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

Abstract
Spectral imaging is a technique that captures spectral information from a scene and maps it onto a 2D image, featuring the potential to reveal hidden features and properties of objects that are invisible to the human eye, such as elemental and molecular compositions. Augmented reality (AR), on the other hand, is a technology that enhances the perception of reality by superimposing digital information on the physical world. While these technologies have different purposes, they can be considered one and the same in terms of providing an user-centric extension of reality. Spectral imaging provides the information that can reveal the underlying nature of objects, while AR provides the method of visualization that can display the information in an intuitive and interactive way. In this work, we present a novel Unity toolkit that combines spectral imaging and a HoloLens 2 AR device to create an interactive and immersive experience for the user. The toolkit enables the interactive visualization of various elemental maps of a 3D rock model in AR using a simple and intuitive interface. With this technique, the user can select a sample model and an elemental map from a preloaded asset library and then see the map projected onto the rock model in AR, using simple interactions such as zoom adjustment, rotation, and pan of the models to explore features and properties in detail. The toolkit offers several advantages, including better contextual interpretation of the spectral data by placing it in relation to the shape and texture of the rock, increased user engagement and curiosity through the creation of a realistic and immersive experience, and ease of decision-making through the provision of comparative tools. In short, by combining spectral imaging and AR, we present an innovative approach that can enrich the user experience and expand the user knowledge of the environment.

Abstract
Combining data from different sensing modalities has been a promising research topic for building better and more reliable data-driven models. In particular, it is known that multimodal spectral imaging can improve the analytical capabilities of standalone spectroscopy techniques through fusion, hyphenation, or knowledge distillation techniques. In this manuscript, we focus on the latter, exploring how one can increase the performance of a Laser-induced Breakdown Spectroscopy system for mineral classification problems using additional spectral imaging techniques. Specifically, focusing on a scenario where Raman spectroscopy delivers accurate mineral classification performance, we show how to deploy a knowledge distillation pipeline where Raman spectroscopy may act as an autonomous supervisor for LIBS. For a case study concerning a challenging Li-bearing mineral identification of spodumene and petalite, our results demonstrate the advantages of this method in improving the performance of a single-technique system. LIBS trained with labels obtained by Raman presents an enhanced classification performance. Furthermore, leveraging the interpretability of the model deployed, the workflow opens opportunities for the deployment of assisted feature discovery pipelines, which may impact future academic and industrial applications.