps in time using the U-AFNO with high-fidelity time stepping. We demonstrate that while advantageous for some surrogate model design choices, our proposed U-AFNO model in fully auto-regressive settings consistently outperforms hybrid schemes.

36 MATERIALS SCIENCE↗
DOE OSTI2020

An uranyl sorption study inside functionalised nanopores

Sorption mechanism of uranyl by poly(bis[2-(methacryloyloxy)ethyl] phosphate) (PB2MP) functionalised polyvinylidene fluoride (PVDF) track-etched membranes, PB2MP-g-PVDF, was investigated. It was found that uranyl sorption obeyed Langmuir isotherm model giving a maximum U(VI) membrane uptake of 6.73 μmol g -1 and an affinity constant of 9.85 10 6 L mol -1 . XPS and TRPL measurements were performed to identify sorbed uranyl oxidation state and its environment. Uranyl was found to be mainly in its hexavalent state, i.e. U(VI), showing that the trapping inside the PB2MPg-PVDF nanoporous membranes did not change the ion speciation. Two sorbed uranyl life-times ($τ_1$ = 8.8 μs and $τ_2$ = 102.8 μs) were measured by TRPL which pointed out different complexations taking place inside the nanopores. Uranyl sorption by PB2MP-g-PVDF membranes was also found to be pH dependent demonstrating the highest performance at circumneutral pH. In addition, TRPL was demonstrated to be not only a remarkable technique for U(VI) characterization, but also an alternative to voltammetry detection for trace on-site uranyl monitoring using PB2MP-g-PVDF nanoporous membranes

36 MATERIALS SCIENCE↗
DOE OSTI2022

Combining Experimental and Theoretical Techniques to Gain an Atomic Level Understanding of the Defect Binding Mechanism in Hard Carbon Anodes for Sodium Ion Batteries

Sodium ion batteries (NIBs) are an attractive alternative to lithium–ion batteries in applications that require large–scale energy storage due to sodium's high natural abundance and low cost. Hard carbon (HC) is the most promising anode material for NIBs; however, there is a knowledge gap in the understanding of the sodium binding mechanism that prevents a rational design of HC. This study tunes sucrose–derived HC via synthesis temperature then evaluates the structural, physical, and electrochemical properties. Neutron total scattering is used to generate structural models by fitting pair distribution functions (PDF) with a combination of molecular dynamics and reverse Monte Carlo methods. From this model, the number and type of structural features are identified, quantified, and correlated to the galvanostatic charge/discharge. A method of PDF “fingerprinting” binding sites using Na probe atoms is developed and analyzing these PDFs reveals an atomistic view of ion binding sites responsible for “defect” storage mechanisms. Combining these techniques results in an atomic–level study that provides a big picture of the Na–binding mechanism in NIBs, which allows for more precise tuning of the structure–property relationships in the future. Finally, the methodologies developed will also enable new strategies for the analysis of amorphous functional materials.

25 ENERGY STORAGE↗
DOE OSTI2021

Flowback Test Analyses at the Utah Frontier Observatory for Research in Geothermal Energy (FORGE) Site

Abstract In 2017 and 2019, injection testing was carried out in three zones in a vertical well in granite at the Frontier Observatory for Research in Geothermal Energy site near Milford, Utah, USA. In several injection cycles, flowback was implemented rather than shut-in. The goal was to explore an alternative to prolonged shut-in periods for inferring closure stress, formation compressibility, and formation permeability (permeability thickness product). The flowback procedures involved a cyclic flowback/shut-in, while pressure decreased. The flowback data are presented, and analyses are shown. The inferred closure stress(es) from flowback analyses are lower than for equivalent injection cycles that were strictly shut-in. Relatively high formation compressibility obtained from the flowback analyses indicates an extensive, fractured system. This study also includes numerical simulation of the flowback events. The numerical model shows that the rebound pressure is not necessarily the lower bound of the minimum principal stress. The signature of stiffness change can be identified as the process when the depletion mainly transitions from hydraulic fracture to natural fractures from numerical analysis. Overall, flowback potentially has advantages over shut-in because of the reduced time to closure.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Techno-Economic Analysis of decentralized preprocessing systems for fast pyrolysis biorefineries with blended feedstocks in the southeastern United States

