10 research outputs found
All Optical Implementation of Multi-Spin Entanglement in a Semiconductor Quantum Well
We use ultrafast optical pulses and coherent techniques to create spin
entangled states of non-interacting electrons bound to donors (at least three)
and at least two Mn2+ ions in a CdTe quantum well. Our method, relying on the
exchange interaction between localized excitons and paramagnetic impurities,
can in principle be applied to entangle a large number of spins.Comment: 17 pages, 3 figure
Identification des sources dâĂ©mission des polluants organiques persistants dans les vallĂ©es alpines par lâutilisation de nouveau traceurs
National audienc
Air Quality and Biomass Burning: Active air-quality research in-put to engage quickly field actions
International audienc
Evaluation of receptor and chemical transport models for PM10 source apportionment
In this study, the performance of two types of source apportionment models was evaluated by assessing the results provided by 40 different groups in the framework of an intercomparison organised by FAIRMODE WG3 (Forum for air quality modelling in Europe, Working Group 3). The evaluation was based on two performance indicators: z-scores and the root mean square error weighted by the reference uncertainty (RMSEu), with pre-established acceptability criteria. By involving models based on completely different and independent input data, such as receptor models (RMs) and chemical transport models (CTMs), the intercomparison provided a unique opportunity for their cross-validation. In addition, comparing the CTM chemical profiles with those measured directly at the source contributed to corroborate the consistency of the tested model results. The most commonly used RM was the US EPA- PMF version 5. RMs showed very good performance for the overall dataset (91% of z-scores accepted) while more difficulties were observed with the source contribution time series (72% of RMSEu accepted). Industrial activities proved to be the most difficult sources to be quantified by RMs, with high variability in the estimated contributions. In the CTMs, the sum of computed source contributions was lower than the measured gravimetric PM10 mass concentrations. The performance tests pointed out the differences between the two CTM approaches used for source apportionment in this study: brute force (or emission reduction impact) and tagged species methods. The sources meeting the z-score and RMSEu acceptability criteria tests were 50% and 86%, respectively. The CTM source contributions to PM10 were in the majority of cases lower than the RM averages for the corresponding source. The CTMs and RMs source contributions for the overall dataset were more comparable (83% of the z-scores accepted) than their time series (successful RMSEu in the range 25% - 34%). The comparability between CTMs and RMs varied depending on the source: traffic/exhaust and industry were the source categories with the best results in the RMSEu tests while the most critical ones were soil dust and road dust. The differences between RMs and CTMs source reconstructions confirmed the importance of cross validating the results of these two families of modelsIn this study, the performance of two types of source apportionment models was evaluated by assessing the results provided by 40 different groups in the framework of an intercomparison organised by FAIRMODE WG3 (Forum for air quality modelling in Europe, Working Group 3). The evaluation was based on two performance indicators: z-scores and the root mean square error weighted by the reference uncertainty (RMSEu), with pre-established acceptability criteria. By involving models based on completely different and independent input data, such as receptor models (RMs) and chemical transport models (CTMs), the intercomparison provided a unique opportunity for their cross-validation. In addition, comparing the CTM chemical profiles with those measured directly at the source contributed to corroborate the consistency of the tested model results. The most commonly used RM was the US EPA- PMF version 5. RMs showed very good performance for the overall dataset (91% of z-scores accepted) while more difficulties were observed with the source contribution time series (72% of RMSEu accepted). Industrial activities proved to be the most difficult sources to be quantified by RMs, with high variability in the estimated contributions. In the CTMs, the sum of computed source contributions was lower than the measured gravimetric PM10 mass concentrations. The performance tests pointed out the differences between the two CTM approaches used for source apportionment in this study: brute force (or emission reduction impact) and tagged species methods. The sources meeting the z-score and RMSEu acceptability criteria tests were 50% and 86%, respectively. The CTM source contributions to PM10 were in the majority of cases lower than the RM averages for the corresponding source. The CTMs and RMs source contributions for the overall dataset were more comparable (83% of the z-scores accepted) than their time series (successful RMSEu in the range 25% - 34%). The comparability between CTMs and RMs varied depending on the source: traffic/exhaust and industry were the source categories with the best results in the RMSEu tests while the most critical ones were soil dust and road dust. The differences between RMs and CTMs source reconstructions confirmed the importance of cross validating the results of these two families of models
Fermented Cassava Residue Lignin Prepared by Sequential Acid Steam-Explosion and Hot-Alkaline Treatment and Its Antioxidant Properties
Tyrosinase inhibition organic phase biosensor for triazinic and benzotriazinic pesticide analysis (part two)
Characterization of particulate polycyclic aromatic hydrocarbons in the east of France urban areas
Microalgal Biomass of Industrial Interest: Methods of Characterization
International audienceMicroalgae represent a new source of biomass for many applications. The advantage of microalgae over higher plants is their high productivities. The photoautotrophic microalgae include all photosynthetic microorganisms, i.e. Cyanobacteria (prokaryotes) or microalgae (eukaryotes). These microorganisms are characterized by a large biodiversity and chimiodiversity. Then, the analysis of microalgal and cyanobacterial biomass often needs specific adaptations of the classical protocols for extraction as well as for quantification of their contents. This chapter reviewed the main analytical methods used for the analysis of microalgae biomass and its main vaporizable compounds: proteins, polysaccharides, lipids, pigments and secondary metabolites