Abstract. Mixed phase clouds (MPCs) represent a great source of uncertainty for both climate predictions and weather forecasts. In particular, there is still a lack of under- standing on how ice forms in these clouds. In this work we present a technique to analyze in situ measurements of MPCs performed with the latest instruments from the Small Ice De- tector family. These instruments record high-resolution scat- tering patterns of individual small cloud particles. For the analysis of the scattering patterns we developed an algorithm that can discriminate the phase of the cloud particles. In the case of a droplet, a Mie solution is fitted to the recorded pat- tern and the size of the corresponding particle is obtained, which allows for a size calibration of the instrument. In the case of an ice particle, its shape is deduced from the scatter- ing pattern.
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isopropanol solution and stirred until a homogenoues mixture was obtained. The mixture was then added dropwise into distilled water and vigorously stirred for several minutes. After that, the nitric acid was added into the mixture and vigorously stirred for about 30 min. The prepared mixture was aged in tight air for several days until the formation of white sol-gel was observed. The white sol-gel was then dried at 75ºC for 74 h in vacuum oven until white powder was obtained. The dried powder was ground to get fine powder and denoted as T75. In order to study the mixed phase of anatase/rutile TiO 2 formation,
1991; Kaul et al., 2004; Fujii and Fukuchi, 2005; Weitkamp, 2005; Freudenthaler et al., 2009; Hayman and Thayer, 2012; Groß et al., 2015). The utility of lidar observations can be en- hanced by using complementary measurements that grant a more complete perspective such as cloud radars, microwave radiometers, and radiosondes as done for programs like the Surface Heat Budget of the Arctic Ocean (SHEBA) (Shupe et al., 2006), the Department of Energy Atmospheric Radia- tion Measurement (ARM) program’s atmospheric observato- ries (Verlinde et al., 2016), and Mixed Phase Arctic Clouds Experiment (MPACE) (Verlinde et al., 2007). Despite its util- ity, polarimetric lidar has limitations. Among them is the stringent requirement of linear signal operation over a large dynamic range. If not properly designed or considered, mea- surements can be misinterpreted casting doubt on critical measurements like cloud phase (Hayman and Thayer, 2009; Liu et al., 2009; Neely et al., 2013). For example, traditional two-channel orthogonal polarization measurements using co- polarized and cross-polarized signals can not unambiguously separate systematic polarization effects and geophysical ef- fects (Biele et al., 2000; Alvarez et al., 2006; Hayman and Thayer, 2009). These measurement errors result in cloud- phase misidentification, which, in turn, introduce unquanti- fied errors into observationally based understanding of key cloud and radiative processes. Observations by lidar of Arc- tic liquid-only and mixed-phase clouds in particular are chal- lenging due to their high optical thicknesses, relative to ice- only clouds, and low-lying altitude, which demands large system dynamic ranges.
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Abstract. Knowledge of cloud phase (liquid, ice, mixed, etc.) is necessary to describe the radiative impact of clouds and their lifetimes, but is a property that is difficult to simulate correctly in climate models. One step towards improving those simulations is to make observations of cloud phase with sufficient accuracy to help constrain model representations of cloud processes. In this study, we outline a methodology using a basic Bayesian classifier to estimate the probabilities of cloud-phase class from Atmospheric Radiation Measurement (ARM) vertically pointing active remote sensors. The advantage of this method over previous ones is that it provides uncertainty information on the phase classification. We also test the value of including higher moments of the cloud radar Doppler spectrum than are traditionally used operationally. Using training data of known phase from the Mixed-Phase Arctic Cloud Experiment (M-PACE) field campaign, we demonstrate a proof of concept for how the method can be used to train an algorithm that identifies ice, liquid, mixed phase, and snow. Over 95 % of data are identified correctly for pure ice and liquid cases used in this study. Mixed-phase and snow cases are more problematic to identify correctly. When lidar data are not available, including additional information from the Doppler spectrum provides substantial improvement to the algorithm. This is a first step towards an operational algorithm and can be expanded to include additional categories such as drizzle with additional training data.
