888 resultados para ARTIFICIAL PHOTOSYNTHESIS


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Field-collected specimens of three species of Laminaria and three species of subtidal red algae (Delesseria sanguinea, Plocamium cartilagineum and Phyllophora pseudoceranoides) were exposed to natural summer sunlight on Helgoland (southern North Sea) for up to 4 h at 15 °C. Dark-adapted variable fluorescence (Fv : Fm) was measured immediately after these treatments, and following 6, 24 and 48 h of recovery in moderate irradiances of white light. The response of plants to the full spectrum of natural sunlight was compared with that to PAR alone, UV-A + visible, UV-A + UV-B, or UV-A alone. The Fv : Fm values of all species were reduced to minimal values after 4 h in all of these treatments, but those of the more resistant species (Laminaria spp. and P. pseudoceranoides) were higher after shorter exposures to UV radiation alone than to PAR with or without UV. The recovery of Fv : Fm in all species was also more rapid in the two treatments that contained UV radiation alone than in those that included PAR. These results suggest that it is the high irradiances of PAR in natural sunlight which are responsible for the photoinhibition of photosynthesis of subtidal seaweeds and that the current ambient irradiances of UV radiation (either UV-B or UV-A) in northern temperate latitudes would not contribute significantly to this photoinhibition.

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High-fidelity quantum computation and quantum state transfer are possible in short spin chains. We exploit a system based on a dispersive qubit-boson interaction to mimic XY coupling. In this model, the usually assumed nearest-neighbor coupling is no longer valid: all the qubits are mutually coupled. We analyze the performances of our model for quantum state transfer showing how preengineered coupling rates allow for nearly optimal state transfer. We address a setup of superconducting qubits coupled to a microstrip cavity in which our analysis may be applied.

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An artificial neural network (ANN) model is developed for the analysis and simulation of the correlation between the properties of maraging steels and composition, processing and working conditions. The input parameters of the model consist of alloy composition, processing parameters (including cold deformation degree, ageing temperature, and ageing time), and working temperature. The outputs of the ANN model include property parameters namely: ultimate tensile strength, yield strength, elongation, reduction in area, hardness, notched tensile strength, Charpy impact energy, fracture toughness, and martensitic transformation start temperature. Good performance of the ANN model is achieved. The model can be used to calculate properties of maraging steels as functions of alloy composition, processing parameters, and working condition. The combined influence of Co and Mo on the properties of maraging steels is simulated using the model. The results are in agreement with experimental data. Explanation of the calculated results from the metallurgical point of view is attempted. The model can be used as a guide for further alloy development.