This study evaluated the economic feasibility of fast pyrolysis biorefineries fed with blended pine residues and switchgrass in the Southeastern U.S. with different supply chain design. Previous techno-economic analyses (TEA) have focused on either blended biomass or decentralized preprocessing without investigating the impacts of varied process parameters, technology options, and real-world biomass distribution. This study fills the literature gap by modeling scenarios for different biomass blending ratios, biorefinery and preprocessing site (so-called depot) capacities, and alternative preprocessing technologies. High-resolution, real-world geospatial data were analyzed using Geographic Information Systems to facilitate supply chain design and TEA. For a decentralized system, the minimum fuel selling price (MFSP) of biofuel was 3.92–4.33 per gallon gasoline equivalent (GGE), while the MFSP for the centralized biorefinery at the same capacities ranged between 3.75–4.02/GGE. Implementing a high moisture pelleting process depot rather than a conventional pelleting process lowered the MFSP by 0.03–0.17/GGE. Scenario analysis indicated decreased MFSP with increasing biorefinery capacities but not necessarily with increasing depot size. Medium-size depots (500 OMDT/day) achieved the lowest MFSP. Here, this analysis identified the optimal blending ratios for two preprocessing technologies at varied depot sizes. Counterintuitively, increasing the proportion of higher cost switchgrass reduced the MFSP for large biorefineries (>5000 ODMT/day), but increased the MFSP for small biorefineries (1000–2500 ODMT/day). Although the decentralized systems have a higher MFSP based on current analysis, it has other potential benefits such as mitigated supply chain risks and improved feedstock quality that are difficult to be quantified in this TEA.

09 BIOMASS FUELS↗
DOE OSTI2025

Passivation and Degradation of Sulfur-Treated Silicon Surfaces for Photovoltaics

Sulfur-based passivation for silicon surfaces using H2S gas is an alternative passivation method to reduce the thermal budget for Si photovoltaics. To understand the impact of the high-quality passivation and an observed passivation efficiency decrease after air exposure, we have studied the chemical surface structure by X-ray photoelectron spectroscopy (XPS), X-ray Auger electron spectroscopy (XAES), and S and Si L2,3 X-ray emission spectroscopy (XES). On the S-passivated silicon surfaces, we find the formation of S–Si bonds, in addition to some Si–O bonds. Upon air exposure, sulfur partially desorbs from the Si surface and an increased presence of Si–O and S–O bonds is observed. We identify that well-defined S–Si bonds are crucial to maintain high-quality surface passivation for Si photovoltaics, which allows further optimization of the fabrication process for S-based passivation on silicon.

Hua, Amandee↗
DOE OSTI2023

Testing Paired Neural Network Models for Aftershock Identification

Aftershock sequences are a burden to real-time seismic monitoring. Cross-correlation can be used because aftershocks exhibit similar waveforms, but the method is computationally expensive. Deep learning may be an alternative, as it is computationally efficient, but great attention to training and testing is required in order to trust that the model can generalize to new aftershock sequences. This is problematic for aftershock sequences, because large-magnitude earthquakes are unpredictable and are globally widespread. Here, we test several paired neural network (PNN) models trained on a augmented (noise-added) earthquake dataset, to determine whether they can be generalized to process real aftershock sequences. Two aftershock datasets that were originally detected by cross-correlation and subsequently validated by an expert analyst were used. We found that current PNN models struggle to generalize to aftershock sequences. However, we identify approaches to improve training future PNN models and believe that improvements may be achieved by transfer learning.

58 GEOSCIENCES↗
DOE OSTI2025

Risk Assessment of EIC Central Detector (ePIC) Solenoid Magnet (MARCO)

As part of the BNL-JLab-CEA Electron Ion Collider (EIC) collaboration, the design of a 2 T, 2.8 m bore diameter, 3.8 m long conduction cooled superconducting detector magnet design is completed. Such magnet will be employed at the interaction region of ePIC for physics experiment. The magnet is a passive shielded solenoidal magnet system consisting of 3 coils wound with specially designed conductor using NbTi Rutherford type cable in copper stabilized channel. This paper describes the risk analysis as the part of Failure Modes and Effects Analysis (FMEA) that was carried out as a team to identify their various failure modes and risks associated with the magnets system. In conclusion, this FMEA is intended to become the content of the designed document as an integral part of the engineering assessment and the statement of work for the potential vendors towards design and built.