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increased from 44 pC/N for KBT to a maximum of 130 pC/N at x = 0.1, decreasing to ~ 100 pC/N for x = 0.2 and then dropping sharply at x > 0.2, Figure 8. Hence the optimum ferroelectric and piezoelectric properties in KBT-BZT occur around the changeover in phase content from tetragonal to mixed phase (tetragonal and pseudocubic) at x = 0.1. A future detailed crystallographic study would clarify the symmetry of the ferroelectric phase(s) in this region.
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One of the main goals of the future heavy-ion collider NICA in Dubna will be the search for signals of the phase transition between hadronic matter and quark-gluon plasma and search for new phases of baryonic matter, including the mixed phase. However, up to date one still has no full understanding for the state of dense and hot hadronic matter which is born in heavy-ion collisions, its phase and temperature distribution, etc. There is a key hypothesis about partial chiral symmetry restoration (CSR) in hot and/or dense nuclear matter inside the ﬁreball, which is a consequence of basic principles of QCD. The CSR eﬀect can be observed in two areas: (i) in a single hadron when it gets to be highly excited [1, 2] and (ii) in hot and/or dense nuclear matter, i.e., in medium [3–6]. In the latter case CSR occurs due to renormalization of hadron and σ-meson propagators in nuclear medium.
We report an effective method for producing graphene sheets using solvothermal-assisted exfoliation of graphite in a mixed solvent of toluene and oleylamine. The mixed solvent of toluene and oleylamine produces higher yield of graphene than its constituents, oleylamine and toluene. The oleylamine molecules with its long chain enwrap the graphene sheets efficiently, while toluene helps the oleylamine molecules become more flexible and easily intercalate into the edge of graphite. The prepared graphene sheets have a high quality, and the concentration of graphene in the dispersion is as high as 0.128 mg mL − 1 . The high-quality graphene sheets obtained in this work make them suitable for application in many fields such as energy-storage materials and polymer composites.
al. 6 studied ice nucleation by several montmorillonite samples. Three of these samples (M SWy-2, M KSF and M K-10) contain K-feldspar and had higher freezing temperatures than the M STx-1b sample which did not contain measurable quantities of feldspar. Arizona test dust had the highest onset freezing temperature of any mixed-mineral dust proxy 10 and also contains the most K-feldspar (20 wt%). In general, the more feldspar a sample contains the higher the freezing temperature. We hypothesise that that the feldspar component controlled the nucleation of ice in these experiments, highlighting the need to characterise sample mineralogy in such work.
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The study has shown that the synthesized mixed iron oxide has a good fluoride adsorption capability. Studies were carried out to remove fluoride from water samples collected from paddy fields at Atri (Khurda, Bhubaneswar, Orissa) hot spring containing 10.25 mg/L fluoride. The pH of this contaminated water was 7.75. The chemical analysis of the water sample showed it to contain 9.6, 11.2, 269.7 and 148.9 mg/L of Ca, Mg, Cl - and SO 4 2- , respectively. The experiments were carried out following stage wise removal
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Portions of stock solutions of Chromium (VI) varying from 0.1 to 1.0 mL with a 0.1-mL step, a 2.5 mL portion of a 0.01 M solution of HBTP, and a 2.0 mL portion of a 0.01M solution of Am were placed in to calibrated test tubes with ground- glass stoppers (the volume of the organic phase was 5 mL). The required value of pH was adjusted by adding 0.1M HCl. The volume of the aqueous phase was increased to 20 mL using distilled water. In 10 minnute after the complete separation of the phases, the organic phase was separated from the aqueous phase
