Bent crystal are widely used as optics for X-rays, but via the phenomenon of planar channeling they may act as waveguide for relativistic charged particles beam as well, outperforming some of the traditional technologies currently employed. A physical description of the phenomenon and the resulting potential for applications in a particle accelerator is reported. The elastic properties of the anisotropic crystal lattice medium are discussed, introducing different types of curvature which can enable a wide array of bending schemes optimized for each different case features. The technological development of machining strategy and bending solutions useful for the fabrication of crystals suitable in high energy particle manipulations are described. As well as the high precision characterization processes developed in order to satisfy the strict requirements for installation in an accelerator. Finally, the characterization of channeling phenomenon in bent crystal is described, pointing out several experimental setups suitable to comply each specific case constrains.

Currently, plant phenological phases are determined through models based on historical data series and advanced measurement tools such as satellite imaging, which are not always accurate. This study explores innovatively the relationship between Volatile Organic Compounds emitted by tomato plants and their phenological phases using machine learning algorithms. Supervised models, particularly k-NN, achieved an accuracy of 96.8% in identifying phases. These results highlight the potential of sensors and AI to accurately monitor crop development.

The analysis of volatile organic compounds emitted by cell cultures provides a non-invasive method for monitoring metabolic and oxidative stress states. However, detection is challenged by low volatile organic compound concentrations and high sample humidity. This study introduces an integrated system combining a MEMS-based pre-concentrator with an array of n-type Metal-Oxide chemiresistive gas sensors to analyze emissions from the K562 leukemia cell line. The main goal is to distinguish cellular volatile organic compound signals from those of the culture medium. To achieve this, the pre-concentrator is used with different temperature-programmed desorption protocols to enhance signal intensity and improve discrimination performance.

Nowadays, agriculture is facing significant challenges, including climate change. Precision agriculture might address these issues by optimizing resource use and promoting sustainability. In this work, a case study of tomato crop monitoring is presented, employing a large amount of gas sensor data collected over three years (2020–2022) to develop models for phenological phase classification. A k-NN classifier achieved accuracies above 99% across multiple train/test splits, with AUC, sensitivity, specificity, precision, and F1-score above 98%. Results demonstrate the feasibility of low-computational-cost systems capable of real-time detection of the transition point between plants’ developmental stages.