150 research outputs found

    Long-path quantum cascade laser–based sensor for methane measurements

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    Author Posting. © American Meteorological Society, 2016. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Atmospheric and Oceanic Technology 33 (2016): 2373-2384, doi:10.1175/JTECH-D-16-0024.1.A long-path methane (CH4) sensor was developed and field deployed using an 8-μm quantum cascade laser. The high optical power (40 mW) of the laser allowed for path-integrated measurements of ambient CH4 at total pathlengths from 100 to 1200 m with the use of a retroreflector. Wavelength modulation spectroscopy was used to make high-precision measurements of atmospheric pressure–broadened CH4 absorption over these long distances. An in-line reference cell with higher harmonic detection provided metrics of system stability in rapidly changing and harsh environments. The system consumed less than 100 W of power and required no consumables. The measurements intercompared favorably (typically less than 5% difference) with a commercial in situ methane sensor when accounting for the different spatiotemporal scales of the measurements. The sensor was field deployed for 2 weeks at an arctic lake to examine the robustness of the approach in harsh field environments. Short-term precision over a 458-m pathlength was 10 ppbv at 1 Hz, equivalent to a signal from a methane enhancement above background of 5 ppmv in a 1-m length. The sensor performed well in a range of harsh environmental conditions, including snow, rain, wind, and changing temperatures. These field measurements demonstrate the capabilities of the approach for use in detecting large but highly variable emissions in arctic environments.The authors gratefully acknowledge funding for this work by MIRTHE through NSF-ERC Grant EEC-0540832. D. J. Miller acknowledges support by the National Science Foundation Graduate Research Fellowship under Grant DGE-0646086. K. Sun acknowledges support by the NASA Earth and Space Science Fellowship IIP-1263579.2017-05-0

    Electronic sculpting of ligand-GPCR subtype selectivity:the case of angiotensin II

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    GPCR subtypes possess distinct functional and pharmacological profiles, and thus development of subtype-selective ligands has immense therapeutic potential. This is especially the case for the angiotensin receptor subtypes AT1R and AT2R, where a functional negative control has been described and AT2R activation highlighted as an important cancer drug target. We describe a strategy to fine-tune ligand selectivity for the AT2R/AT1R subtypes through electronic control of ligand aromatic-prolyl interactions. Through this strategy an AT2R high affinity (<i>K</i><sub>i</sub> = 3 nM) agonist analogue that exerted 18,000-fold higher selectivity for AT2R versus AT1R was obtained. We show that this compound is a negative regulator of AT1R signaling since it is able to inhibit MCF-7 breast carcinoma cellular proliferation in the low nanomolar range

    Clarifying the Dominant Sources and Mechanisms of Cirrus Cloud Formation

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    Formation of cirrus clouds depends on the availability of ice nuclei to begin condensation of atmospheric water vapor. Although it is known that only a small fraction of atmospheric aerosols are efficient ice nuclei, the critical ingredients that make those aerosols so effective have not been established. We have determined in situ the composition of the residual particles within cirrus crystals after the ice was sublimated. Our results demonstrate that mineral dust and metallic particles are the dominant source of residual particles, whereas sulfate and organic particles are underrepresented, and elemental carbon and biological materials are essentially absent. Further, composition analysis combined with relative humidity measurements suggests that heterogeneous freezing was the dominant formation mechanism of these clouds.National Science Foundation (U.S.) (NSF AGS-0840732)National Science Foundation (U.S.) (NSF grant AGS-1036275)United States. National Aeronautics and Space Administration (NASA Earth and Space Science Graduate Fellowship)United States. National Aeronautics and Space Administration (NASA Radiation Sciences Program award number NNX07AL11G)United States. National Aeronautics and Space Administration (NASA Radiation Sciences Program award number NNX08AH57G)United States. National Aeronautics and Space Administration (NASA Earth Science Division Atmospheric Composition program award number NNH11AQ58UI

    Validation of MUSES NH<sub>3</sub> observations from AIRS and CrIS against aircraft measurements from DISCOVER-AQ and a surface network in the Magic Valley

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    Ammonia is a significant precursor of PM2.5 particles and thus contributes to poor air quality in many regions. Furthermore, ammonia concentrations are rising due to the increase of large-scale, intensive agricultural activities, which are often accompanied by greater use of fertilizers and concentrated animal feedlots. Ammonia is highly reactive and thus highly variable and difficult to measure. Satellite-based instruments, such as the Atmospheric Infrared Sounder (AIRS) and the Cross-Track Infrared Sounder (CrIS), have been shown to provide much greater temporal and spatial coverage of ammonia distribution and variability than is possible with in situ networks or aircraft campaigns, but the validation of these data is limited. Here we evaluate MUSES (multi-spectra, multi-species, multi-sensors) ammonia retrievals from AIRS and CrIS against ammonia measurements from aircraft in the California Central Valley and in the Colorado Front Range. These are small datasets taken over high-source regions under very different conditions: winter in California and summer in Colorado. Direct comparisons of the surface values of the retrieved profiles are biased very low in California (∼ 40 ppbv) and slightly high in Colorado (∼ 4 ppbv). This bias appears to be primarily due to smoothing error, since applying the instrument operator effectively reduces the bias to zero; even after the smoothing error is accounted for, the average uncertainty at the surface is in the 20 %–30 % range. We also compare 3 years of CrIS ammonia against an in situ network in the Magic Valley in Idaho We show that CrIS ammonia captures both the seasonal signal and the spatial variability in the Magic Valley, although it is biased low here also. In summary, this analysis substantially adds to the validation record but also points to the need for more validation under many different conditions and at higher altitudes.</p
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