Improved Performance and Functionality in UV-Visible Measurements

A newly published review in the journal Advanced Materials explores how intelligent wearable sensors, powered by smart materials and machine learning, are changing healthcare into a decentralized, personalized, and predictive modeling system. An international team of researchers highlights emerging technologies that promise earlier diagnosis, improved therapy, and continuous health monitoring—anytime, anywhere.

Compositional Depth Profile (CDP) using the LECO GDS950 rapidly provides analysis of various galvanized coatings on steel.

A study from Chinese researchers demonstrates how combining satellite imagery, land use data, and machine learning can improve pollution monitoring in fast-changing urban rivers. The study focuses on non-optically active pollutants in the Weihe River Basin and showcases promising results for remote, data-driven water quality assessments.

New research highlights how remote satellite sensing technologies are changing the way scientists monitor inland water quality, offering powerful tools for tracking pollutants, analyzing ecological health, and supporting environmental policies across the globe.

Modern remote sensing technologies have evolved from coarse-resolution multispectral sensors like MODIS and MERIS to high-resolution, multi-band systems such as Sentinel-2 MSI, Landsat OLI, and UAV-mounted spectrometers. These advancements provide greater spectral and spatial detail, enabling precise monitoring of environmental, agricultural, and land-use dynamics.

A global research team has detailed how smart sensors, artificial intelligence (AI), machine learning, and Internet of Things (IoT) technologies are transforming the detection and management of environmental pollutants. Their comprehensive review highlights how spectroscopy and sensor networks are now key tools in real-time pollution tracking.