Nanowerk December 15, 2023 Negatively charged group-IV color centers in diamond are promising candidates for quantum memories as they combine long storage times with excellent optical emission properties and an optically addressable spin state. However, as a material, diamond lacks the many functionalities needed to realize scalable quantum systems. Thin-film lithium niobate (TFLN), in contrast, offers several useful photonic nonlinearities, including the electro-optic effect, piezoelectricity, and capabilities for periodically poled quasi-phase matching. Researchers at Stanford University have presented highly efficient heterogeneous integration of diamond nanobeams containing negatively charged silicon-vacancy (SiV) centers with TFLN waveguides. They observed greater than 90% transmission […]
Category Archives: Lithium
Revolutionizing lithium production on a string
Science Daily September 7, 2023 A team of researchers in the US (Princeton University, University of Maryland) has developed an efficient and self-concentrating crystallization method for the selective extraction of lithium from both brine and seawater. The sequential and separable crystallization of cation species with different concentrations and solubilities was enabled by a twisted and slender 3D porous natural cellulose fibre structure via capillary and evaporative flows. The process exhibited an evaporation rate as high as 9.8 kg m−2 h−1, and it selectively concentrated lithium by orders of magnitude. They characterized the composition and spatial distribution of crystals, and a transport model deciphered […]
Mapping Australia’s hidden lithium reserves
Phys.org August 31, 2023 Researchers in Australia used a digital soil mapping framework to combine data from recent geochemical surveys and environmental covariates that affect soil formation to predict and map aqua-regia-extractable Li content across the 7.6×106 km2 area of Australia. Catchment outlet sediment samples were collected with both top (0–10 cm depth) and bottom (on average ∼60–80 cm depth). They developed 50 bootstrap models using a cubist regression tree algorithm for each depth. The spatial prediction models were validated on an independent Northern Australia Geochemical Survey dataset, showing a good prediction for the top depth. The model for the bottom depth is […]