► Combined micro/nano scale texture was produced on crystalline silicon surface ► The micro/nano scale texture features antireflection properties ► Solar cells with this texture are shown improved ...blue-response and conversion efficiency.
Crystalline silicon solar cells with two-scale texture consisting of random upright pyramids and surface nanotextured layer directly onto the pyramids are prepared and reflectance properties and I–V characteristics measured. Random pyramids texture is produced by etching in an alkaline solution. On top of the pyramids texture, a nanotexture is developed using an electroless oxidation/etching process. Solar cells with two-scale surface texturization are prepared following the standard screen-printing technology sequence. The micro/nano surface is found to lower considerably the light reflectance of silicon. The short wavelengths spectral response (blue response) improvement is observed in micro/nano textured solar cells compared to standard upright pyramids textured cells. An efficiency of 17.5% is measured for the best micro/nano textured c-Si solar cell. The efficiency improvement is found to be due to the gain in both Jsc and Voc.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
The Special Issue “Internet and Computers for Agriculture” reflects the rapidly growing need for new information and communication technology (ICT) involvement in agriculture which is changing ...globally ...
•Lanthanide doped ZnO powders are prepared by green, simple and low cost method.•La doping in ZnO leads to complete RB mineralization within short irradiation time.•Optimal dopants content and ...catalysts preparation temperature are established.•The La-doped photocatalysts are more efficient than those doped with Eu and Ce.•Photocatalytic purification of contaminated sea water by RE doped ZnO is achieved.
The photocatalytic degradation of the textile dye Reactive Black 5 in distilled and sea water by Ln modified ZnO is studied for the first time under UV-light irradiation. The bleaching process is investigated from different aspects: the type of rare earth element, La3+ concentration and annealing temperature. The observed dye degradation rate increases with the La content up to 2mol % and then decreases (2.5 and 3.0mol%). It is found out that Ln modified ZnO photocatalyst achieves contaminants mineralization within a short irradiation time. The optimal dopants concentration and annealing temperature are experimentally established-powders, modified with 2mol% La3+ and annealed at 100°C, are the most efficient in Reactive Black 5 photodegradation in comparison with pure and (Ce3+, Eu3+) modified ZnO.
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► The addition of Ni2+ ions to the initial solution modifies the the ganglia-like hills and wrinkles morphology of the films. ► Amount of nickel doping strongly affect the ...photocatalytic activity of ZnO films under both, the UV and visible light iradiation. ► The concentration of MG by ZnO films decreases even in darkness.
Nanostructured ZnO thin films with different concentrations of Ni2+ doping (0, 1, 5, 10 and 15wt.%) are prepared by the sol–gel method for the first time. The thin films are prepared from zinc acetate, 2-methoxyethanol and monoethanolamine on glass substrates by using dip coating method. The films comprise of ZnO nanocrystallites with hexagonal crystal structure, as revealed by X-ray diffraction. The film surface is with characteristic ganglia-like structure as observed by Scanning Electron Microscopy. Furthermore, the Ni-doped films are tested with respect to the photocatalysis in aqueous solutions of malachite green upon UV-light illumination, visible light and in darkness. The initial concentration of malachite green and the amount of catalyst are varied during the experiments. It is found that increasing of the amount of Ni2+ ions with respect to ZnO generally lowers the photocatalytic activity in comparison with the pure ZnO films. Nevertheless, all films exhibit a substantial activity under both, UV and visible light and in darkness as well, which is promising for the development of new ZnO photocatalysts by the sol–gel method.
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Simple and robust hydrological modelling is critical for peat studies as water content (θ) and water table depth (d
WT
) are key controls on many biogeochemical processes. We show that near-surface θ ...can be a good predictor of θ at any depth and/or d
WT
in peat. This was achieved by further developing the formulae of an existing model and applying it for Mer Bleue bog (Ontario, Canada) and a permafrost peat plateau at Scotty Creek (Northwest Territories, Canada). Simulated θ dynamics at various depths in hummocks and hollows at both sites matched observations with R
2
, Willmott's index of agreement (d), and normalized Nash-Sutcliffe efficiency coefficient (NNSE), reaching 0.97, 0.95, and 0.86, respectively. Simulated bog WT dynamics matched observations with R
2
, d, and NNSE reaching 0.67, 0.87, and 0.72. Our approach circumvents the difficulties of measuring subsurface hydrology and reveals a perspective for large spatial scale estimation of θ and d
WT
in peat.
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Accurate modelling of peat water contents (θ) is critical for wetland studies. We modified the Campbell and Van Genuchten soil water retention curves (SWRCs) by replacing their empirical parameters ...with measurable properties. Combining the water table depth (d
WT
) into SWRCs, we derived formulae for calculating volumetric θ from d
WT
, coded in a simple model to test our hypotheses that d
WT
is a reliable predictor of θ for peat of low and high water holding capacity at near-saturation. We compared our simulations with time-domain reflectometry θ measurements at Mer Bleue bog (Ontario, Canada) and the Western Peatland fen (Alberta, Canada). Constraining Campbell SWRC at extreme drying and waterlogging rather than wilting point and field capacity produced superior results. The Van Genuchten SWRC was approximated by hyperbolic and inverse hyperbolic segments. When simplified, its performance reconciles with that of the modified Campbell SWRC. Overall, our formulae performed well with generalized peat parameters.
