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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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Modernizing the manufacturing of high-performance polymer foams such as amino-poly(oxadiazole) bismaleimide (APO-BMI), a bismaleimide resin with superior thermal and compressive strength that incorporates additive manufacturing (AM) techniques, is crucial for its applications, but the parameters for AM can be challenging based on the physical properties of the associated monomer. For our applications, selective laser sintering (SLS) is typically used. SLS is a 3D printing technique that allows for complex shapes and geometries without structural supports while also providing high resolution material. However, printing thermosets like APO-BMI with SLS is challenging due to the complex melting and curing considerations required when selecting parameters. Additionally, the temperature difference between melting and curing of APO-BMI is over a hundred º C, which makes selecting a sintering window especially difficult. This work explores structural modifications of APO-BMI that may be more amenable for selective laser sintering. The effects of how different structural changes such as substitution pattern, heteroatom identity in the bridge, and bridge length affect the thermal properties of the material were also compared. All APO variants were found to have a smaller temperature window between the melting and curing peaks based on differential scanning calorimetry (DSC) which is advantageous for SLS. Small structural changes significantly altered the melting and curing properties of APO. Additionally, DSC revealed significant polymorphisms in APO-BMI and other APO variants which could be attributed to differences in thermal history and would need to be considered when adapting for SLS.
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Abstract not provided.
Sound production rates of fishes can be used as an indicator for coral reef health, providing an opportunity to utilize long-term acoustic recordings to assess environmental change. As acoustic datasets become more common, computational techniques need to be developed to facilitate analysis of the massive data files produced by long-term monitoring. Machine learning techniques demonstrate an advantage in the identification of fish sounds over manual sampling approaches. Here we evaluated the ability of convolutional neural networks to identify and monitor call patterns for pomacentrids (damselfishes) in a tropical reef region of the western Pacific. A stationary hydrophone was deployed for 39 mo (2014-2018) in the National Park of American Samoa to continuously record the local marine acoustic environment. A neural network was trained—achieving 94% identification accuracy of pomacentrids—to demonstrate the applicability of machine learning in fish acoustics and ecology. The distribution of sound production was found to vary on diel and interannual timescales. Additionally, the distribution of sound production was correlated with wind speed, water temperature, tidal amplitude, and sound pressure level. This research has broad implications for state-of-the-art acoustic analysis and promises to be an efficient, scalable asset for ecological research, environmental monitoring, and conservation planning.
To convert lightning indices generated by numerical weather prediction experiments into binary lightning hazard, a machine-learning tool was developed. This tool, consisting of parallel multilayer perceptron classifiers, was trained on an ensemble of planetary boundary layer schemes and microphysics parameterizations that generated four different lightning indices over 1 week. In a subsequent week, the multi-physics ensemble was applied and the machine-learning tool was used to evaluate the accuracy. Unintuitively, the machine-learning tool performed better on the testing dataset than the training dataset. Much of the error may be attributed to mischaracterizing the convection. The combination of the machine learning model and simulations could not differentiate between cloud-to-cloud lightning and cloud-to-ground lightning, despite being trained on cloud-to-ground lightning. It was found that the simulation most representative of the local operational model was the most accurate simulation tested.
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Hydrogen (H 2 ) will play a vital role in the global shift towards sustainable energy systems. Due to the high cost and challenges associated with storing hydrogen in large quantities for industrial applications, Underground Hydrogen Storage (UHS) in geological formations has emerged as a promising solution. Clay minerals, abundant in subsurface environments, play a critical role in UHS by providing low permeability, cation exchange capacity, and stability, essential for preventing hydrogen leakage. However, microorganisms in the subsurface, particularly hydrogenotrophic species, interact with clay minerals in ways that can affect the integrity of these storage systems. Microbes form biofilms on clay surfaces, which can cause pore clogging and reduce the permeability of the reservoir, potentially stabilizing H 2 storage and limiting injectivity. Microbial-induced chemical weathering, through the production of organic acids and redox reactions, can degrade clay minerals, releasing metal ions and destabilizing the storage site. These interactions raise concerns about the long-term storage capacity of UHS, as microbial processes could lead to H 2 loss and caprock degradation, compromising the storage system’s effectiveness. This mini review aims to cover the current understanding of the interactions between clay minerals and microorganisms and how these dynamics can affect the safe and sustainable deployment of UHS .
