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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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Curcumin, a natural polyphenol derived from the spice turmeric (Curcuma longa), contains antioxidant, anti-inflammatory, and anti-cancer properties. However, curcumin bioavailability is inherently low due to poor water solubility and rapid metabolism. Here, we further refined for use curcumin incorporated into “biomimetic” nanolipoprotein particles (cNLPs) consisting of a phospholipid bilayer surrounded by apolipoprotein A1 and amphipathic polymer scaffolding moieties. Our cNLP formulation improves the water solubility of curcumin over 30-fold and produces nanoparticles with ~350 µg/mL total loading capacity for downstream in vitro and in vivo applications. We found that cNLPs were well tolerated in AG05965/MRC-5 human primary lung fibroblasts compared to cultures treated with curcumin solubilized in DMSO (curDMSO). Pre-treatment with cNLPs of quiescent G0/G1-phase MRC-5 cultures improved cell survival following 137Cs gamma ray irradiations, although this finding was reversed in asynchronously cycling log-phase cell cultures. These findings may be useful for establishing cNLPs as a method to improve curcumin bioavailability for administration as a radioprotective and/or radiomitigative agent against ionizing radiation (IR) exposures in non-cycling cells or as a radiosensitizing agent for actively dividing cell populations, such as tumors.
Reprocessing of used nuclear fuel reduces the requirements for long-term storage repositories. Cost-efficient processes depend on knowledge of radiation effects, but gaps occur in understanding how radiation results in the degradation of fuel separation molecules. This project investigates the influence of different n-alkyl solvents on the radiolytic fragmentation behavior of N,N,N',N'-Tetraoctyl-3-oxapentane-1,5-diamide (TODGA), a promising ligand for extraction of the lanthanides and minor actinides from dissolved nuclear fuel. Findings from this work suggest a linkage between the ionization potential of the solvent and the degradation products observed in the resulting mass spectrum.
Responders need tools to rapidly detect and identify airborne alpha radioactivity during consequence management scenarios. Traditional continuous air monitor systems used for this purpose compute the net counts in various energy windows to determine the presence of specified isotopes, such as 235U, 239Pu, and 241Am. These calculations rely on having a well-calibrated detector, which is challenging in low-background environments. Here an alternative approach of using artificial neural networks to classify alpha spectra is presented. Two network architectures, fully connected and convolutional networks, were trained to classify alpha spectra into four categories: background and background plus the three isotopes above. Sources were injected into measured background at various fractions of the derived response level (DRL) corresponding to early-phase Protective Action Guides. The convolutional network identifies all sources at 1% of the DRL with average probability of detection of 95% and false alarm probability of 1%. Further, the network identifies sources ranging between 0.25% and 1% of the DRL with higher than 80% probability of detection and lower than 7% false alarm probability. Most significantly, the network performance improves in low-count background conditions, increasing its minimum probability of detection to 93% and reducing the false alarm probabilities to lower than 0.25%. These results show that, once trained on datasets representing a range of detection scenarios, artificial neural networks can accurately identify alpha isotopes of interest without the need for detector calibration.
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This is a presentation that will be given during the American Geophysical Union Fall Meeting to an international audience. The meeting is in December, but since it is virtual this year,
Maturity models can be used to provide government agencies, industry associations, and organizations operating nuclear facilities with the ability to quickly evaluate the maturity of their cybersecurity programs and identify areas to prioritize for improvement. The Cybersecurity Capability Maturity Model (C2M2) was developed by the U.S. Department of Energy to allow organizations in the energy sector to evaluate the programmatic capabilities of their cybersecurity programs in a consistent manner, communicate programmatic maturity information, prioritize cybersecurity investments in targeted areas of concern, and track how the maturity of their cybersecurity program evolves over time. The C2M2 is designed for use by any critical infrastructure organization regardless of ownership, structure, or size. The C2M2 can be easily fine-tuned to access the maturity of nuclear cybersecurity programs and their application at individual facilities. Built on a foundation of existing cybersecurity standards, frameworks, programs, and initiatives, the model features 10 security domains. Performance in each security domain is characterized using a structured set of cybersecurity practices that represent activities an organization can perform to improve cybersecurity in their domain. Each practice can be quickly evaluated as being either fully, largely, partially, or not implemented. Once practices are evaluated for each security domain, the model defines four maturity indicator levels that apply independently to each domain in the model. To earn a maturity level in a given domain, an organization must adequately perform all the practices for that maturity level and its predecessor level(s). A small assessment team can conduct a C2M2 assessment in a single day. A screening version of the C2M2 allows an initial look at the maturity of nuclear cybersecurity programs that could be completed in under an hour.
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