2018, Córdoba-Jabonero, Carmen, Sicard, Michaël, Ansmann, Albert, del Águila, Ana, Baars, Holger

The application of the POLIPHON (POlarization-LIdar PHOtometer Networking) method is presented for the first time in synergy with continuous 24/7 polarized Micro-Pulse Lidar (P-MPL) measurements to derive the vertical separation of two or three particle components in different aerosol mixtures, and the retrieval of their particular optical properties. The procedure of extinction-to-mass conversion, together with an analysis of the mass extinction efficiency (MEE) parameter, is described, and the relative mass contribution of each aerosol component is also derived in a further step. The general POLIPHON algorithm is based on the specific particle linear depolarization ratio given for different types of aerosols and can be run in either 1-step (POL-1) or 2 steps (POL-2) versions with dependence on either the 2- or 3-component separation. In order to illustrate this procedure, aerosol mixing cases observed over Barcelona (NE Spain) are selected: a dust event on 5 July 2016, smoke plumes detected on 23 May 2016 and a pollination episode observed on 23 March 2016. In particular, the 3-component separation is just applied for the dust case: a combined POL-1 with POL-2 procedure (POL-1/2) is used, and additionally the fine-dust contribution to the total fine mode (fine dust plus non-dust aerosols) is estimated. The high dust impact before 12:00 UTC yields a mean mass loading of 0.6±0.1 g m'2 due to the prevalence of Saharan coarse-dust particles. After that time, the mean mass loading is reduced by two-thirds, showing a rather weak dust incidence. In the smoke case, the arrival of fine biomass-burning particles is detected at altitudes as high as 7 km. The smoke particles, probably mixed with less depolarizing non-smoke aerosols, are observed in air masses, having their origin from either North American fires or the Arctic area, as reported by HYSPLIT back-trajectory analysis. The particle linear depolarization ratio for smoke shows values in the 0.10-0.15 range and even higher at given times, and the daily mean smoke mass loading is 0.017±0.008 g m'2, around 3 % of that found for the dust event. Pollen particles are detected up to 1.5 km in height from 10:00 UTC during an intense pollination event with a particle linear depolarization ratio ranging between 0.10 and 0.15. The maximal mass loading of Platanus pollen particles is 0.011±0.003 g m'2, representing around 2 % of the dust loading during the higher dust incidence. Regarding the MEE derived for each aerosol component, their values are in agreement with others referenced in the literature for the specific aerosol types examined in this work: 0.5±0.1 and 1.7±0.2 m2 g'1 are found for coarse and fine dust particles, 4.5±1.4 m2 g'1 is derived for smoke and 2.4±0.5 m2 g'1 for non-smoke aerosols with Arctic origin, and a MEE of 2.4±0.8 m2 g'1 is obtained for pollen particles, though it can reach higher or lower values depending on predominantly smaller or larger pollen grain sizes. Results reveal the high potential of the P-MPL system, a simple polarization-sensitive elastic backscatter lidar working in a 24/7 operation mode, to retrieve the relative optical and mass contributions of each aerosol component throughout the day, reflecting the daily variability of their properties. In fact, this procedure can be simply implemented in other P-MPLs that also operate within the worldwide Micro-Pulse Lidar Network (MPLNET), thus extending the aerosol discrimination at a global scale. Moreover, the method has the advantage of also being relatively easily applicable to space-borne lidars with an equivalent configuration such as the ongoing Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) on board NASA CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) and the forthcoming Atmospheric Lidar (ATLID) on board the ESA EarthCARE mission.

2006, Polzehl, Jörg, Tabelow, Karsten

This document describes the usage of the R package fmri to analyse functional Magnetic Resonance Imaging (fMRI) data with structure adaptive smoothing procedures (Propagation-Separation (PS) approach) as described in [7].