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Abstract Pro-inflammatory cytokines have been consistently reported to be elevated in schizophrenia patients. However, it is not known whether cytokines influence the presentation of psychotic ...symptoms. To address this issue, we evaluated the relationship between levels of inflammatory molecules and psychopathological parameters in patients with schizophrenia. We hypothesized that severity of symptoms would correlate with increased levels of inflammatory cytokines. Serum samples from 47 veterans with a diagnosis of schizophrenia and 20 healthy controls were tested for levels of 38 cytokines/chemokines involved in regulation of immune/inflammatory reactions using a Millipore multiplex bead array in a Luminex 100 system. We found significantly increased levels of GRO, MCP-1, MDC, and sCD40L, and significantly decreased levels of IFN-γ, IL-2, IL-12p70, and IL-17, in schizophrenia patients compared to controls. In addition, we observed positive correlations between levels of cytokines and the Positive and Negative Symptoms Scale (PANSS) scores in subjects with schizophrenia for G-CSF, IL-1β, IL1ra, IL-3, IL-6, IL-9, IL-10, sCD40L and TNF-β. Pathway analyses showed these cytokines to be part of the IL17 pathway. Using principal component analyses, we found the factor that included these cytokines and IL-17 to be associated with positive, general and total PANSS scores. These results suggest that alterations in this pathway may play a role in development of psychotic symptoms in schizophrenia.
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Accurately simulating gross primary productivity (GPP) in terrestrial ecosystem models is critical because errors in simulated GPP propagate through the model to introduce additional errors in ...simulated biomass and other fluxes. We evaluated simulated, daily average GPP from 26 models against estimated GPP at 39 eddy covariance flux tower sites across the United States and Canada. None of the models in this study match estimated GPP within observed uncertainty. On average, models overestimate GPP in winter, spring, and fall, and underestimate GPP in summer. Models overpredicted GPP under dry conditions and for temperatures below 0°C. Improvements in simulated soil moisture and ecosystem response to drought or humidity stress will improve simulated GPP under dry conditions. Adding a low‐temperature response to shut down GPP for temperatures below 0°C will reduce the positive bias in winter, spring, and fall and improve simulated phenology. The negative bias in summer and poor overall performance resulted from mismatches between simulated and observed light use efficiency (LUE). Improving simulated GPP requires better leaf‐to‐canopy scaling and better values of model parameters that control the maximum potential GPP, such asεmax (LUE), Vcmax (unstressed Rubisco catalytic capacity) or Jmax (the maximum electron transport rate).
Key Points
Gross primary productivity (GPP) from 26 models tested at 39 flux tower sites
Simulated light use efficiency controls model performance
Models overpredict GPP under dry conditions
•We modelled hydrology-related nutrient limitations in boreal transition zones to peatlands.•GPP, ER and NEP declined sharply from forest margins down to poor acidic fens.•That decline was due to ...rising WT, losing root contact with mineral soil, hence P limitations.
Forest productivity declines along the transition zones (ecotone) occupying the elevational gradient from upland boreal forest to peatlands. This decline is associated with water table rise and increasing peat depth from upper to lower topographic positions. We hypothesize that in boreal transition zones to poor fens, phosphorus limitations to plant growth are imposed by low pH, rising water table and increasing depth to mineral soil in the waterlogged peat. These phosphorus limitations, together with O2 limitations to root growth, cause sharp decline of productivity down to the fen. The ecosys model was applied to test these hypotheses in a boreal transition zone in central Saskatchewan, Canada. This zone extended from an upland black spruce forest down to a poor forested fen over an organic–mineral soil gradient with peat depth increasing from 60cm to 160cm. Model output was compared with field-derived tree carbon stocks, leaf area indexes, and moss net primary productivities at upper, middle, and lower undisturbed ecotone positions. Model results suggest that root contact with mineral soil through shallow peat over a deep water table at the upper ecotone sustained phosphorus uptake and plants did not experience phosphorus limitations. However deep peat and shallow water table towards the fen prevented root contact with mineral soil, imposing phosphorus limitations to plants growing in the poor, acidic peat, causing productivity decline by ∼75–80%, and tree biomass decline from ∼6900 to 2100gCm−2 in the model vs. ∼6600 to 800gCm−2 observed along the ecotone.
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Given the current growth in global challenges, the need for smart agriculture practices and effective strategies is emerging as an imminent issue at a planetary scale. Agriculture 4.0 involves a ...large variety of mobile apps, web applications, Internet of Things (IoT) devices and platforms, drones, robots, and smart machinery for precision agriculture. The expansion of cloud technologies, artificial intelligence (AI), machine learning (ML), deep learning (DL), and big data collection are setting the stage for Agriculture 5.0. Agriculture science and natural sciences are further promoting this trend with the development of leading-edge scientific models and platforms, including stochastic, process-based, and data-driven machine learning modeling. This Special Issue covers the most recent and up-to-date progress in all aspects of internet and computer software applications in agriculture, focusing on the development of web applications and mobile apps, smart IoT devices and platforms, AI, ML and DL solutions in precision agriculture for detection, recognition, classification, monitoring, cultivation, harvesting, and marketing; development of cloud technologies for smart agriculture; computer and machine vision methods and applications for drones and smart machinery, and sensors for field operations; diagnostics and data collection; big data science; scientific process-based and stochastic modeling; and machine learning modeling for agriculture, agroecosystems and natural ecosystems. The research in this Special Issue will contribute to the promotion of modern agriculture practices in the current climate and in the future.
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