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End date: 2021 × DDC: 610 × Subject: particle size ×

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    Collocated observations of cloud condensation nuclei, particle size distributions, and chemical composition
    (London : Nature Publ. Group, 2017) Schmale, Julia; Henning, Silvia; Henzing, Bas; Keskinen, Helmi; Sellegri, Karine; Ovadnevaite, Jurgita; Bougiatioti, Aikaterini; Kalivitis, Nikos; Stavroulas, Iasonas; Jefferson, Anne; Park, Minsu; Schlag, Patrick; Kristensson, Adam; Iwamoto, Yoko; Pringle, Kirsty; Reddington, Carly; Aalto, Pasi; Äijälä, Mikko; Baltensperger, Urs; Bialek, Jakub; Birmili, Wolfram; Bukowiecki, Nicolas; Ehn, Mikael; Fjæraa, Ann Mari; Fiebig, Markus; Frank, Göran; Fröhlich, Roman; Frumau, Arnoud; Furuya, Masaki; Hammer, Emanuel; Heikkinen, Liine; Herrmann, Erik; Holzinger, Rupert; Hyono, Hiroyuki; Kanakidou, Maria; Kiendler-Scharr, Astrid; Kinouchi, Kento; Kos, Gerard; Kulmala, Markku; Mihalopoulos, Nikolaos; Motos, Ghislain; Nenes, Athanasios; O’Dowd, Colin; Paramonov, Mikhail; Petäjä, Tuukka; Picard, David; Poulain, Laurent; Prévôt, André Stephan Henry; Slowik, Jay; Sonntag, Andre; Swietlicki, Erik; Svenningsson, Birgitta; Tsurumaru, Hiroshi; Wiedensohler, Alfred; Wittbom, Cerina; Ogren, John A.; Matsuki, Atsushi; Yum, Seong Soo; Myhre, Cathrine Lund; Carslaw, Ken; Stratmann, Frank; Gysel, Martin
    Cloud condensation nuclei (CCN) number concentrations alongside with submicrometer particle number size distributions and particle chemical composition have been measured at atmospheric observatories of the Aerosols, Clouds, and Trace gases Research InfraStructure (ACTRIS) as well as other international sites over multiple years. Here, harmonized data records from 11 observatories are summarized, spanning 98,677 instrument hours for CCN data, 157,880 for particle number size distributions, and 70,817 for chemical composition data. The observatories represent nine different environments, e.g., Arctic, Atlantic, Pacific and Mediterranean maritime, boreal forest, or high alpine atmospheric conditions. This is a unique collection of aerosol particle properties most relevant for studying aerosol-cloud interactions which constitute the largest uncertainty in anthropogenic radiative forcing of the climate. The dataset is appropriate for comprehensive aerosol characterization (e.g., closure studies of CCN), model-measurement intercomparison and satellite retrieval method evaluation, among others. Data have been acquired and processed following international recommendations for quality assurance and have undergone multiple stages of quality assessment.
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    Accurate in vivo tumor detection using plasmonic-enhanced shifted-excitation Raman difference spectroscopy (SERDS)
    (Wyoming, NSW : Ivyspring, 2021) Strobbia, Pietro; Cupil-Garcia, Vanessa; Crawford, Bridget M.; Fales, Andrew M.; Pfefer, T. Joshua; Liu, Yang; Maiwald, Martin; Sumpf, Bernd; Vo-Dinh, Tuan
    For the majority of cancer patients, surgery is the primary method of treatment. In these cases, accurately removing the entire tumor without harming surrounding tissue is critical; however, due to the lack of intraoperative imaging techniques, surgeons rely on visual and physical inspection to identify tumors. Surface-enhanced Raman scattering (SERS) is emerging as a non-invasive optical alternative for intraoperative tumor identification, with high accuracy and stability. However, Raman detection requires dark rooms to work, which is not consistent with surgical settings. Methods: Herein, we used SERS nanoprobes combined with shifted-excitation Raman difference spectroscopy (SERDS) detection, to accurately detect tumors in xenograft murine model. Results: We demonstrate for the first time the use of SERDS for in vivo tumor detection in a murine model under ambient light conditions. We compare traditional Raman detection with SERDS, showing that our method can improve sensitivity and accuracy for this task. Conclusion: Our results show that this method can be used to improve the accuracy and robustness of in vivo Raman/SERS biomedical application, aiding the process of clinical translation of these technologies. © The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions.