61 research outputs found

    Finding related sentence pairs in MEDLINE

    Get PDF
    We explore the feasibility of automatically identifying sentences in different MEDLINE abstracts that are related in meaning. We compared traditional vector space models with machine learning methods for detecting relatedness, and found that machine learning was superior. The Huber method, a variant of Support Vector Machines which minimizes the modified Huber loss function, achieves 73% precision when the score cutoff is set high enough to identify about one related sentence per abstract on average. We illustrate how an abstract viewed in PubMed might be modified to present the related sentences found in other abstracts by this automatic procedure

    Ammonia-Nitrogen Recovery from Synthetic Solution using Agricultural Waste Fibers

    Get PDF
    In this study, modification of Empty Fruit Bunch (EFB) fibers as a means to recover ammonianitrogen from a synthetic solution was investigated. Methods: The EFB fiber was modified using sodium hydroxide.Adsorption-desorption studies of ammonia nitrogen into the modified EFB fiber were investigated Findings: Theincrease in adsorption capacity was found to be proportional with the increase of pH up to 7, temperature and ammoniaconcentration. The maximum adsorption capacity is 0.53-10.89 mg/g. The attachment of ammonia nitrogen involves ionexchange-chemisorption. The maximum desorption capacity of 0.0999 mg/g. Applications: This study can be used as abaseline for designing a low cost adsorbent system for ammonia nitrogen recovery drainage and industrial wastewater aswell as EFBs-palm oil mill effluent composting

    Stochastic upscaling of hydrodynamic dispersion and retardation factor in a physically and chemically heterogeneous tropical soil

    Full text link
    [EN] Stochastic upscaling of flow and reactive solute transport in a tropical soil is performed using real data collected in the laboratory. Upscaling of hydraulic conductivity, longitudinal hydrodynamic dispersion, and retardation factor were done using three different approaches of varying complexity. How uncertainty propagates after upscaling was also studied. The results show that upscaling must be taken into account if a good reproduction of the flow and transport behavior of a given soil is to be attained when modeled at larger than laboratory scales. The results also show that arrival time uncertainty was well reproduced after solute transport upscaling. This work represents a first demonstration of flow and reactive transport upscaling in a soil based on laboratory data. It also shows how simple upscaling methods can be incorporated into daily modeling practice using commercial flow and transport codes.The authors thank the financial support by the Brazilian National Council for Scientific and Technological Development (CNPq) (Project 401441/2014-8). The doctoral fellowship award to the first author by the Coordination of Improvement of Higher Level Personnel (CAPES) is acknowledged. The first author also thanks the international mobility grant awarded by CNPq, through the Sciences Without Borders program (Grant Number: 200597/2015-9). The international mobility grant awarded by Santander Mobility in cooperation with the University of Sao Paulo is also acknowledged. DHI-WASI is gratefully thanked for providing a FEFLOW license.Almeida De-Godoy, V.; Zuquette, L.; Gómez-Hernández, JJ. (2019). Stochastic upscaling of hydrodynamic dispersion and retardation factor in a physically and chemically heterogeneous tropical soil. Stochastic Environmental Research and Risk Assessment. 33(1):201-216. https://doi.org/10.1007/s00477-018-1624-zS201216331Ahuja LR, Naney JW, Green RE, Nielsen DR (1984) Macroporosity to characterize spatial variability of hydraulic conductivity and effects of land management. Soil Sci Soc Am J 48:699. https://doi.org/10.2136/sssaj1984.03615995004800040001xBellin A, Lawrence AE, Rubin Y (2004) Models of sub-grid variability in numerical simulations of solute transport in heterogeneous porous formations: three-dimensional flow and effect of pore-scale dispersion. Stoch Environ Res Risk Assess 18:31–38. https://doi.org/10.1007/s00477-003-0164-2Brent RP (1973) Algorithms for minimization without derivatives. Prentice Hall, Englewood CliffsBrusseau ML (1998) Non-ideal transport of reactive solutes in heterogeneous porous media: 3. model testing and data analysis using calibration versus prediction. J Hydrol 209:147–165. https://doi.org/10.1016/S0022-1694(98)00121-8Brusseau ML, Srivastava R (1999) Nonideal transport of reactive solutes in heterogeneous porous media: 4. Analysis of the cape cod natural-gradient field experiment. Water Resour Res 35:1113–1125. https://doi.org/10.1029/1998WR900019Brutsaert W (1967) Some methods of calculating unsaturated permeability. Trans ASAE 10:400–404Cadini F, De Sanctis J, Bertoli I, Zio E (2013) Upscaling of a dual-permeability Monte Carlo simulation model for contaminant transport in fractured networks by genetic algorithm parameter identification. Stoch Environ Res Risk Assess 27:505–516. https://doi.org/10.1007/s00477-012-0595-8Cambardella CA, Moorman TB, Parkin TB, Karlen DL, Novak JM, Turco RF, Konopka AE (1994) Field-scale variability of soil properties in central iowa soils. Soil Sci Soc Am J 58:1501. https://doi.org/10.2136/sssaj1994.03615995005800050033xCapilla JE, Rodrigo J, Gómez-Hernández JJ (1999) Simulation of non-Gaussian transmissivity fields honoring piezometric data and integrating soft and secondary information. Math Geol 31:907–927. https://doi.org/10.1023/A:1007580902175Cassiraga EF, Fernàndez-Garcia D, Gómez-Hernández JJ (2005) Performance assessment of solute transport upscaling methods in the context of nuclear waste disposal. Int J Rock Mech Min Sci 42:756–764. https://doi.org/10.1016/j.ijrmms.2005.03.013Corey AT (1977) Mechanics of heterogeneous fluids in porous media. Water Resources Publications, Fort Collins, CO, p 259Dagan G (1989) Flow and transport in porous formations. Springer, Berlin. https://doi.org/10.1007/978-3-642-75015-1Dagan G (2004) On application of stochastic modeling of groundwater flow and transport. Stoch Environ Res Risk Assess. https://doi.org/10.1007/s00477-004-0191-7de Azevedo AAB, Pressinotti MMN, Massoli M (1981) Sedimentological studies of the Botucatu and Pirambóia formations in the region of Santa Rita do Passa Quatro (In portuguese). Rev do Inst Geológico 2:31–38. https://doi.org/10.5935/0100-929X.19810003Deng H, Dai Z, Wolfsberg AV, Ye M, Stauffer PH, Lu Z, Kwicklis E (2013) Upscaling retardation factor in hierarchical porous media with multimodal reactive mineral facies. Chemosphere 91:248–257. https://doi.org/10.1016/j.chemosphere.2012.10.105Diersch H-JG (2014) Finite element modeling of flow, mass and heat transport in porous and fractured media. Springer, Berlin. https://doi.org/10.1007/978-3-642-38739-5Dippenaar MA (2014) Porosity reviewed: quantitative multi-disciplinary understanding, recent advances and applications in vadose zone hydrology. Geotech Geol Eng 32:1–19. https://doi.org/10.1007/s10706-013-9704-9Fagundes JRT, Zuquette LV (2011) Sorption behavior of the sandy residual unconsolidated materials from the sandstones of the Botucatu Formation, the main aquifer of Brazil. Environ Earth Sci 62:831–845. https://doi.org/10.1007/s12665-010-0570-yFenton GA, Griffiths DV (2008) Risk assessment in geotechnical engineering. Wiley, p 463Fernàndez-Garcia D, Gómez-Hernández JJ (2007) Impact of upscaling on solute transport: Traveltimes, scale dependence of dispersivity, and propagation of uncertainty. Water Resour Res. https://doi.org/10.1029/2005WR004727Fernàndez-Garcia D, Llerar-Meza G, Gómez-Hernández JJ (2009) Upscaling transport with mass transfer models: mean behavior and propagation of uncertainty. Water Resour Res. https://doi.org/10.1029/2009WR007764Feyen L, Gómez-Hernández JJ, Ribeiro PJ, Beven KJ, De Smedt F (2003a) A Bayesian approach to stochastic capture zone delineation incorporating tracer arrival times, conductivity measurements, and hydraulic head observations. Water Resour Res. https://doi.org/10.1029/2002WR001544Feyen L, Ribeiro PJ, Gómez-Hernández JJ, Beven KJ, De Smedt F (2003b) Bayesian methodology for stochastic capture zone delineation incorporating transmissivity measurements and hydraulic head observations. J Hydrol 271:156–170. https://doi.org/10.1016/S0022-1694(02)00314-1Forsythe GE, Malcolm MA, Moler CB (1976) Computer methods for mathematical computations. Prentice-Hall, Englewood Cliffs, p 259Freeze R, Cherry J (1979) Groundwater. PrenticeHall Inc, Englewood cliffs, p 604Frippiat CC, Holeyman AE (2008) A comparative review of upscaling methods for solute transport in heterogeneous porous media. J Hydrol 362:150–176. https://doi.org/10.1016/j.jhydrol.2008.08.015Fu J, Gómez-Hernández JJ (2009) Uncertainty assessment and data worth in groundwater flow and mass transport modeling using a blocking Markov chain Monte Carlo method. J Hydrol 364:328–341. https://doi.org/10.1016/j.jhydrol.2008.11.014Gelhar LW, Axness CL (1983) Three-dimensional stochastic analysis of macrodispersion in aquifers. Water Resour Res 19:161–180. https://doi.org/10.1029/WR019i001p00161Gelhar LW, Welty C, Rehfeldt KR (1992) A critical review of data on field-scale dispersion in aquifers. Water Resour Res 28:1955–1974. https://doi.org/10.1029/92WR00607Giacheti HL, Rohm SA, Nogueira JB, Cintra JCA (1993) Geotechnical properties of the Cenozoic sediment (in protuguese). In: Albiero JH, Cintra JCA (eds) Soil from the interior of São Paulo. ABMS, Sao Paulo, pp 143–175Gómez-Hernandez JJ (1990) A stochastic approach to the simulation of block conductivity fields conditional upon data measured at a smaller scale. Stanford University, StanfordGómez-Hernández JJ, Gorelick SM (1989) Effective groundwater model parameter values: influence of spatial variabiity of hydraulic conductivity, leackance, and recharge. Water Resour Res 25:405–419Gómez-Hernández JJ, Journel A (1993) Joint sequential simulation of multigaussian fields. In: Geostatistics Tróia’92. pp 85–94. https://doi.org/10.1007/978-94-011-1739-5_8Gómez-Hernández JJ, Wen X-H (1994) Probabilistic assessment of travel times in groundwater modeling. Stoch Hydrol Hydraul 8:19–55. https://doi.org/10.1007/BF01581389Gómez-Hernández JJ, Fu J, Fernandez-Garcia D (2006) Upscaling retardation factors in 2-D porous media. In: Bierkens MFP, Gehrels JC, Kovar K (eds) Calibration and reliability in groundwater modelling: from uncertainty to decision making: proceedings of the ModelCARE 2005 conference held in The Hague, The Netherlands, 6–9 June, 2005. IAHS Publication, pp 130–136Goovaerts P (1999) Geostatistics in soil science: state-of-the-art and perspectives. Geoderma 89:1–45. https://doi.org/10.1016/S0016-7061(98)00078-0Jarvis NJ (2007) A review of non-equilibrium water fl ow and solute transport in soil macropores: principles, controlling factors and consequences for water quality. Eur J Soil Sci 58:523–546. https://doi.org/10.4141/cjss2011-050Jellali S, Diamantopoulos E, Kallali H, Bennaceur S, Anane M, Jedidi N (2010) Dynamic sorption of ammonium by sandy soil in fixed bed columns: evaluation of equilibrium and non-equilibrium transport processes. J Environ Manag 91:897–905. https://doi.org/10.1016/j.jenvman.2009.11.006Journel AG, Gomez-Hernandez JJ (1993) Stochastic imaging of the wilmington clastic sequence. SPE Form Eval 8:33–40. https://doi.org/10.2118/19857-PAJournel A, Deutsch C, Desbarats A (1986) Power averaging for block effective permeability. Proc SPE Calif Reg Meet. https://doi.org/10.2118/15128-MSKronberg BI, Fyfe WS, Leonardos OH, Santos AM (1979) The chemistry of some Brazilian soils: element mobility during intense weathering. Chem Geol 24:211–229. https://doi.org/10.1016/0009-2541(79)90124-4Lake LW (1988) The origins of anisotropy (includes associated papers 18394 and 18458). J Pet Technol 40:395–396. https://doi.org/10.2118/17652-PALawrence AE, Rubin Y (2007) Block-effective macrodispersion for numerical simulations of sorbing solute transport in heterogeneous porous formations. Adv Water Resour 30:1272–1285. https://doi.org/10.1016/j.advwatres.2006.11.005Lemke LD, Barrack WA II, Abriola LM, Goovaerts P (2004) Matching solute breakthrough with deterministic and stochastic aquifer models. Groundwater 42:920–934Li L, Zhou H, Gómez-Hernández JJ (2011a) A comparative study of three-dimensional hydraulic conductivity upscaling at the macro-dispersion experiment (MADE) site, Columbus Air Force Base, Mississippi (USA). J Hydrol 404:278–293. https://doi.org/10.1016/j.jhydrol.2011.05.001Li L, Zhou H, Gómez-Hernández JJ (2011b) Transport upscaling using multi-rate mass transfer in three-dimensional highly heterogeneous porous media. Adv Water Resour 34:478–489. https://doi.org/10.1016/j.advwatres.2011.01.001Logsdon Keller KE, Moorman TB (2002) Measured and predicted solute leaching from multiple undisturbed soil columns. Soil Sci Soc Am J 