XRFast a new software package for processing of MA-XRF datasets using machine learning

X-ray fluorescence (XRF) spectroscopy is a common technique in the field of heritage science. However, data processing and data interpretation remain a challenge as they are time consuming and often require a priori knowledge of the composition of the materials present in the analyzed objects. For this reason, we developed an open-source,...

Can deep learning assist automatic identification of layered pigments from XRF data?

X-ray fluorescence spectroscopy (XRF) plays an important role for elemental analysis in a wide range of scientific fields, especially in cultural heritage. XRF imaging, which uses a raster scan to acquire spectra pixel-wise across artworks, provides the opportunity for spatial analysis of pigment distributions based on their elemental...

Author Correction: In-plane selective area InSb–Al nanowire quantum networks (Communications Physics, (2020), 3, 1, (59), 10.1038/s42005-020-0324-4)

The Data availability statement of this article has been modified to add the accession link to the raw data. The old Data availability statement read “Materials and data that support the findings of this research are available within the paper. All data are available from the corresponding author upon request”. This has been replaced by ...

In-plane selective area InSb–Al nanowire quantum networks

Strong spin–orbit semiconductor nanowires coupled to a superconductor are predicted to host Majorana zero modes. Exchange (braiding) operations of Majorana modes form the logical gates of a topological quantum computer and require a network of nanowires. Here, we utilize an in-plane selective area growth technique for InSb–Al semiconductor...