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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Analysis of persistent contaminants and personal care products by dispersive liquid-liquid microextraction using hydrophobic magnetic deep eutectic solvents

Here, in this work, hydrophobic magnetic deep eutectic solvents (HMDESs) were used in the development of a simple and rapid dispersive liquid-liquid microextraction (DLLME) approach coupled to high performance liquid chromatography with UV detection (HPLC-UV) for the determination of ten organic contaminants including five polycyclic aromatic hydrocarbons, four UV filters, and a pesticide from water at trace levels. The HMDESs were prepared by mixing a hydrogen bond acceptor, metal halide salt, and hydrogen bond donor in suitable molar ratios. Two HMDESs, 2 tetraoctylammonium bromide ([N 8888 + ][Br - ]): cobalt chloride (CoCl 2 ): 4 octanoic acid (OA) and 3 trioctylphosphine oxide (TOPO): neodymium chloride (NdCl 3 ): 3 OA, offered the highest analyte extraction efficiency overall and were chosen as suitable solvents for validation of the microextraction method. Under optimized extraction conditions, the method required 30 µL of HMDES as extraction solvent, acetone (87.5 µL) as disperser solvent, a NaCl concentration of 30% (w/v), and an extraction time of 120 s at 20°C. Enrichment factors of the analytes ranged from 44.6 for 3-(4-methylbenzylindene) camphor to 66.0 for 2-ethylhexyl-4-(dimethyl)aminobenzoate. The method provided low limits of detection (LODs) ranging from 0.5 to 4.5 µg L -1 , and acceptable precision, with RSD values lower than 9.6%. Furthermore, the validated method was successfully applied for tap and lake water analysis, resulting in relative recoveries of spiked samples ranging between 94.7 and 119.2%.

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DOE OSTI2020

Residential electricity conservation in response to auto-generated, multi-featured, personalized eco-feedback designed for large scale applications with utilities

While past research has shown that providing residents with feedback about their electricity usage can reduce demand and its associated environmental burdens, some questions remain regarding what makes such feedback most effective. We followed the electricity usage of 36 residents who each received 14 feedback messages over 2 months. Using approaches borrowed from Natural-Language-Processing, feedbacks were generated automatically, using 10 features in random combinations. Unlike in previous studies, each resident received varying types of messages over time. In 504 observations, the average prompted reduction in electricity usage was 11 ± 3%, compared to a control group of 89 residents who received no messages. Feedback types prompting the largest reductions were self-comparisons with one’s own earlier usage (average reduction 14%) and messages of high variety from one feedback-cycle to the next (average reduction 16%). Comparisons with neighbors did not prompt higher reductions on average. Instead, they prompted reductions only when a resident’s recent usage happened to be higher than the average usage of neighbors, and increases when the reverse was true. Finally, this behavior was exhibited by all residents and is likely explained by a norm-conforming mean reversion of residents to their neighbors’ average usage, rather than an anti-conform “boomerang” behavior previously suggested in similar contexts.

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