66:686–695. https://doi.org/10.2136/sssaj2002.6860Lourens A, van Geer FC (2016) Uncertainty propagation of arbitrary probability density functions applied to upscaling of transmissivities. Stoch Environ Res Risk Assess 30:237–249. https://doi.org/10.1007/s00477-015-1075-8Mahapatra IC, Singh KN, Pillai KG, Bapat SR (1985) Rice soils and their management. Indian J Agron 30:R1–R41Morakinyo JA, Mackay R (2006) Geostatistical modelling of ground conditions to support the assessment of site contamination. Stoch Environ Res Risk Assess 20:106–118. https://doi.org/10.1007/s00477-005-0015-4Moslehi M, de Barros FPJ, Ebrahimi F, Sahimi M (2016) Upscaling of solute transport in disordered porous media by wavelet transformations. Adv Water Resour 96:180–189. https://doi.org/10.1016/j.advwatres.2016.07.013Osinubi KJ, Nwaiwu CM (2005) Hydraulic conductivity of compacted lateritic soil. J Geotech Geoenviron Eng 131:1034–1041. https://doi.org/10.1061/(ASCE)1090-0241(2005)131:8(1034)Remy N (2004) SGeMS: stanford geostatistical modeling software. Softw Man. https://doi.org/10.1007/978-1-4020-3610-1_89Renard P, de Marsily G (1997) Calculating equivalent permeability: a review. Adv Water Resour 20:253–278. https://doi.org/10.1016/S0309-1708(96)00050-4Robin MJL, Sudicky EA, Gillham RW, Kachanoski RG (1991) Spatial variability of strontium distribution coefficients and their correlation with hydraulic conductivity in the Canadian forces base borden aquifer. Water Resour Res 27:2619–2632. https://doi.org/10.1029/91WR01107Salamon P, Fernàndez-Garcia D, Gómez-Hernández JJ (2007) Modeling tracer transport at the MADE site: the importance of heterogeneity. Water Resour Res. https://doi.org/10.1029/2006WR005522Sánchez-Vila X, Carrera J, Girardi JP (1996) Scale effects in transmissivity. J Hydrol 183:1–22. https://doi.org/10.1016/S0022-1694(96)80031-XScheibe T, Yabusaki S (1998) Scaling of flow and transport behavior in heterogeneous groundwater systems. Adv Water Resour 22:223–238. https://doi.org/10.1016/S0309-1708(98)00014-1Selvadurai PA, Selvadurai APS (2014) On the effective permeability of a heterogeneous porous medium: the role of the geometric mean. Philos Mag 94:2318–2338. https://doi.org/10.1080/14786435.2014.913111Shackelford CD (1994) Critical concepts for column testing. J Geotech Eng 120:1804–1828. https://doi.org/10.1016/0148-9062(95)96996-OŠimůnek J, van Genuchten MT, Šejna M, Toride N, Leij FJ (1999) The STANMOD computer software for evaluating solute transport in porous media using analytical solutions of convection-dispersion equation. Riverside, CaliforniaTaskinen A, Sirviö H, Bruen M (2008) Modelling effects of spatial variability of saturated hydraulic conductivity on autocorrelated overland flow data: linear mixed model approach. Stoch Environ Res Risk Assess 22:67–82. https://doi.org/10.1007/s00477-006-0099-5Tuli A, Hopmans JW, Rolston DE, Moldrup P (2005) Comparison of air and water permeability between disturbed and undisturbed soils. Soil Sci Soc Am J 69:1361. https://doi.org/10.2136/sssaj2004.0332Tyukhova AR, Willmann M (2016) Conservative transport upscaling based on information of connectivity. Water Resour Res 52:6867–6880. https://doi.org/10.1002/2015WR018331van Genuchten MTh (1980) Determining transport parameters from solute displacement experiments. Research Report 118. U.S. Salinity Lab., Riverside, CAVanderborght J, Timmerman A, Feyen J (2000) Solute transport for steady-state and transient flow in soils with and without macropores. Soil Sci Soc Am J 64:1305–1317. https://doi.org/10.2136/sssaj2000.6441305xVanmarcke E (2010) Random fields: analysis and synthesis. World Scientific. MIT Press, Cambridge, MA, p 364Vishal V, Leung JY (2017) Statistical scale-up of 3D particle-tracking simulation for non-Fickian dispersive solute transport modeling. Environ Res Risk Assess, Stoch. https://doi.org/10.1007/s00477-017-1501-1Wen X-H, Gómez-Hernández JJ (1996) Upscaling hydraulic conductivities in heterogeneous media: an overview. J Hydrol 183:ix–xxxii. https://doi.org/10.1016/S0022-1694(96)80030-8Wen XH, Gómez-Hernández JJ (1998) Numerical modeling of macrodispersion in heterogeneous media: a comparison of multi-Gaussian and non-multi-Gaussian models. J Contam Hydrol 30:129–156. https://doi.org/10.1016/S0169-7722(97)00035-1Wen XH, Capilla JE, Deutsch CV, Gómez-Hernández JJ, Cullick AS (1999) A program to create permeability fields that honor single-phase flow rate and pressure data. Comput Geosci 25:217–230. https://doi.org/10.1016/S0098-3004(98)00126-5Wilding LP, Drees LR (1983) Spatial variability and pedology. In: Wilding LP, Smeck NE, Hall GF (eds) Pedogenesis and soil taxonomy: the soil orders. Elsevier, Amsterdam, pp 83–116Willmann M, Carrera J, Guadagnini A (2006) Block-upscaling of transport in heterogeneous aquifers. h2ogeo.upc.edu 1–7Xu Z, Meakin P (2013) Upscaling of solute transport in heterogeneous media with non-uniform flow and dispersion fields. Appl Math Model 37:8533–8542. https://doi.org/10.1016/j.apm.2013.03.070Zech A, Attinger S, Cvetkovic V, Dagan G, Dietrich P, Fiori A, Rubin Y, Teutsch G (2015) Is unique scaling of aquifer macrodispersivity supported by field data? Water Resour Res 51:7662–7679. https://doi.org/10.1002/2015WR017220Zhou H, Li L, Gómez-Hernández JJ (2010) Three-dimensional hydraulic conductivity upscaling in groundwater modeling. Comput Geosci 36:1224–1235. https://doi.org/10.1016/j.cageo.2010.03.008Zhou H, Li L, Hendricks Franssen H-J, Gómez-Hernández JJ (2012) Pattern recognition in a bimodal aquifer using the normal-score ensemble Kalman filter. Math Geosci 44:169–185. https://doi.org/10.1007/s11004-011-9372-

