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- Coroaba, Adina, et al.
(författare)
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Probing the supramolecular features via π–π interaction of a di-iminopyrene-di-benzo-18-crown-6-ether compound : experimental and theoretical study
- 2020
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Ingår i: RSC Advances. - : Royal Society of Chemistry (RSC). - 2046-2069. ; 10:63, s. 38304-38315
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Tidskriftsartikel (refereegranskat)abstract
- A novel DPyDB-CN-18C6 compound was synthesised by linking a pyrene moiety to each phenyl group of dibenzo-18-crown-6-ether, the crown ether, through –HCN– bonds and characterized by FTIR, 1H-NMR, 13C-NMR, TGA, and DSC techniques. The quantitative 13C-NMR analysis revealed the presence of two position isomers. The electronic structure of the DPyDB-CN-18C6 molecule was characterized by UV-vis and fluorescence spectroscopies in four solvents with different polarities to observe particular behavior of isomers, as well as to demonstrate a possible non-bonding chemical association (such as ground- and excited-state associations, namely, to probe if there were forming dimers/excimers). The interpretation of the electronic structure was realized through QM calculations. The TD-CAM-B3LYP functional, at the 6-311+G(d,p) basis set, indicated the presence of predominant π → π* and mixed π → π* + n → π* transitions, in line with the UV-vis experimental data. Even though DPyDB-CN-18C6 computational studies revealed a π-extended conjugation effect with predominantly π → π* transitions, thorough fluorescence analysis was observed a weak emission, as an effect of PET and ACQ. In particular, the WAXD analysis of powder and thin films obtained from n-hexane, 1,2-dichloroethane, and ethanol indicated an amorphous organization, whereas from toluene a smectic ordering was obtained. These results were correlated with MD simulation, and it was observed that the molecular geometry of DPyDB-CN-18C6 molecule played a defining role in the pyrene stacking arrangement.
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- Levin, Ingeborg, et al.
(författare)
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A dedicated flask sampling strategy developed for Integrated Carbon Observation System (ICOS) stations based on CO2 and CO measurements and Stochastic Time-Inverted Lagrangian Transport (STILT) footprint modelling
- 2020
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Ingår i: Atmospheric Chemistry and Physics. - : Copernicus GmbH. - 1680-7316 .- 1680-7324. ; 20:18, s. 11161-11180
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Tidskriftsartikel (refereegranskat)abstract
- In situ CO2 and CO measurements from five Integrated Carbon Observation System (ICOS) atmosphere stations have been analysed together with footprint model runs from the regional Stochastic Time-Inverted Lagrangian Transport (STILT) model to develop a dedicated strategy for flask sampling with an automated sampler. Flask sampling in ICOS has three different purposes, namely (1) to provide an independent quality control for in situ observations, (2) to provide representative information on atmospheric components currently not monitored in situ at the stations, and (3) to collect samples for 14CO2 analysis that are significantly influenced by fossil fuel CO2 ( ffCO2 ) emission areas. Based on the existing data and experimental results obtained at the Heidelberg pilot station with a prototype flask sampler, we suggest that single flask samples are collected regularly every third day around noon or in the afternoon from the highest level of a tower station. Air samples shall be collected over 1 h, with equal temporal weighting, to obtain a true hourly mean. At all stations studied, more than 50 % of flasks collected around midday will likely be sampled during low ambient variability ( <0.5 parts per million (ppm) standard deviation of 1 min values). Based on a first application at the Hohenpeißenberg ICOS site, such flask data are principally suitable for detecting CO2 concentration biases larger than 0.1 ppm with a 1 σ confidence level between flask and in situ observations from only five flask comparisons. In order to have a maximum chance to also sample ffCO2 emission areas, additional flasks are collected on all other days in the afternoon. To check if the ffCO2 component will indeed be large in these samples, we use the continuous in situ CO observations. The CO deviation from an estimated background value is determined the day after each flask sampling, and depending on this offset, an automated decision is made as to whether a flask shall be retained for 14CO2 analysis. It turned out that, based on existing data, ffCO2 events of more than 4-5 ppm that would allow ffCO2 estimates with an uncertainty below 30 % were very rare at all stations studied, particularly in summer (only zero to five events per month from May to August). During the other seasons, events could be collected more frequently. The strategy developed in this project is currently being implemented at the ICOS stations.
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- Loni, Mohammad, et al.
(författare)
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DenseDisp : Resource-Aware Disparity Map Estimation by Compressing Siamese Neural Architecture
- 2020
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Ingår i: IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE (WCCI) 2020 IEEE WCCI. - Glasgow, United Kingdom.
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Konferensbidrag (refereegranskat)abstract
- Stereo vision cameras are flexible sensors due to providing heterogeneous information such as color, luminance, disparity map (depth), and shape of the objects. Today, Convolutional Neural Networks (CNNs) present the highest accuracy for the disparity map estimation [1]. However, CNNs require considerable computing capacity to process billions of floating-point operations in a real-time fashion. Besides, commercial stereo cameras produce huge size images (e.g., 10 Megapixels [2]), which impose a new computational cost to the system. The problem will be pronounced if we target resource-limited hardware for the implementation. In this paper, we propose DenseDisp, an automatic framework that designs a Siamese neural architecture for disparity map estimation in a reasonable time. DenseDisp leverages a meta-heuristic multi-objective exploration to discover hardware-friendly architectures by considering accuracy and network FLOPS as the optimization objectives. We explore the design space with four different fitness functions to improve the accuracy-FLOPS trade-off and convergency time of the DenseDisp. According to the experimental results, DenseDisp provides up to 39.1x compression rate while losing around 5% accuracy compared to the state-of-the-art results.
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