Laser-Induced Breakdown Spectroscopy-Visible and Near-Infrared Spectroscopy Fusion Based on Deep Learning Network for Identification of Adulterated Polygonati Rhizoma.

The geographical origin of foods greatly influences their quality and price, leading to adulteration between high-priced and low-priced regions in the market. The rapid detection of such adulteration is crucial for food safety and fair competition. To detect the adulteration of Polygonati Rhizoma from different regions, we proposed LIBS-VNIR fusion based on the deep learning network (LVDLNet), which combines laser-induced breakdown spectroscopy (LIBS) containing element information with visible and near-infrared spectroscopy (VNIR) containing molecular information.

Enhancement of spectral model transferability in LIBS systems through LIBS-LIPAS fusion technique.

Background: Laser-induced breakdown spectroscopy (LIBS) is extensively utilized a range of scientific and industrial detection applications owing to its capability for rapid, in-situ detection. However, conventional LIBS models are often tailored to specific LIBS systems, hindering their transferability between LIBS subsystems. Transfer algorithms can adapt spectral models to subsystems, but require access to the datasets of each subsystem beforehand, followed by making individual adjustments for the dataset of each subsystem.

Investigation of ultra wide bandgap Flouro-perovskite materials RBeF (R[bond, double bond]K and Li) for smart window applications: A DFT study.

The human body is affected by ultraviolet radiation because it can penetrate and harm bodily cells. Although skin cancer and early aging are consequences of prolonged exposure to ultraviolet (UV) rays, sun rays signify immediate excessive exposure. In this context, some structural, optical, electrical, and mechanical properties of the beryllium-based cubic fluoro-perovskite RBeF (R[bond, double bond]K and Li) compounds are examined through the use of density functional theory (DFT) within generalized gradient approximation (GGA) using the Perdew-Burke-Ernzerhof (PBE) approximations (GGA-PBE).