    Prevalence, associated factors and predictors of anxiety: a community survey in Selangor, Malaysia

    Get PDF
    Background: Anxiety is the most common mental health disorders in the general population. This study aimed to determine the prevalence of anxiety, its associated factors and the predictors of anxiety among adults in the community of Selangor, Malaysia. Methods: A cross sectional study was carried out in three districts in Selangor, Malaysia. The inclusion criteria of this study were Malaysian citizens, adults aged 18 years and above, and living in the selected living quarters based on the list provided by the Department of Statistics Malaysia (DOS). Participants completed a set of questionnaires, including the validated Malay version of Generalized Anxiety Disorder 7 (GAD 7) to detect anxiety. Results: Of the 2512 participants who were approached, 1556 of them participated in the study (61.90 %). Based on the cut-off point of 8 and above in the GAD-7, the prevalence of anxiety was 8.2 %. Based on the initial multiple logistic regression analysis, the predictors of anxiety were depression, serious problems at work, domestic violence and high perceived stress. When reanalyzed again after removing depression, low self-esteem and high perceived stress, six predictors that were identified are cancer, serious problems at work, domestic violence, unhappy relationship with family, non-organizational religious activity and intrinsic religiosity. Conclusion: This study reports the prevalence of anxiety among adults in the community of Selangor, Malaysia and also the magnitude of the associations between various factors and anxiety

