Abstract
Polarization optical fiber sensors are based on modifications of fiber birefringence by an external measurand (e.g., strain, pressure, acoustic waves). Yet, this means that different input states of polarization will result in very distinct behaviors, which may or may not be optimal in terms of sensitivity and signal-to-noise ratio. To tackle this challenge, this manuscript presents an optimization technique for the input polarization state using the Fisher information formalism, which allows for achieving maximal precision for a statistically unbiased metric. By first measuring the variation of the Mueller matrix of the optical fiber in response to controlled acoustic perturbations induced by piezo speakers, we compute the corresponding Fisher information operator. Using maximal information states of the Fisher information, it was possible to observe a significant improvement in the performance of the sensor, increasing the signal-to-noise ratio from 4.3 to 37.6 dB, attaining an almost flat response from 1.5 kHz up to 15 kHz. As a proof-of-concept for dynamic audio signal detection, a broadband acoustic signal was also reconstructed with significant gain, demonstrating the usefulness of the introduced formalism for high-precision sensing with polarimetric fiber sensors. (c) 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

Abstract
Solid-state batteries are prominent in today's research landscape due to their advantages in capacity and safety. This work explores anode-less all-solid-state batteries, a configuration with industrial benefits as it avoids handling alkali metal anodes, albeit with room for improvement. To elucidate the intricacies of these batteries, Laser-Induced Breakdown Spectroscopy (LIBS) served as a pivotal analytical tool, primarily focusing on the negative current collector surface where Li+ nucleation occurs from the Li-rich electrolyte. The use of a fiber-laser for breakdown spectroscopy offers advantages over conventional lasers by producing high beam quality, enabling minimal spot size, and ensuring excellent spatial resolution. LIBS is an asset to verify Li presence, discerning its source, assessing nucleation and distinguishing it from electrolyte-derived Li. For instance, in this work utilizing Li2.99Ba0.005ClO as the electrolyte, LIBS is crucial to elucidate the relationship between Li and other elements like Cl, Zn, or Fe, shedding light on key battery performance aspects. LIBS demonstrated a high potential for verifying in situ Li metal nucleation in anode-less cells. This study highlights its effectiveness in conceptual and product development and advanced quality testing. The application of a clustering method enhanced result interpretability and the distinction between electrolyte and in situ anode regions.

Abstract
Spectral imaging is a broad term that refers to the use of a spectroscopy technique to analyze sample surfaces, collecting and representing spatially referenced signals. Depending on the technique utilized, it allows the user to reveal features and properties of objects that are invisible to the human eye, such as chemical or molecular composition. However, the interpretability and interaction with the results are often limited to screen visualization of two-dimensional representations. To surpass such limitations, augmented reality emerges as a promising technology, assisted by recent developments in the integration of spectral imaging datasets onto three-dimensional models. Building on this context, this work explores the integration of spectral imaging with augmented reality, aiming to create an immersive toolset to increase the interpretability and interactivity of the results of spectral imaging analysis. The procedure follows a two-step approach, starting from the integration of spectral maps onto a three-dimensional models, and proceeding with the development of an interactive interface to allow immersive visualization and interaction with the results. The approach and tool developed present the opportunity for a user-centric extension of reality, enabling more intuitive and comprehensive analyses with the potential to drive advancements in various research domains.

Abstract
Management and reuse of wood waste can be a challenging process due to the frequent presence of hazardous contaminants. Conventional detection methods are often limited by the need for excessive sample preparation and lengthy and expensive analysis. Laser-induced Breakdown Spectroscopy (LIBS) is a rapid and micro- destructive technique that can be a promising alternative, providing in-situ and real-time analysis, with minimal to no sample preparation required. In this study, LIBS imaging was used to analyze wood waste samples to determine the presence of contaminants such as As, Ba, Cd, Cr, Cu, Hg, Pb, Sb, and Ti. For this analysis, a methodology based on detecting three lines per element was developed, offering a screening method that can be easily adapted to perform qualitative analysis in industrial contexts with high throughput operations. For the LIBS experimental lines selection, control and reference samples, and a pilot set of 10 wood wastes were analysed. Results were validated by two different X-ray Fluorescence (XRF) systems, an imaging XRF and a handheld XRF, that provided spatial elemental information and spectral information, respectively. The results obtained highlighted LIBS ability to detect highly contaminated samples and the importance of using a 3-line criteria to mitigate spectral interferences and discard outliers. To increase the dataset, a LIBS large-scale study was performed using 100 samples. These results were only corroborated by the XRF-handheld system, as it provides a faster alternative. In particular cases, ICP-MS analysis was also performed. The success rates achieved, mostly above 88 %, confirm the capability of LIBS to perform this analysis, contributing to more sustainable waste management practices and facilitating the quick identifi- cation and remediation of contaminated materials.

Abstract
Spectral Imaging techniques such as Laser-induced Breakdown Spectroscopy (LIBS) and Raman Spectroscopy (RS) enable the localized acquisition of spectral data, providing insights into the presence, quantity, and spatial distribution of chemical elements or molecules within a sample. This significantly expands the accessible information compared to conventional imaging approaches such as machine vision. However, despite its potential, spectral imaging also faces specific challenges depending on the limitations of the spectroscopy technique used, such as signal saturation, matrix interferences, fluorescence, or background emission. To address these challenges, this work explores the potential of using techniques from conventional RGB imaging to enhance the dynamic range of spectral imaging. Drawing inspiration from multi-exposure fusion techniques, we propose an algorithm that calculates a global weight map using exposure and contrast metrics. This map is then used to merge datasets acquired with the same technique under distinct acquisition conditions. With case studies focused on LIBS and Raman Imaging, we demonstrate the potential of our approach to enhance the quality of spectral data, mitigating the impact of the aforementioned limitations. Results show a consistent improvement in overall contrast and peak signal-to-noise ratios of the merged images compared to single-condition images. Additionally, from the application perspective, we also discuss the impact of our approach on sample classification problems. The results indicate that LIBS-based classification of Li-bearing minerals (with Raman serving as the ground truth), is significantly improved when using merged images, reinforcing the advantages of the proposed solution for practical applications.