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"The ion exchange property of a bipolar membrane has enabled us to load the CdS photocatalyst on one side and Pd electrocatalyst on the other. " We can convert vectorial electron transfer from CdS to Pd and to an electron acceptor by direct light excitation of CdS in the BPM-CdS/Pd membrane. Pd loading in the membrane, as well as its effect on modulating electron and hole transfer rates are discussed. ".
Source link: https://www.osti.gov/biblio/1856482
"IIu2013VI semiconductors are used in numerous electro-optical applications. " Double heterostructures with passivated interfaces live for lifespans over 1 u00b5s, but not for CdTe DHs or CdTe DHs. In CdTe and CdSeTe DHs, we investigate the passivation systems. A combination of superior intragrain longevity, extremely low grain boundary recombination, and increased Te4+ interfacial presence in comparison to CdTe" contributes to a number of outstanding intragrain lifetimes, 1 u00b5s, and greater Te4+ interfacial presence in comparison to CdTe. ".
Source link: https://www.osti.gov/biblio/1788434
To create the CdS@ZnIn 2 S 4 hierarchical hollow photocatalysts with chemically bonded interface, the two-dimensional ZnIn 2 S 4 nanosheets are grown on the surface of CdS hollow cubes. This is the stoichiometric generation of H 2 and H 2 O 2 from pure water found for the CdS@ZnIn 2 S 4 sulfide-only photocatalysts under visible light irradiation with an apparent quantum yield of 1. 63 percent at 400 nm.
Source link: https://www.osti.gov/biblio/1864344
"The complexity and cost of scientific and computational testing of impurity levels makes a data-driven machine learning approach appropriate. " Regression models for the impurity formation enthalpy and charge transition rates are created for the conversion of any semiconductor + impurity network into a series of numerical descriptors. These regression models can then predict impurity characteristics in mixed anion CdX compounds more accurately, showing that although initially only focused on the end points, they are also applicable to intermediate compositions. We make machine-learned calculations of hundreds of potential impurities in 5 chalcogenide compounds, and we also provide a rundown of impurities that can change the equilibrium Fermi level in the semiconductor as determined by the dominant intrinsic defects. Machine learning results for the dominating impurities align well with DFT predictions, demonstrating the accuracy of machine-learned algorithms in the quick testing of impurities that is likely to influence semiconductors' optoelectronic behavior. ".
Source link: https://www.osti.gov/biblio/1632326
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