    Chemical treatment of orange tree sawdust for a cationic dye enhancement removal from aqueous solutions: kinetic, equilibrium and thermodynamic studies

    No full text
    International audienceIn order to assess the potential use of low-cost materials for dye removal from aqueous solutions, the adsorption of cationic dye methylene blue (MB) onto orange tree sawdust was studied under static mode using raw sawdust (ROS) and chemically modified sawdust (MOS). The effect of several parameters such as contact time, initial dye concentrations, initial pH, adsorbent dose, and temperature were also investigated. Results showed that the adsorption kinetic data of MB onto both materials were well fitted by the second-order model and the equilibrium state was reached after 180 min of contact time. For both ROS and MOS, MB removal efficiency was improved by the increase in the initial aqueous concentrations, adsorbent dose, and aqueous pH. Moreover, MB adsorption data at equilibrium were well fitted by Langmuir model suggesting a probable monolayer adsorption process. The chemical treatment of the orange tree sawdust with sodium hydroxide (1 M) significantly increased the density of sorption sites and lead to the appearance of new functional groups. Therefore, MB removal capacity increased from about 40 mg/g for ROS to 111 mg/g for MOS at an initial pH value 6.0. The thermodynamic study demonstrated that MB adsorption was endothermic for ROS and spontaneous and exothermic for MOS, respectively. Desorption experiments with HNO3 acidic solutions proved that MB was significantly desorbed from the tested adsorbents, which offers a possible reusability. All these findings indicate that alkaline-treated orange tree sawdust could be employed as an efficient low-cost and eco-friendly adsorbent for cationic dye removal from industrial wastewaters. © 2015 Balaban Desalination Publications. All rights reserved

    Long term human primary hepatocyte cultures in a microfluidic liver biochip show maintenance of mRNA levels and higher drugs metabolisms when compared to Petri cultures.

    No full text
    International audienceHuman primary hepatocytes were cultivated in a microfluidic bioreactor and in Petri dishes for 13 days. mRNA kinetics in biochips showed an increase in the levels of CYP2B6, CYP2C19, CYP2C8, CYP3A4, CYP1A2, CYP2D6, HNF4a, SULT1A1, UGT1A1 mRNA related genes when compared with post extraction levels. In addition, comparison with Petri dishes showed higher levels of CYP2B6, CYP2C19, CYP2C8, CYP3A4, CYP1A2, CYP2D6 related genes at the end of culture. Functional assays illustrated a higher urea and albumin production over the period of culture in biochips. Bioreactor drug metabolism (midazolam and phenacetin) was not superior to the Petri dish after 2 days of culture. The CYP3A4 midazolam metabolism was maintained in biochips after 13 days of culture, whereas it was almost undetectable in Petri dishes. This led to a 5000-fold higher value of the metabolic ratio in the biochips. CYP1A2 phenacetin metabolism was found to be higher in biochips after 5, 9 and 13 days of culture. Thus, a 100-fold higher metabolic ratio of APAP in biochips was measured after 13 days of perfusion. These results demonstrated functional primary human hepatocyte culture in the bioreactor in a long-term culture