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Opportunistic premise plumbing pathogens (OPPPs) have been detected in buildings’ plumbing systems causing waterborne disease outbreaks in the United States. In this study, we monitored the occurrence of OPPPs along with free-living amoeba (FLA) and investigated the effects of residential activities in a simulated home plumbing system (HPS). Water samples were collected from various locations in the HPS and analyzed for three major OPPPs: Legionella pneumophila, nontuberculous mycobacterial species (e.g., Mycobacterium avium, M. intracellulare, and M. abscessus), and Pseudomonas aeruginosa along with two groups of amoebas (Acanthamoeba and Vermamoeba vermiformis). A metagenomic approach was also used to further characterize the microbial communities. Results show that the microbial community is highly diverse with evidence of spatial and temporal structuring influenced by environmental conditions. L. pneumophila was the most prevalent pathogen (86% of samples), followed by M. intracellulare (66%) and P. aeruginosa (21%). Interestingly, M. avium and M. abscessus were not detected in any samples. The data revealed a relatively low prevalence of Acanthamoeba spp. (4%), while V. vermiformis was widely detected (81%) across all the sampling locations within the HPS. Locations with a high concentration of L. pneumophila and M. intracellulare coincided with the highest detection of V. vermiformis, suggesting the potential growth of both populations within FLA and additional protection in drinking water. After a period of stagnation lasting at least 2-weeks, the concentrations of OPPPs and amoeba immediately increased and then decreased gradually back to the baseline. Furthermore, monitoring the microbial population after drainage of the hot water tank and partial drainage of the entire HPS demonstrated no significant mitigation of the selected OPPPs. This study demonstrates that these organisms can adjust to their environment during such events and may survive in biofilms and/or grow within FLA, protecting them from stressors in the supplied water.
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An experiment was conducted from 2016 to 2017 to assess the effect of kernel metabolism in development stages after organic mulching compared to control. Organic mulching significantly increased crop yields (higher 128% in 2016, higher 60% in 2017), oil content (the highest oil content was 27.6% higher than that of the control), and improved soil properties (SOC, SAN, AP, and AK). In this study, soil pH, SOC, AN, AP, and AK in 0–30 cm soil depth were measured. Results showed that the effect of mulching on soil pH was not significant at the harvesting stage. The greatest metabolic differences occurred during the period of high oil conversion (S2–S4), primarily involving 11 relevant metabolic pathways. This further verified that Camellia oleifera oil yield was improved after mulching. A total of 1,106 OTUs were detected by using 16S rRNA, and Venn diagram showed that there were 106 unique OTUs in control and 103 OTUs in the treatment, respectively. Correlation analysis showed that soil pH and soil temperature were two indicators with the most correlations with soil microbiota. The yield was significantly positively correlated with soil microbial Proteobacteria, Bacteroidetes, and soil nutrition indexes. Organic mulching improved the physicochemical properties of soils, caused differences in the relative abundance of dominant bacteria in soil bacteria, and improved the soil microbiological environment to promote plant growth, indicating that organic mulching is an effective measure to alleviate seasonal drought.
Species interactions exhibit varying degrees of specialization, ranging from generalist to specialist interactions. For many interactions (e.g., plant-microbiome) we lack standardized metrics of specialization, hindering our ability to apply comparative frameworks of specificity across niche axes and organismal groups. Here, we discuss the concept of plant host specificity of arbuscular mycorrhizal (AM) fungi and ectomycorrhizal (EM) fungi, including the predominant theories for their interactions: Passenger, Driver, and Habitat Hypotheses. We focus on five major areas of interest in advancing the field of plant-mycorrhizal fungal host specificity: phylogenetic specificity, host physiology specificity, functional specificity, habitat specificity, and mycorrhizal fungal-mediated plant rarity. Considering the need to elucidate foundational concepts of specificity in this globally important symbiosis, we propose standardized metrics and comparative studies to enhance our understanding. We also emphasize the importance of analyzing global mycorrhizal data holistically to draw meaningful conclusions and suggest a shift toward single-species analyses to unravel the complexities underlying these associations.
Psychological factors can modulate the hypothalamic–pituitary–adrenal (HPA) axis activity toward stressors. Animal studies demonstrated that uncontrollability was one critical factor associated with HPA axis stress response, but the results in human studies were inconsistent. The current study adopted a standardized laboratory stress induction procedure, the Trier Social Stress Test (the TSST), as the stressor to regulate the objective controllability level, and young adult participants were asked to rate their subjectively perceived control level toward the stressor and measured their cortisol stress responses ( N =54; 19 females and 35 males) to address this concern. Results showed that participants’ perceived control on the TSST was related to the cortisol stress response. In other words, under the stress of a certain objective controllability level, the lower the subjectively perceived control level, the greater the HPA axis response. This finding suggested that, in addition to objective controllability, subjectively perceived control is a psychological factor that regulates activation of the HPA axis in young adults.