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The mobile app will contain essential diabetes management features for tracking blood glucose, physical exercise, diet, in addition to other preferred features that may be identi ﬁ ed from the mixed methods study. Furthermore, the app will have personalized educational features that provide com- puted algorithm feedback messages in response to reported blood glucose measurements. Several separate feedback messages will be developed and input into the app to be controlled by a decision-based system. Rules will be set, stipulating requirements for messages to be delivered to patients. These requirements will be speci ﬁ c to the indicated type of diabetes, value of blood glucose (whether within or beyond the standard recommended range), and the period of blood glucose measurement (either fasting or 2 hours post- prandial). In addition, regular randomization to guard against unnecessary repetition of the same message will be integrated into the decision-based system. These mes- sages are evidence-based, motivational, health promotional, and behavioral skills information aimed at supporting self- management practices. For example, where a type 1 DM participant logged a fasting BG of 6 mmol/L, feedback message may include “ Excellent: BG appears within recom- mended target range, continue your medication as pre- scribed “ , or “ Excellent . . . keeping BG levels within target range reduces the risk of complications ” . In situations where the inputted BG value is in the range of emergency hyperglycemia ( ≥ 14 mmol/L) consistently, an immediate contact with the patient ’ s health provider will be recom- mended. The intervention group will be instructed to log their blood glucose measurements into the app twice weekly at a minimum. Data logged into the app will be automati- cally transferred to a secured, password protected cloud
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Squeeze casting infiltration is a forced infiltration method of liquid phase fabrication of metal matrix composites, using a ram for applying pressure on the molten metal and forcing it to penetrate into a dispersed phase, placed into the lower fixed mold part. Infiltration method is similar to the squeeze casting technique used for metal alloys casting. Figure 1.3 shows the Schematic view of squeeze casting Infiltration.
The above solution was filtered through whatmann filter paper (0.45µ). This solution is expected to contain PSE - 12000 µg/ml and LOR - 500µg/ml. From this, 5 ml of aliquot was taken and transferred to volumetric flask of 100 ml capacity and volume was made up to the mark with the Diluent to give a solution containing 600 µg/ml PSE and 25 µg/ml LOR. From this solution further diluted 5.0 ml to 20 ml with mobile phase to prepare solution containing 150 µg/ml PSE and 6.25 µg/ml LOR. This solution was used for the estimation of PSE and LOR.
The traditional direction of arrival (DOA) estimation originates from 1960s; it is usually used in radar [1–5], underwater detection [6–8], and mobile communica- tion [9–15]. Generally speaking, most of the direction finding algorithms need to know the accurate array manifold, and they are very sensitive to the errors in the sensor channels. However, due to the present pro- cessing technology, perturbations in applications are often inevitable, such as temperature, humidity, shake, and device aging, all of them will lead to the estimation performance deterioration. The main errors in array signal processing include mutual coupling, gain-phase uncertainty, and sensor position errors, so the array re- quires to be calibrated.
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mate of the INP concentrations (Cziczo et al., 2013; Murray et al., 2012; Atkinson et al., 2013; O’Sullivan et al., 2014; Tobo et al., 2014). This sort of quantification can give an in- sight into the importance of INPs from different sources and potentially allows us to assess changes in INP concentrations due to human activities. Unfortunately, such an estimate is not possible for combustion ashes, because we have a very limited knowledge of the atmospheric abundance of combus- tion ashes. Some of the limiting factors that lead to unavail- ability of data are linked to a lack of airborne and ground measurements of combustion ash particles, and the difficulty in differentiating mineral dust and combustion ashes in the atmosphere. For example, in some ice crystal residue analy- ses natural mineral dusts, fly ashes, volcanic ashes and oth- ers are often classed into a single category (Richardson et al., 2007; Baustian et al., 2012; Kamphus et al., 2010; Fried- man et al., 2013; Cziczo et al., 2004). Some studies only used dust markers to class such aerosols as mineral dust (e.g. Pratt et al., 2009) or grouped aerosol particles as sim- ply mixed or industrial (Pratt et al., 2010). Similar comments have been made by Wang et al. (2013) about the incorrect ap- portionment of coal combustion particles in the atmosphere to biomass burning sources.