    Modeling the Translocation and Transformation of Chemicals in the Soil-Plant Continuum: A Dynamic Plant Uptake Module for the HYDRUS Model

    No full text
    Food contamination is responsible for thousands of deaths worldwide every year. Plants represent the most common pathway for chemicals into the human and animal food chain. Although existing dynamic plant uptake models for chemicals are crucial for the development of reliable mitigation strategies for food pollution, they nevertheless simplify the description of physicochemical processes in soil and plants, mass transfer processes between soil and plants and in plants, and transformation in plants. To fill this scientific gap, we couple a widely used hydrological model (HYDRUS) with a multicompartment dynamic plant uptake model, which accounts for differentiated multiple metabolization pathways in plant's tissues. The developed model is validated first theoretically and then experimentally against measured data from an experiment on the translocation and transformation of carbamazepine in three vegetables. The analysis is further enriched by performing a global sensitivity analysis on the soil-plant model to identify factors driving the compound's accumulation in plants' shoots, as well as to elucidate the role and the importance of soil hydraulic properties on the plant uptake process. Results of the multilevel numerical analysis emphasize the model's flexibility and demonstrate its ability to accurately reproduce physicochemical processes involved in the dynamic plant uptake of chemicals from contaminated soils

    Zn2+ and Cu2+ doping of one-dimensional lead-free hybrid perovskite ABX3 for white light emission and green solar cell applications

    No full text
    International audienceThe one-dimensional ABX3 hybrid perovskite (A=(CH3)2NH2=DMA, B=Cd and X=Cl) was synthesized and structurally characterized. The optical analysis showed that the material has a direct band-gap nature with a gap energy of 5.36 eV. The obtained compound exhibited a “cold” white-light emission under an excitation wavelength of 240 nm with a color-rendering index up to 92 and a correlated color temperature (CCT) of 7582 K. The doping of the crystal with Cu2+ ions decreased the gap energy toward 2.51 eV. The partial substitution of the Cd atoms with Cu atoms decreased the intensity of the emitted white-light under 240 nm and led to a “cold” white-light emission with a CCT of 7117 K. The doping of the material with Zn2+ yielded a blue-shift and the emitted light revealed a CCT of 11,028 K. On the other hand, by incorporating Cu2+ions into the B site a broad absorption band is observed in the visible region resulting from the d-d transitions around the copper atoms which makes the material a good visible-light absorber. Hence, the Cu doped compound could be suitable for both white-light emission and photovoltaic solar cells

    The use of exhausted grape marc to produce biofuels and biofertilizers: Effect of pyrolysis temperatures on biochars properties

    No full text
    International audienceThe wine industry represents an important economic sector in the Mediterranean countries. Currently, grape marc is valorized for ethanol production by distillation process generating a second residue called exhausted grape marc (EGM) that should be properly managed in order to avoid any related negative impacts onto the environment. In the present investigation, an innovative strategy was proposed to convert EGM into biofuels and biofertilizers through thermochemical conversion process such as carbonization/pyrolysis technique. In order to select the appropriate operating parameters, the impact of the slow pyrolysis temperatures of EGM (from 300 to 700 °C) on biochar production yields as well as their physico-chemical characteristics were assessed. The experimental results showed that the biochars yields production decrease with increasing the pyrolysis temperature and reach a plateau above 500 °C. The biochar yield at 500 °C is around 33%, which is amongst the highest values obtained for food processing residues. The biochar physico-chemical characterization showed a higher surface area (253.4 m2/g) was obtained for the char prepared at 600 °C. However, the maximum nutrients contents, namely potassium, nitrogen and phosphorus were registered at 500 °C. Based on the biochar yields and characteristics, it seems that EGM biochar produced through slow pyrolysis at 500 °C could be considered as a promising biofertilizer for agricultural purposes
    corecore