Spatiotemporal spectroscopic characterization of plasmas induced by non-orthogonal laser ablation.

Ablation geometry significantly affects the plasma parameters and the consequent spectroscopic observations in laser-induced breakdown spectroscopy. Nevertheless, plasmas induced by laser ablation under inclined incidence angles are studied to a significantly lesser extent compared to plasmas induced by standard orthogonal ablation. However, inclined ablation is prominent in stand-off applications, such as the Curiosity Mars rover, where the orthogonality of the ablation laser pulse cannot be always secured. Thus, in this work, we characterize non-orthogonal ablation plasmas by applying plasma imaging, tomography, and spectral measurements. We confirm earlier observations according to which non-orthogonal ablation leads to a laser-induced plasma that consists of two distinct parts: one expanding primarily along the incident laser pulse and one expanding along the normal of the sample surface. Moreover, we confirm that the former emits mainly continuum radiation, while the latter emits mainly sample-specific characteristic radiation. We further investigate and compare the homogeneity of the plasmas and report that inclined ablation affects principally the ionic emissivity of laser-induced plasmas. Overall, our results imply that the decreased fluence resulting from inclined angle ablation and the resulting inhomogeneities of the plasmas must be considered for quantitative LIBS employing non-orthogonal ablation.

Interpreting support vector machines applied in laser-induced breakdown spectroscopy.

Laser-induced breakdown spectroscopy is often combined with a multivariate black box model-such as support vector machines (SVMs)-to obtain desirable quantitative or qualitative results. This approach carries obvious risks when practiced in high-stakes applications. Moreover, the lack of understanding of a black-box model limits the user's ability to fine-tune the model. Thus, here we present four approaches to interpret SVMs through investigating which features the models consider important in the classification task of 19 algal and cyanobacterial species. The four feature importance metrics are compared with popular approaches to feature selection for optimal SVM performance. We report that the distinct feature importance metrics yield complementary and often comparable information. In addition, we identify our SVM model's bias towards features with a large variance, even though these features exhibit a significant overlap between classes. We also show that the linear and radial basis kernel SVMs weight the same features to the same degree.

Tomography of double-pulse laser-induced plasmas in the orthogonal geometry.

The temporal evolution of laser-induced plasmas is studied in the orthogonal double-pulse arrangement. Both the pre-ablation mode (an air spark is induced above the sample surface prior to the ablation pulse) and the re-heating mode (additional energy is delivered into the plasma created by the ablation pulse) is considered. The plasmas are investigated in terms of the temporal evolution of their electron density, temperature, and volume. The plasma volumes are determined using a time-resolved tomography technique based on the Radon transformation. The reconstruction is carried out for both white-light and band-pass filtered emissivities. The white-light reconstruction corresponds to the overall size of the plasmas. On the other hand, the band-pass emissivity reconstruction shows the distribution of the atomic sample species (Cu I). Moreover, through spectrally resolved tomographic reconstruction, the spatial homogeneity of the electron density and temperature of the plasmas is also investigated at various horizontal slices of the plasmas. Our results show that the pre-ablation geometry yields a more temporally stable and spatially uniform plasma, which could be beneficial for calibration-free laser-induced breakdown spectroscopy (LIBS) approaches. On the contrary, the plasma generated in the re-heating geometry exhibits significant variations in electron density and temperature along its vertical axis. Overall, our results shed further light on the mechanisms involved in the LIBS signal enhancement using double-pulse ablation.

Benchmark classification dataset for laser-induced breakdown spectroscopy.

In this work, we present an extensive dataset of laser-induced breakdown spectroscopy (LIBS) spectra for the pre-training and evaluation of LIBS classification models. LIBS is a well-established spectroscopic method for in-situ and industrial applications, where LIBS is primarily applied for clustering and classification tasks. As such, our dataset is aimed at helping with the development and testing of classification and clustering methodologies. Moreover, the dataset could be used to pre-train classification models for applications where the amount of available data is limited. The dataset consists of LIBS spectra of 138 soil samples belonging to 12 distinct classes. The spectra were acquired with a state-of-the-art LIBS system. Lastly, the composition of each sample is also provided, including estimated uncertainties.