Progress in nanotechnology calls for material probing techniques of high sensitivity and resolution. Such techniques are also used for high-impact studies of nanoscale materials in medicine and biology. Soft X-ray microscopy has been successfully used for investigating complex biological processes occurring at micrometric and sub-micrometric length scales and is one of the most powerful tools in medicine and the life sciences. Here, we present the capabilities of the TwinMic soft X-ray microscopy end-station at the Elettra synchrotron in the context of medical and biological imaging, while we also describe novel uses and developments.

Electromagnetic waves possessing orbital angular momentum (OAM) are powerful tools for applications in optical communications, quantum technologies, and optical tweezers. Recently, they have attracted growing interest since they can be harnessed to detect peculiar helical dichroic effects in chiral molecular media and in magnetic nanostructures. In this work, we perform single-shot per position ptychography on a nanostructured object at a seeded free-electron laser, using extreme ultraviolet OAM beams of different topological charge orders ℓ generated with spiral zone plates. By controlling ℓ, we demonstrate how the structural features of OAM beam profiles determine an improvement of about 30% in image resolution with respect to conventional Gaussian beam illumination. This result extends the capabilities of coherent diffraction imaging techniques, and paves the way for achieving time-resolved high-resolution (below 100 nm) microscopy on large area samples.

X-ray ptychography is an advanced computational microscopy technique, which is delivering exceptionally detailed quantitative imaging of biological and nanotechnology specimens, which can be used for high-precision X-ray measurements. However, coarse parametrisation in propagation distance, position errors and partial coherence frequently threaten the experimental viability. In this work, we formally introduce these actors, solving the whole reconstruction as an optimisation problem. A modern deep learning framework was used to autonomously correct the setup incoherences, thus improving the quality of a ptychography reconstruction. Automatic procedures are indeed crucial to reduce the time for a reliable analysis, which has a significant impact on all the fields that use this kind of microscopy. We implemented our algorithm in our software framework, SciComPty, releasing it as open-source. We tested our system on both synthetic datasets, as well as on real data acquired at the TwinMic beamline of the Elettra synchrotron facility.

X‐ray fluorescence (XRF) spectroscopy is a versatile tool employed in various fields like biomedicine, nanotechnology, and cultural heritage, providing comprehensive elemental maps that combine spectrographic, spatial, and morphological data. While commonly utilized in laboratories and synchrotron beamlines, accurate XRF data interpretation demands specialized software that incorporates the underlying physics, with PyMCA (ESRF) being a notable example. This manuscript introduces XRFitVis, an innovative, cloud‐based application designed for the effective visualization of μXRF maps, akin to those produced by PyMCA and similar platforms. This modern tool offers a user‐friendly, web‐based solution that facilitates the assessment of experimental results and the creation of publication‐ready figures, eliminating the need for local installation. Garnering positive feedback from early adopters, XRFitVis is now available to a broader audience. Hosted by Elettra Sincrotrone Trieste primarily for PyMCA‐fitted results, it aims for broad compatibility with various XRF fitting software, marking a significant step forward as the first free, generic, yet XRF‐specific visualization tool, poised to benefit the global spectroscopy community.

Computational methods are driving high impact microscopy techniques such as ptychography. However, the design and implementation of new algorithms is often a laborious process, as many parts of the code are written in close-to-the-hardware programming constructs to speed up the reconstruction. In this article, we present SciComPty, a new ptychography software framework aiming at simulating ptychography datasets and testing state-of-the-art and new reconstruction algorithms. Despite its simplicity, the software leverages GPU accelerated processing through the PyTorch CUDA interface. This is essential for designing new methods that can readily be employed. As an example, we present an improved position refinement method based on Adam and a new version of the rPIE algorithm, adapted for partial coherence setups. Results are shown on both synthetic and real datasets. The software is released as open-source.

Face recognition functions are today exploited through biometric sensors in many applications, from extended security systems to inclusion devices; deep neural network methods are reaching in this field stunning performances. The main limitation of the deep learning approach is an inconvenient relation between the accuracy of the results and the needed computing power. When a personal device is employed, in particular, many algorithms require a cloud computing approach to achieve the expected performances; other algorithms adopt models that are simple by design. A third viable option consists of model (oracle) distillation. This is the most intriguing among the compression techniques since it permits to devise of the minimal structure that will enforce the same I/O relation as the original model. In this paper, a distillation technique is applied to a complex model, enabling the introduction of fast state-of-the-art recognition capabilities on a low-end hardware face recognition sensor module. Two distilled models are presented in this contribution: the former can be directly used in place of the original oracle, while the latter incarnates better the end-to-end approach, removing the need for a separate alignment procedure. The presented biometric systems are examined on the two problems of face verification and face recognition in an open set by using well-agreed training/testing methodologies and datasets.