Background Selenium (Se) is an essential trace element for the human body. Serum Se and urinary Se are also biomarkers to assess Se exposure status. However, studies focusing on the association between urinary Se and the risk of gestational diabetes mellitus (GDM) are rare. Objective To investigate the association between urinary Se and the risk of GDM. Methods A nested case–control study based on a prospective birth cohort in Wuhan, China, which focuses on the effects of prenatal environmental factors exposure on pregnant women and children’s health was conducted. Two hundred and twenty-six cases and 452 controls were included. Maternal urine samples were collected before GDM diagnosis, and the urinary Se levels were determined. We assessed the association of urinary Se with GDM by conditional logistic regression with maternal urinary Se level as a categorical variable, and estimated the association between Se and glucose levels by multiple linear regression. The potential modifier roles of maternal age and fetal sex have also been assessed. Results Lower urinary level of Se was significantly associated with a higher risk of GDM (OR = 2.35 for the tertile 1, 95% CI:1.36-4.06; adjusted OR = 1.79 for the tertile 2, 95%CI:1.09-2.95; p for trend = 0.01). Fetal sex had an interaction with Se in the association with GDM. The association was more pronounced among pregnant women with female fetuses than with male fetuses. Discussion Our study suggested a significant negative association between urinary Se and the risk of GDM, and this association may vary depending on the fetal sex.
Background At age 5, while only embarking on their educational journey, substantial differences in children’s cognitive ability will already exist. The aim of this study was to examine the causal association between below average cognitive ability at age 5 years and child-reported experience of school and self-concept, and teacher-reported class engagement and emotional-behavioural function at age 9 years. Methods This longitudinal cohort study used data from 7,392 children in the Growing Up in Ireland Infant Cohort, who had completed the Picture Similarities and Naming Vocabulary subtests of the British Abilities Scales at age 5. Principal components analysis was used to produce a composite general cognitive ability score for each child. Children with a general cognitive ability score more than 1 standard deviation (SD) below the mean at age 5 were categorised as ‘Below Average Cognitive Ability’ (BACA), and those scoring above this as ‘Typical Cognitive Development’ (TCD). The outcomes of interest, measured at age 9, were child-reported experience of school, child’s self-concept, teacher-reported class engagement, and teacher-reported emotional behavioural function. Binary and multinomial logistic regression models were used to examine the association between BACA and these outcomes. Results Compared to those with TCD, those with BACA had significantly higher odds of never liking school [Adjusted odds ratio (AOR) 1.82, 95% CI 1.37–2.43, p < 0.001], of being picked on (AOR 1.27, 95% CI 1.09–1.48) and of picking on others (AOR 1.53, 95% CI 1.27–1.84). They had significantly higher odds of experiencing low self-concept (AOR 1.20, 95% CI 1.02–1.42) and emotional-behavioural difficulties (AOR 1.34, 95% CI 1.10–1.63, p = 0.003). Compared to those with TCD, children with BACA had significantly higher odds of hardly ever or never being interested, motivated and excited to learn (AOR 2.29, 95% CI 1.70–3.10). Conclusion Children with BACA at school-entry had significantly higher odds of reporting a negative school experience and low self-concept at age 9. They had significantly higher odds of having teacher-reported poor class engagement and problematic emotional-behavioural function at age 9. The findings of this study suggest BACA has a causal role in these adverse outcomes. Early childhood policy and intervention design should be cognisant of the important role of cognitive ability in school and childhood outcomes.
Since extreme precipitation impacts society on small scales (i.e., a few kilometers and smaller, it is worthwhile to explore extreme precipitation trends in localized regions, such as Long Island (LI), New York. Its coastal location makes it vulnerable to various extreme events, such as tropical cyclones (TCs). This work aimed to quantify the extreme precipitation events on LI that are caused by TCs, as well as the percentage of TCs passing close to LI that cause extreme precipitation events. Both gauge-based and satellite-based precipitation datasets of varying resolutions (DAYMET, IMERG, and CPC) were used to understand the impact of dataset selection. Results are shown for the common time period of 2001–2020, as well as the full time periods of each dataset. DAYMET shows the highest percentage of extreme precipitation events linked to TCs for 2001–2020 (a maximum of 7.2%) and the highest number of TCs that caused extreme precipitation events (36.5%), with IMERG showing similar results. For the full and common time periods, the highest percentage of extreme precipitation events caused by TCs was found in eastern LI. TC-related extreme precipitation averaged over LI varied year to year, and amounts were dependent on the resolution of the observational dataset, but most datasets showed an increasing trend in the last 19 years that is larger than the trend in mean precipitation. Current infrastructure in the region is likely inadequately prepared for future impacts from TC-related extreme precipitation events in such a population-dense region.