2013, Becker, Saskia

The article presents new results on the Propagation-Separation Approach by Polzehl and Spokoiny [2006]. This iterative procedure provides a unified approach for nonparametric estimation, supposing a local parametric model. The adaptivity of the estimator ensures sensitivity to structural changes. Originally, an additional memory step was included into the algorithm, where most of the theoretical properties were based on. However, in practice, a simplified version of the algorithm is used, where the memory step is omitted. Hence, we aim to justify this simplified procedure by means of a theoretical study and numerical simulations. In our previous study [Becker and Mathé, 2013], we analyzed the simplified Propagation-Separation Approach, supposing piecewise constant parameter functions with sharp discontinuities. Here, we consider the case of a misspecified model.

2017, Kreuzer, Lucas Philipp, Männel, Max Julius, Schubert, Jonas, Höller, Roland P. M., Chanana, Munish

Enzyme-catalyzed controlled radical polymerization represents a powerful approach for the polymerization of a wide variety of water-soluble monomers. However, in such an enzyme-based polymerization system, the macromolecular catalyst (i.e., enzyme) has to be separated from the polymer product. Here, we present a compelling approach for the separation of the two macromolecular species, by taking the catalyst out of the molecular domain and locating it in the colloidal domain, ensuring quasi-homogeneous catalysis as well as easy separation of precious biocatalysts. We report on gold nanoparticles coated with horseradish peroxidase that can catalyze the polymerization of various monomers (e.g., N-isopropylacrylamide), yielding thermoresponsive polymers. Strikingly, these biocatalyst-coated nanoparticles can be recovered completely and reused in more than three independent polymerization cycles, without significant loss of their catalytic activity.

2008, Hoffmann, Devy, Tabelow, Karsten

There exists a variety of software tools for analyzing functional Magnetic Resonance Imaging data. A very popular one is the freely available SPM package by the Functional Imaging Laboratory at the Wellcome Department of Imaging Neuroscience. In order to enhance the signal-to-noise ratio it provides the possibility to smooth the data in a pre-processing step by a Gaussian filter. However, this comes at the cost of reducing the effective resolution. In a series of recent papers it has been shown, that using a structural adaptive smoothing algorithm based on the Propagation-Separation method allows for enhanced signal detection while preserving the shape and spatial extent of the activation areas. Here, we describe our implementation of this algorithm as a toolbox for SPM.

2019, Ye, X., Cui, J., Li, B., Li, N., Wang, R., Yan, Z., Tan, J., Zhang, J., Wan, X.

Selective crystallization represents one of the most economical and convenient methods to provide large-scale optically pure chiral compounds. Although significant development has been achieved since Pasteur’s separation of sodium ammonium tartrate in 1848, this method is still fundamentally low efficient (low transformation ratio or high labor). Herein, we describe an enantiomer-selective-magnetization strategy for quantitatively separating the crystals of conglomerates by using a kind of magnetic nano-splitters. These nano-splitters would be selectively wrapped into the S-crystals, leading to the formation of the crystals with different physical properties from that of R-crystals. As a result of efficient separation under magnetic field, high purity chiral compounds (99.2 ee% for R-crystals, 95.0 ee% for S-crystals) can be obtained in a simple one-step crystallization process with a high separation yield (95.1%). Moreover, the nano-splitters show expandability and excellent recyclability. We foresee their great potential in developing chiral separation methods used on different scales. © 2019, The Author(s).

2006, Polzehl, Jörg, Tabelow, Karsten

Digital imaging has become omnipresent in the past years with a bulk of applications ranging from medical imaging to photography. When pushing the limits of resolution and sensitivity noise has ever been a major issue. However, commonly used non-adaptive filters can do noise reduction at the cost of a reduced effective spatial resolution only. Here we present a new package adimpro for R, which implements the Propagation-Separation approach by Polzehl and Spokoiny (2006) for smoothing digital images. This method naturally adapts to different structures of different size in the image and thus avoids oversmoothing edges and fine structures. We extend the method for imaging data with spatial correlation. Furthermore we show how the estimation of the dependence between variance and mean value can be included. We illustrate the use of the package through some examples.