Diana Faria Capela

Abstract
Laser-induced breakdown spectroscopy (LIBS) imaging has now a well-established position in the subject of spectral imaging, leveraging multi-element detection capabilities and fast acquisition rates to support applications both at academic and technological levels. In current applications, the standard processing pipeline to explore LIBS imaging data sets revolves around identifying an element that is suspected to exist within the sample and generating maps based on its characteristic emission lines. Such an approach requires some previous expert knowledge both on the technique and on the sample side, which hinders a wider and more transparent accessibility of the LIBS imaging technique by non-specialists. To address this issue, techniques based on visual analysis or peak finding algorithms are applied on the average or maximum spectrum, and may be employed for automatically identifying relevant spectral regions. Yet, maps containing relevant information may often be discarded due to low signal-to-noise ratios or interference with other elements. In this context, this work presents an agnostic processing pipeline based on a spatial information ratio metric that is computed in the Fourier space for each wavelength and that allows for the identification of relevant spectral ranges in LIBS. The results suggest a more robust and streamlined approach to feature extraction in LIBS imaging compared with traditional inspection of the spectra, which can introduce novel opportunities not only for spectral data analysis but also in the field of data compression.

Abstract
Laser-induced breakdown spectroscopy is a spectroscopic technique that allows for fast elemental mapping of heterogeneous samples. Yet, detailed maps need high-resolution sampling grids, which can turn the task into a time-consuming process and can increase sample damage. In this work, we present the implementation of an imaged-based intelligent mesh algorithm that makes use of superpixel segmentation to optimize elemental mapping processes. Our results show that the approach can increase the elemental mapping resolution and decrease acquisition times, fostering opportunities for applications that benefit from minimal sample damage such as heritage analysis, or timely analysis such as industrial applications. © 2022 The Author(s)

Abstract
Imaging the spatial distribution of chemical elements at a sample surface is a common application of laserinduced breakdown spectroscopy with vast scientific and technological applications. Yet, typical imaging solutions only explore the creation of two-dimensional maps, which can limit the interpretability of the results and further diagnostics in three-dimensional settings. Within this context, this work explores the combination of spectral imaging techniques and photogrammetry to deploy a versatile solution for the creation of threedimensional spectral imaging models. First, by making use of a numerical algorithm that is able to match features in the spectral image with those of the three-dimensional model, we show how to match the mesh from distinct sensor modalities. Then, we describe a possible visualization workflow, making use of dedicated photogrammetry and visualization software to easily deploy interactive models. Overall, the results demonstrate the versatility of our approach and pave for the development of novel spectral imaging diagnostic strategies that are able to deliver better qualitative analysis and insight in the three-dimensional space.

Abstract
Anode-less batteries are a promising innovation in energy storage technology, eliminating the need for traditional anodes and offering potential improvements in efficiency and capacity. Here, we have fabricated and tested two types of anode-less pouch cells, the first using solely a copper negative current collector and the other the same current collector but coated with a nucleation seed ZnO layer. Both types of cells used the same all-solid-state electrolyte, Li2.99Ba0.005ClO composite, in a cellulose matrix and a LiFePO4 cathode. Direct and indirect methods confirmed Li metal anode plating after charging the cells. The direct methods are X-ray photoelectron spectroscopy (XPS) and laser-induced breakdown spectroscopy (LIBS), a technique not divulged in the battery world but friendly to study the surface of the negative current collector, as it detects lithium. The indirect methods used were electrochemical cycling and impedance and scanning electron microscopy (SEM). It became evident the presence of plated Li on the surface of the current collector in contact with the electrolyte upon charging, both directly and indirectly. A maximum average lithium plating thickness of 2.9 mu m was charged, and 0.13 mu m was discharged. The discharge initiates from a maximum potential of 3.2 V, solely possible if an anode-like high chemical potential phase, such as Li, would form while plating. Although the ratings and energy densities are minor in this study, it was concluded that a layer of ZnO, even at 25 degrees C, allows for higher discharge power for more hours than plain Cu. It was observed that where Li plates on ZnO, Zn is not detected or barely detected by XPS. The present anode-less cells discharge quickly initially at higher potentials but may hold a discharge potential for many hours, likely due to the ferroelectric character of the electrolyte.

Abstract
Fast and precise identification of minerals in geological samples is of paramount importance for the study of rock constituents and for technological applications in the context of mining. However, analyzing samples based only on the extrinsic properties of the minerals such as color can often be insufficient, making additional analysis crucial to improve the accuracy of the methods. In this context, Laser-induced breakdown spectroscopy mapping is an interesting technique to perform the study of the distribution of the chemical elements in sample surfaces, thus allowing deeper insights to help the process of mineral identification. In this work, we present the development and deployment of a processing pipeline and algorithm to identify spatial regions of the same mineralogical composition through chemical information in a fast and automatic way. Furthermore, by providing the necessary labels to the results on a training sample, we can turn this unsupervised methodology into a classifier that can be used to generalize and classify minerals in similar but unseen samples. The results obtained show good accuracy in reproducing the expected mineral regions and extend the interpretability of previous unsupervised methods with a visualization tool for cluster assignment, thus paving for future applications in contexts requiring high-throughput mineral identification systems, such as mining.