Noureddine Melikechi

Tag-Laser Induced Breakdown Spectroscopy (Tag-LIBS) is an emerging technique designed to enhance the analytical performance of conventional LIBS using specific tagging strategies.

Under various atmospheric conditions, laser-induced breakdown spectroscopy (LIBS) is a powerful technique for elemental analysis, including in Earth- and Mars-like environments. However, understanding the plasma behavior and its dependence on ambient pressure and laser parameters remains a challenge. In this study, a numerical model based on a three-temperature Eulerian radiation framework under non-local thermodynamic equilibrium conditions is employed to investigate the interaction of a nanosecond laser pulse with a graphite target under helium (He) and carbon dioxide (CO ₂ atmospheres. The aim is to provide insights into the effects of focusing conditions and ambient pressure (3 to 9 mbar and 1000 mbar) on plasma parameters relevant to both Earth- and Mars-like settings. Our results show that increased ambient pressure significantly enhances electron and ion densities, while the focusing conditions influence the temperature and fluid velocity of plasma species, as well as the spatial distribution and intensity of the plasma, ultimately affecting its diagnostic potential. These findings are critical for optimizing LIBS applications in planetary exploration and contribute to improving quantitative analyses under varying atmospheric compositions.

Machine learning analysis of ICP-MS data identifies elemental ratios that differentiates with great accuracy blood plasma of Alzheimer’s patients and healthy donors.

Research on the correlation between metal levels in blood and Covid-19 infection has been conducted primarily by assessing how each individual blood metal is linked to different aspects of the disease using samples from donors with various levels of severity to Covid-19 infection. Using logistics regression on LIBS spectra of plasma samples collected pre- and post- Covid-19 pandemic from donors known to have developed various levels of antibodies to the SARS-Cov-2 virus, we show that relying on the levels of Na, K, and Mg together is more efficient at differentiating the two types of plasma samples than any single blood alone.

Gulf War illness (GWI) is a chronic illness with no known validated biomarkers that affects the lives of hundreds of thousands of people. As a result, there is an urgent need for the development of an untargeted and unbiased method to distinguish GWI patients from non-GWI patients. We report on the application of laser-induced breakdown spectroscopy (LIBS) to distinguish blood plasma samples from a group of subjects with GWI and from subjects with chronic low back pain as controls. We initially obtained LIBS data from blood plasma samples of four GWI patients and four non-GWI patients. We used an analytical method based on taking the difference between a mean LIBS spectrum obtained with those of GWI patients from the mean LIBS spectrum of those of the control group, to generate a “difference” spectrum for our classification model. This model was cross-validated using different numbers of differential LIBS emission peaks. A subset of 17 of the 82 atomic and ionic transitions that provided 70% of correct diagnosis was selected test in a blinded fashion using 10 additional samples and was found to yield 90% classification accuracy, 100% sensitivity, and 83.3% specificity. Of the 17 atomic and ionic transitions, eight could be assigned unambiguously to species of Na, K, and Fe.

Classification and identification of amino acids in aqueous solutions is important in the study of biomacromolecules. Laser Induced Breakdown Spectroscopy (LIBS) uses high energy laser-pulses for ablation of chemical compounds whose radiated spectra are captured and recorded to reveal molecular structure. Spectral peaks and noise from LIBS are impacted by experimental protocols. Current methods for LIBS spectral analysis achieves promising results using PCA, a linear method. It is well-known that the underlying physical processes behind LIBS are highly nonlinear. Our work set out to understand the impact of LIBS spectra on suitable neighborhood size over which to consider pattern phenomena, if nonlinear methods capture pattern phenomena with increased efficacy, and how they improve classification and identification of compounds. We analyzed four amino acids, polysaccharide, and a control group, water. We developed an information theoretic method for measurement of LIBS energy spectra, implemented manifold methods for nonlinear dimensionality reduction, and found while clustering results were not statistically significantly different, nonlinear methods lead to increased classification accuracy. Moreover, our approach uncovered the contribution of micro-wells (experimental protocol) in LIBS spectra. To the best of our knowledge, ours is the first application of Manifold methods to LIBS amino-acid analysis in the research literature.

The impact of altering laser focusing conditions on laser-induced breakdown spectroscopy experiments is investigated under ambient Earth laboratory and simulated Martian atmospheres. Experiments were performed in which the focal spot size was varied on a sample by altering the lens to sample distance with respect to targets of interest. Samples investigated include aluminum, copper, and steel. Specific neutral and ionic transitions of each sample were monitored. Atomic and ionic emissions show different intensity peak distributions along the varying lens to sample distance. Ionic species have peak emissions when laser plasma is initiated with a focused spot within the sample in ambient Earth laboratory air, while atomic emissions have peak intensities several millimeters deeper into a sample. In simulated Martian atmospheres, atomic emissions are observed to peak when the laser is focused within the sample, while ionic emissions have peak intensities when the laser is focused near the surface of a sample.