Ghoshal, Probir K. [Thomas Jefferson National Acce↗
DOE OSTI2021

Instability of U 3 Si 2 in pressurized water media at elevated temperatures

Following the Fukushima Daiichi accident, significant efforts from industry and the scientific community have been directed towards the development of alternative nuclear reactor fuels with enhanced accident tolerance. Among the proposed materials for such fuels is a uranium silicide compound (U 3 Si 2 ), which has been selected for its enhanced thermal conductivity and high density of uranium compared to the reference commercial light water reactor (LWR) nuclear fuel, uranium oxide (UO 2 ). To be a viable candidate LWR fuel, however, U 3 Si 2 must also demonstrate that, in the event of this fuel coming in contact with aqueous media, it will not degrade rapidly. In this contribution, we report the results of experiments investigating the stability of U 3 Si 2 in pressurized water at elevated temperatures and identify the mechanisms that control the interaction of U 3 Si 2 under these conditions. Our data indicate that the stability of this material is primarily controlled by the formation of a layer of USiO 4 (the mineral, coffinite) at the surface of U 3 Si 2 . The results also show that these layers are destabilized at T > 300 °C, leading to the complete decomposition of U 3 Si 2 and its pulverization due to its full oxidation to UO 2 .

36 MATERIALS SCIENCE↗
DOE OSTI2021

Generation of isolated soft x-ray attosecond pulses with mid-infrared driving lasers via transient phase-matching gating

Abstract Isolated attosecond pulses (IAPs) in the soft x-ray (SXR) region are highly desirable for attosecond time-resolved experiments. Here we identify a transient phase matching gating method for the generation of such IAPs with mid-infrared (MIR) lasers. This gating method works when a loosely focused, long-duration MIR Gaussian driving beam is slightly reshaped during its propagation in the gas medium under the ‘critical’ ionization condition. Quantitatively, the calculated coherence length of high harmonic is used to analyze the mechanism of the gating method, by using one-dimensional plane-wave beams and by comparing 2000 nm and 800 nm lasers. The robustness of the generation method is checked by varying laser’s carrier-envelope-phase and gas pressure. This gating method provides with an alternative route to efficiently produce tabletop ultrashort attosecond SXR light sources with the emerging MIR lasers.

Physics↗
DOE OSTI2023

Spectroscopic survey of faint planetary-nebula nuclei – II. The subdwarf O central star of Fr 2-30

Fr 2-30 = PN? G126.8−15.5 is a faint emission nebula, hosting a 14th-mag central star that we identify here for the first time. Deep Hα and [O III ] images reveal a roughly elliptical nebula with dimensions of at least 22 arcmin × 14 arcmin, fading into a surrounding network of even fainter emission. Optical spectrograms of the central star show it to have a subdwarf O spectral type, with a Gaia parallax distance of 890 pc. A model-atmosphere analysis gives parameters of T eff = 60 000 K, log g = 6.0, and a low helium content of n He /n H = 0.0017. The location of the central star in the log g–T eff plane is inconsistent with a post-asymptotic-giant-branch evolutionary status. Two alternatives are that it is a helium-burning post-extreme-horizontal-branch object, or a hydrogen-burning post-red-giant-branch star. In either case, the evolutionary ages are so long that a detectable planetary nebula (PN) should not be present. We find evidence for a variable radial velocity (RV), suggesting that the star is a close binary. However, there are no photometric variations, and the spectral-energy distribution rules out a companion earlier than M2 V. The RVs of the star and surrounding nebula are discordant, and the nebula lacks typical PN morphology. We suggest that Fr 2-30 is a ‘PN mimic’ – the result of a chance encounter between the hot sdO star and an interstellar cloud. However, we note the puzzling fact that there are several nuclei of genuine PNe that are known to be in evolutionary states similar to that of the Fr 2-30 central star.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

The metabolic origins of non-photorespiratory CO 2 release during photosynthesis: a metabolic flux analysis

