Abstract
Spectral imaging is a powerful technology that uses spatially referenced spectral signatures to create informative visual maps of sample surfaces that can reveal more than what conventional RGB-visual images can show. Indeed, different spectroscopy modalities can provide different information about the same sample: for instance, Laser-Induced Breakdown Spectroscopy (LIBS) imaging can detect the presence of specific elements on the surface, while Raman imaging can identify the molecular structures and compositions of the sample, both of which have potential applications in various industrial processes, from quality control to material sorting. In the path from science to technology, the increasing accessibility to such solutions and the strong market pull have opened a window of opportunity for innovative multimodal imaging solutions, where information from distinct sources is set to be combined in order to enhance the capabilities of the single modality system. However, the practical implementation of multimodal spectral imaging is still a challenge, despite its theoretical potential, and as such, it is yet to be achieved. In this work, we will go over multimodal spectral knowledge distillation, a disruptive approach to multimodal spectral imaging techniques that tries to explore the combination of two techniques to capitalize on their individual strengths. In specific, this approach allows us to utilize one technique as an autonomous supervisor for the other, leveraging the higher degree of knowledge and interpretability of one of the techniques to increase the performance and transparency of the other. We present some example scenarios with LIBS and HSI and Raman spectroscopy and LIBS, discussing the impact of this new approach for scientific and technological applications.

Abstract
As lithium-bearing minerals become critical raw materials for the field of energy storage and advanced technologies, the development of tools to accurately identify and differentiate these minerals is becoming essential for efficient resource exploration, mining, and processing. Conventional methods for identifying ore minerals often depend on the subjective observation skills of experts, which can lead to errors, or on expensive and time-consuming techniques such as Inductively Coupled Plasma Mass Spectrometry (ICP-MS) or Optical Emission Spectroscopy (ICPOES). More recently, Raman Spectroscopy (RS) has emerged as a powerful tool for characterizing and identifying minerals due to its ability to provide detailed molecular information. This technique excels in scenarios where minerals have similar elemental content, such as petalite and spodumene, by offering distinct vibrational information that allows for clear differentiation between such minerals. Considering this case study and its particular relevance to the lithium- mining industry, this manuscript reports the development of an unsupervised methodology for lithium-mineral identification based on Raman Imaging. The deployed machine-learning solution provides accurate and interpretable results using the specific bands expected for each mineral. Furthermore, its robustness is tested with additional blind samples, providing insights into the unique spectral signatures and analytical features that enable reliable mineral identification.

Abstract
Sensing at the single cell level can provide insights into its dynamics and heterogeneity, yielding information otherwise unattainable with traditional biological methods where average population behavior is observed. In this context, optical tweezers provide the ability to select, separate, manipulate and identify single cells or other types of microparticles, potentially enabling single cell diagnostics. Forward or backscatter analysis of the light interacting with the trapped cells can provide valuable insights on the cell optical, geometrical and mechanical properties. In particular, the combination of tweezers systems with advanced machine learning algorithms can enable single cell identification capabilities. However, typical processing pipelines require a training stage which often struggles when trying to generalize to new sets of data. In this context, fully automated tweezers system can provide mechanisms to obtain much larger datasets with minimum effort form the users, while eliminating procedural variability. In this work, a pipeline for full automation of optical tweezers systems is discussed. A performance comparison between manually operated and fully automated tweezers systems is presented, clearly showing advantages of the latter. A case study demonstrating the ability of the system to discriminate molecular binding events on microparticles is presented.

Abstract
Carbon dioxide (CO2) holds paramount significance in nature, serving as a vital component in Earth's ecosystems. Its evaluation has become increasingly important across various sectors, spanning from environmental conservation to industrial operations. Therefore, this study investigates the viability of utilizing a pH colorimetric dye as a CO2-sensitive material. The material's effectiveness relies on chemical modifications induced in the dye structure through the action of a phase transfer agent, which establishes a stable equilibrium with the dye, thereby promoting its receptivity to CO2 molecules. As the resulting physicochemical changes primarily exhibit colorimetric alterations, an optical system was developed to assess the performance of this material upon exposure to CO2. Employing a dual-wavelength method, the system also incorporates a ratiometric relationship between the two signals to provide the most precise information. The conducted experiments generated promising results when the dye was subjected to varying concentrations of CO2, ranging from 0% to 4%, in comparison to nitrogen (N-2). The application of the ratiometric method emerged as a crucial factor in this system, enabling its potential use in environments characterized by instability. Finally, the dye-sensitive characteristics experienced enhancement through the integration of an ionic liquid within the membrane matrix.

Abstract
Optical microbubble resonators (OMBRs)-understood as localized thin wall bulges induced in silica microcapillaries-are gaining an ever-growing interest in microfluidic sensing applications due to their capability to sustain whispering gallery modes (WGMs) and confine the fluidic sample within their own hollow-core microcavity. Currently, most applications use an external tapered optical fiber for coupling light to the resonator. This arrangement is known to be fragile and prone to vibrations. In this work, an alternative approach, based on coupling OMBR with a femtosecond (fs) laser-written optical waveguides, integrated at the surface of fused silica substrate, is proposed. In this configuration, a stable and robust final structure is accomplished by gluing the two ends of the microcapillary, on which the OMBR is made, to the substrate. The OMBR quality factors, measured at the excitation wavelength of 1540 nm, show values close to 10(4) in the case of a water-filled cavity, with a maximum coupling efficiency of up to 6.5%. Finally, the operation of the integrated optical devices as refractometers is demonstrated by delivering different solutions with successively increasing concentrations of NaCl inside the OMBR. An average sensitivity of 45 nm/RIU is obtained, yielding a resolution of 4.4x10(-5) RIU, creating the potential for this platform to be applied in chemical/biochemical sensing.

Abstract
Surface plasmon resonance (SPR) conventionally occurs at the interface of a thin metallic film and an external dielectric medium in fiber optics through core-guided light. However, this work introduces theoretical and experimental evidence suggesting that the SPR in optical fibers can also be induced through light scattering from Au nanoparticles (NPs) on the thin metallic film, defined as nanoparticle-induced SPR (NPI-SPR). This method adheres to phase-matching conditions between SPR dispersion curves and the wave vectors of scattered light from Au NPs. Experimentally, these conditions are met on an etched optical fiber, enabling direct interaction between light and immobilized Au NPs. Compared to SPR, NPI-SPR exhibits stronger field intensity in the external region and wavelength tuning capabilities (750 to 1250 nm) by varying Au NP diameters (20 to 90 nm). NPI-SPR demonstrates refractive index sensitivities of 4000 to 4416 nm per refractive index unit, nearly double those of typical SPR using the same optical fiber configuration sans Au NPs. Additionally, NPI-SPR fiber configuration has demonstrated its applicability for developing biosensors, achieving a remarkable limit of detection of 0.004 nm for thrombin protein evaluation, a twenty-fold enhancement compared to typical SPR. These findings underscore the intrinsic advantages of NPI-SPR for sensing. Surface plasmon resonance (SPR) typically occurs at the interface of a thin metallic film and a dielectric medium in fiber optics. This work presents evidence of nanoparticle-induced SPR (NPI-SPR) in optical fibers through light scattering from Au nanoparticles on the thin metallic film. NPI-SPR offers stronger field intensity, wavelength tuning, and enhanced refractive index sensitivities, making it advantageous for biosensing applications. image