In a recent study, researchers analyzed the correlation between laser-induced breakdown spectroscopy (LIBS) and acoustic signals captured by the microphone (MIC) on NASA's Perseverance rover during its mission on Mars.

Researchers have developed microbiological and spectrophotometric methods for the quantitative determination of tioconazole, an antifungal drug, providing valuable tools for the analysis and quality control of tioconazole in pharmaceutical preparations.

A recent study utilized infrared microspectroscopy to investigate the chemical composition of psammoma bodies (PBs) in ovarian and thyroid cancer tissues.

A recent study utilized laser-induced breakdown spectroscopy (LIBS) and inductively coupled plasma optical emission spectroscopy (ICP-OES) to examine the uptake and distribution of heavy metals in industrial hemp and white mustard plants.

Researchers have developed a novel near-infrared fluorescent probe, FNIR-pH, that allows sensitive detection of mitochondrial pH and study of mitophagy, revealing cellular health and disease processes.

A newly published study reveals that double spacer disk antennas enable the simultaneous detection of molecules in a surface-enhanced infrared absorption (SEIRA) sensor platform.

Scientists have introduced a statistical definition of the limit of detection (LOD) for calibration-free laser-induced breakdown spectroscopy (CF-LIBS). The study provides a robust framework for LOD determination, addressing the high LOD associated with LIBS and enabling more accurate trace element analysis.

Researchers have developed a new biosensors decorated with silver nanoparticles that enable the sensitive detection of the Acyclovir drug on filter paper substrates.

Serum Raman spectroscopy combined with a convolutional neural network (CNN) offers a highly accurate and noninvasive method for diagnosing gastric, colon, rectal, and lung cancers paving the way for improved cancer screening and early detection.

Researchers have developed an intelligent detection method for quaternary blended oil using near-infrared spectroscopy (NIRS) technology.

Scientists have developed a rapid and accurate method for predicting cocoa shell content in cocoa powder using handheld and benchtop vis-NIR spectrometers combined with chemometric techniques.

Researchers have developed an eco-friendly method using chemometric techniques and artificial neural networks for simultaneous determination of aspirin, clopidogrel, and either atorvastatin or rosuvastatin in their fixed-dose combination (FDC) formulations using ultraviolet (UV) spectrophotometry.

A recent study reveals the pressure-induced phase transitions in imidazolium manganese-hypophosphite hybrid perovskite using Raman spectroscopy.

Scientists have developed a fluorescent sensor via a surface ion imprinting rice husk-based polymer capable of selectively detecting and efficiently adsorbing copper ions from lake water.

Researchers have conducted a study on natural stilbene-based sunscreens, uncovering the ultrafast non-adiabatic dynamics and UV protection mechanisms of hydroxy resveratrol and pterostilbene.

A recent study delves into the size-dependent optoelectronics of the N-type ultra-high conductive polymer PBFDO. Researchers explore the absorption spectra, charge transfer modes, and electron transport properties of PBFDO, providing valuable theoretical guidance for its potential applications in nanoscale optoelectronics and device design.

Researchers have conducted a preliminary study on the potential use of near-infrared (NIR) and Raman spectroscopy for predicting ice cream mix viscosity. The study highlights the promising performance of NIR spectroscopy and serves as a starting point for further investigations into in situ application of these analytical tools in the ice cream manufacturing process.

Researchers have developed a novel method, IEC-LIBS, which combines laser-induced breakdown spectroscopy and an ion enrichment chip for sensitive and rapid detection of chromium in different valence states in water and soil. The study demonstrates the effectiveness of this simple and environmentally friendly approach, offering potential for field applications and compliance with environmental quality standards.

Scientists have developed an optimized method for the precise measurement of iodine-129 in decommissioning wastes using tandem ICP-MS/MS. Their study demonstrates the effectiveness of this approach in achieving low-level measurements with improved sensitivity for waste characterization and environmental monitoring.

A team of researchers has developed a novel algorithm for rapid peak fitting and resolution enhancement in Raman hyperspectra analysis. The algorithm offers significant advancements in processing large datasets, improving peak resolution, and extracting valuable information about analytes.

A recent study presents an innovative approach for predicting sugarbeet seed germination using the fusion of hyperspectral imaging and information analysis techniques.

A new study demonstrates the improved accuracy of depth profiling in confocal Raman microscopy for analyzing the structural and chemical composition of polymeric microsphere layers.

Revolutionary research demonstrates the power of machine learning in predicting the ground-state electronic structure of organic molecules from core-loss spectra, offering new insights into nanomaterial design.

A new study leverages Fourier transform infrared spectroscopy (FT-IR) to determine the geographical origin and assess the quality of Rosa roxburghii Tratt (RRT), providing valuable insights for functional food production.

New research uncovers novel findings regarding rare earth element (REE) isotopes through the utilization of high-resolution laser-induced breakdown spectroscopy (LIBS) and laser-induced breakdown spectroscopy-molecular laser-induced fluorescence (LIBS-MLIF) techniques.

Machine learning and synchrotron radiation-based micro X-ray fluorescence imaging show promise for early cancer diagnostics by identifying trace biometals as potential cancer biomarkers. The research demonstrates the feasibility of using machine learning algorithms to analyze the spatial distribution of biometals and classify cancer pathogenesis stages, offering potential advancements in non-invasive cancer detection.

New research has demonstrated a two-stage machine learning strategy to overcome bias in spICP-TOF-MS data and improve the classification of nanoparticles. The approach achieves high accuracy in identifying engineered, incidental, and natural nanoparticle types, providing a robust and efficient method for nanoparticle classification in complex samples.

Advanced spectroscopic techniques reveal strong spin-phonon coupling in hexagonal lutetium manganese(III) oxide, providing valuable insights into its magnetoelectric properties. The study's findings pave the way for further exploration and utilization of multiferroic materials in electronic and magnetic device applications.