In a recent study, laser-induced breakdown spectroscopy (LIBS) was used, for the first time, to quantitatively analyze powder materials used in additive technologies.
In a recent study (1), laser-induced breakdown spectroscopy (LIBS) was used, for the first time, to quantitatively analyze powder materials used in additive technologies. Researchers found that using LIBS to map loose metal powder attached to double-sided adhesive tape provided high reproducibility of measurements even for powder mixtures with a large range of particle densities (tungsten carbide particles in nickel alloy powder).
Calibration curve construction and accuracy estimation by the leave-one-out cross-validation procedure was used to estimate LIBS analytical capabilities for tungsten and carbon analysis. A LIBS and X-ray fluorescence (XRF) spectroscopy comparison showed better results for LIBS analysis. Improved analysis accuracy and the capability to quantify light elements (for example, carbon) demonstrated the suitability of LIBS as a technique for express on-site multielement analysis of powder materials used in additive technologies.
Reference
V.N. Lednev, P.A. Sdvizhenskii, M. Ya. Grishin, M.A. Davidov, A. Ya. Stavertiy, R.S. Tretyakov, M.V. Taksanc, and S.M. Pershin, arXiv.org (2018). https://arxiv.org/pdf/1802.00236.pdf
Spectroscopy and GPC to Evaluate Dissolved Organic Matter
February 4th 2025In a new study, a team of scientists used gel permeation chromatography, three-dimensional excitation-emission matrix fluorescence spectroscopy, and UV-visible spectroscopy to assess road runoff from drinking water treatment plants to evaluate the method' capacity for removing dissolved organic matter (DOM).
Blood-Glucose Testing: AI and FT-IR Claim Improved Accuracy to 98.8%
February 3rd 2025A research team is claiming significantly enhanced accuracy of non-invasive blood-glucose testing by upgrading Fourier transform infrared spectroscopy (FT-IR) with multiple-reflections, quantum cascade lasers, two-dimensional correlation spectroscopy, and machine learning. The study, published in Spectrochimica Acta Part A, reports achieving a record-breaking 98.8% accuracy, surpassing previous benchmarks for non-invasive glucose detection.
Distinguishing Horsetails Using NIR and Predictive Modeling
February 3rd 2025Spectroscopy sat down with Knut Baumann of the University of Technology Braunschweig to discuss his latest research examining the classification of two closely related horsetail species, Equisetum arvense (field horsetail) and Equisetum palustre (marsh horsetail), using near-infrared spectroscopy (NIR).