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Highly polycrystalline and pure delafossite phase CuAlO 2 powder has been synthesised within a short annealing period, shorter than most conventional processes. This is an improvement over the conventional synthesis procedures. Conventional synthesis procedure has seen CuAlO 2 only formed at high annealing temperatures ≥ 1100 °C over long annealing time, some as long as 96 hours. In the current process, a pure phase devoid of impurities has been obtained at reduced calcination time of 1.5 hours in an argon atmosphere at a temperature of 1150 °C. This was confirmed by XRD and SEM/EDX. High temperature DC/AC electrical measurements show a change in conduction mechanism from mixed conductivity (ionic + p-type) in the temperature range of 375 ≥ T ≥ 25 °C to intrinsic type behavior above 375 °C. The activation energies for these two regimes are 0.27 eV and 0.08 eV respectively. This change from mixed to DC conductivity is confirmed by spectral analysis too. Spectral analysis using the power law also revealed that conduction is of long range hopping. Use of platinum as a contact electrode at elevated temperatures has a detrimental effect on the electrical properties since it encourages the formation of CuAl 2 O 4 at the interface due to the formation of more stable Cu−Pt alloy by virtue of the chemical reaction Pt + 2CuAlO 2 → − CuAl 2 O 4 + Pt Cu .
Dense mixed ionic-electronic conducting (MIEC) membranes consisting of ionic conductive perovskite-type and/or fluorite-type oxides and high electronic conductive spinel type oxides, at elevated temperature can play a useful role in a number of energy conversion related systems including the solid oxide fuel cell (SOFC), oxygen separation and permeation membranes, partial oxidization membrane reactors for natural gas processing, high temperature electrolysis cells, and others. This study will investigate the impact of different heterogeneous characteristics of dual phase ionic and electronic conductive oxygen separation membranes on their transport mechanisms, in an attempt to develop a foundation for the rational design of such membranes. The dielectric behavior of a material can be an indicator for MIEC performance and can be incorporated into computational models of MIEC membranes in order to optimize the composition, microstructure, and ultimately predict long term membrane performance. The dielectric behavior of the MIECs can also be an indicator of the transport mechanisms and the parameters they are dependent upon.
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word, because the ion radii of Y 3? , Gd 3? and La 3? are 88, 93.8 and 106.1 pm, respectively, the radii difference between rare earth ions strongly affects the crystal phase and microstructure the mixed rare earth phosphates. The dif- ference in radius between Y 3? and La 3? is so large that it is not easy to form the product of the single phase. Addition- ally, the difference in radius between La 3? and Gd 3? (Gd 3? and Y 3? ) is smaller than that of Y 3? and La 3? , so the product can present the pure phase with the different content ratio of the rare earth ions. Besides, the calculated grain sizes of these samples are in the range of 12–40 nm using Scherrer’s equation, which delegates the dimension in the normal direction of (111) plane.
One nebulizer will contain a solution of the reactions’ Limiting-Reagent and the other will contain the Ex- cess-Reagent. The conducting containers of the nebuliz- ers’ aerosol will be the electrodes that charge their solu- tions oppositely and the drops leaving the surface of so- lutions will be entrained in the inert carrier gas. This will produce aerosols containing oppositely charged, mono- disperse drops (see Section 3). The charged aerosols will be transported through the apparatus using the inert car- rier gas, Helium and will be mixed in a Reaction-Phase Chamber.
(3.5), second, the summations in equation (5.11) have been replaced with an integral. It can be seen that for larger α increasing N does relatively little to change the damping rate. Figure 9 uses a range of α values including 1.5 and 5/3 as these are the values predicted from MHD turbulence the- ory (Bruno & Carbone 2016). Morton et al. (2016) provides observations of the power spectra of velocity fluctuations in the quiet sun, active regions and coronal holes. They find that the slope varies from α = 1 to α = 1.53 for higher frequencies, although they are only able to measure up to frequencies of around 10 −2 Hz. Podesta et al. (2007) mea- sure the power spectra in the solar wind and can measure up to 10 −1 Hz and find the slope to be between α = 1.5 and α = 5/3. From Figure 2, it can be seen that for the higher frequencies, the heating due to parallel gradients start to dominate. Therefore, if we use a value of N of higher than 100, we can see that parallel gradients will start to dominate the heating. Hence, we can no longer describe the system as being heated mainly by phase mixing.
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