Respiration in the light (R L ) releases CO 2 in photosynthesizing leaves and is a phenomenon that occurs independently from photorespiration. Since R L lowers net carbon fixation, understanding R L could help improve plant carbon-use efficiency and models of crop photosynthesis. Although R L was identified more than 75 years ago, its biochemical mechanisms remain unclear. To identify reactions contributing to R L , we mapped metabolic fluxes in photosynthesizing source leaves of the oilseed crop and model plant camelina (Camelina sativa). We performed a flux analysis using isotopic labeling patterns of central metabolites during 13 CO 2 labeling time course, gas exchange, and carbohydrate production rate experiments. To quantify the contributions of multiple potential CO 2 sources with statistical and biological confidence, we increased the number of metabolites measured and reduced biological and technical heterogeneity by using single mature source leaves and quickly quenching metabolism by directly injecting liquid N 2 ; we then compared the goodness-of-fit between these data and data from models with alternative metabolic network structures and constraints. Our analysis predicted that R L releases 5.2 μmol CO2 g –1 FW h –1 of CO 2 , which is relatively consistent with a value of 9.3 μmol CO 2 g –1 FW h –1 measured by CO 2 gas exchange. The results indicated that ≤10% of R L results from TCA cycle reactions, which are widely considered to dominate R L . Further analysis of the results indicated that oxidation of glucose-6-phosphate to pentose phosphate via 6-phosphogluconate (the G6P/OPP shunt) can account for >% of CO 2 released by R L .

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Non-Neutron Transmutation of Used Nuclear Fuel (Final Report)

The primary goal of this study is to develop a national facility concept for transmuting long-lived fission products (LLFP) to substantially reduce the disposal impact by minimizing the need for a geologic-timescale repository. As a charter for this study, the national transmutation facility was required to reduce the radiotoxicity and decay heat of LLFP isotopes by at least 90% relative to their values at discharge from a commercial LWR, while consuming less than 10% of the reactor's energy. The identified LLFP isotopes are Se-79, Zr-93, Tc-99, I-129, Sn-126, and Cs-135, whose radiotoxicity is about 99% of the total radiotoxicity of all fission products at 1,000 years. Approximately ~72 kg of LLFPs is discharged every year from a 1,000 MWe commercial or advanced nuclear reactor. First, LLFP transmutation options with non-neutron beams (photons and protons) were explored. The study concluded that LLFP transmutation is feasible with high-energy, high-intensity photons or protons, but impractical on an engineering scale due to low transmutation rates and the high energy requirements to produce the desired photon or proton beams. As alternatives, LLFP transmutation options with neutrons from fission, fusion, and spallation reactions were additionally explored. The transmutation options using advanced critical reactors are attractive only for selective LLFP isotopes because the production rates of several LLFP isotopes (Zr-93, Sn-126, and Cs-135) from fission reactions are larger than the transmutation rates. The transmutation options with only spallation neutrons are favorable to transmute all LLFP isotopes, but as a tradeoff, the net transmutation rates are reduced. The national transmutation facility concept was developed following an exploration of transmutation options using various incident particles. The proposed national LLFP transmutation comprises a dedicated molten-salt reactor (MSR), a proton accelerator, and a spallation neutron-based transmuter. The MSR power was set at 300 MWt and 120 MWe, with the thermal power approximately 10% of that of a commercial 1,000 MWe PWR. The electricity generated by the MSR powers the accelerator and transmuter. The accelerator produces 1 GeV, 30 mA protons, which are introduced into the spallation neutron-based transmuter. The spallation neutron-based transmuter consists of a central spallation target and LLFP target pins merged in a heavy water tank. The six LLFP isotopes are separated into two groups. Tc-99, I-129, and Se-79, having larger neutron cross sections, belong to group A, while Zr-93, Sn126, and Cs-135, having smaller neutron cross sections, belong to group B. Then, for effective transmutation, LLFPs in groups A and B are transmuted in the dedicated MSR and in a spallation neutron-based transmuter, respectively. The estimated capital cost of the national transmutation facility is approximately $\$$3.1B, and its annual O&M cost is expected to be ~$\$$182M. Radiotoxicity and decay heat of LLFPs were calculated and compared with those of the original LLFPs. It was assumed that the targets were made with elementwise LLFP rather than isotopic LLFP, owing to the potentially high cost of isotopic separation from used nuclear fuels. The decay heat of LLFPs can be reduced by more than 90% using a single national transmutation facility. However, radiotoxicity decreases by 79–84%, which does not meet the transmutation performance requirement, primarily because Cs-135 is produced rather than depleted. Thus, to meet the design requirement, Cs-135 should be separated from other Cs isotopes and irradiated in a spallation neutron-based transmuter. Then, radiotoxicity decreases by ~92%.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