Conventional wearable sweat sensors utilize hydrophobic ion-selective membranes (ISMs) and require tight contact and adhesives to achieve signal stability. However, this can lead to user discomfort and skin-related diseases, necessitating the development of non-contact alternatives. In a new study, inspired by the self-cleaning behavior of rose petals, researchers developed novel ISM-based sweat sensors that feature enhanced signal stability and performance, avoid skin contact, and are reusable, making them practical for daily use.

The latest phase of the Radiation Transfer Model Inter-comparison (RAMI-V) aimed to improve the accuracy of radiative transfer models (RTMs) used for simulating radiation measurements over plant canopy surfaces. 

To address these limitations, we introduce a novel framework: the Molecular Merged Hypergraph Neural Network (MMHNN). MMHNN innovatively incorporates a predefined set of molecular subgraphs, replacing each with a supernode to construct a compact hypergraph. This architectural change substantially reduces computational overhead while preserving essential molecular interactions.