Abstract
Apart from radiation, which constitutes the primary source of information in laser-induced breakdown spectroscopy, the process is accompanied by secondary processes such as shock wave generation and sound emission. In this manuscript, we explore the possibility of relating plasma properties with the sound from the shock waves in multiple materials, from metals to minerals. By analyzing the behavior of shock wave sound from homogeneous reference metallic targets, we investigate the relation between plasma properties and sound signal, demonstrating that distinct materials and plasma characteristics correspond to distinct plasma sound fingerprints. © Published under licence by IOP Publishing Ltd.

Abstract
While laser-induced breakdown spectroscopy is often used as a standalone technique, recent years saw an increasing interest in their combination with additional techniques towards multimodal sensing solutions capable of enhancing the capabilities of this technological solution. In this work, we try to identify possible synergies that arise from merging the analysis of laser-induced breakdown spectroscopy with that from a hyperspectral scanning of the sample, comparing it with the performance of standalone solutions. Having investigated the multimodal approach for a case study involving the identification of lithium minerals, our preliminary results demonstrate that while both solutions can provide reasonable results for qualitative mineral identification, they feature advantages and disadvantages that shall be taken into further consideration. Nevertheless, when working in collaboration, the results enclosed suggest that an integrated tandem solution can be an interesting tool for material analysis for research and industrial applications, combining the best of both instruments. © Published under licence by IOP Publishing Ltd.

Abstract
The possibility to map the element distribution on a sample surface is one of the interesting applications of laser-induced breakdown spectroscopy that has been extensively explored in recent years. In this manuscript, we explore the combination of photogrammetry and LIBS techniques for the creation of a three-dimensional model of the map of the elements on the surface of the sample. Using a dedicated photogrammetry solution and software, we reconstruct the three-dimensional model of the mineral sample whose mesh is later exploited for the interactive interpretation of the results. Then, making use of Paraview software, which integrates production algorithms and computing performance in a unified solution for scientific purposes, we establish a process pipeline that allows the creation of an interactive three-dimensional model with the spatial distribution of the target elements on top of the sample surface. Our results demonstrate that combining these two techniques can give us a valuable resource for better qualitative analysis and insight, providing an innovative three-dimensional modeling solution that may open the door to a new range of possibilities, from quality control technology involving alloys and mechanical parts to interactive teaching environments for geo and biosciences, just to name a few examples. © Published under licence by IOP Publishing Ltd.

Abstract
Optical trapping provides a way to isolate, manipulate, and probe a wide range of microscopic particles. Moreover, as particle dynamics are strongly affected by their shape and composition, optical tweezers can also be used to identify and classify particles, paving the way for multiple applications such as intelligent microfluidic devices for personalized medicine purposes, or integrated sensing for bioengineering. In this work, we explore the possibility of using properties of the forward scattered radiation of the optical trapping beam to analyze properties of the trapped specimen and deploy an autonomous classification algorithm. For this purpose, we process the signal in the Fourier domain and apply a dimensionality reduction technique using UMAP algorithms, before using the reduced number of features to feed standard machine learning algorithms such as K-nearest neighbors or random forests. Using a stratified 5-fold cross-validation procedure, our results show that the implemented classification strategy allows the identification of particle material with accuracies up to 80%, demonstrating the potential of using signal processing techniques to probe properties of optical trapped particles based on the forward scattered light. Furthermore, preliminary results of an autonomous implementation in a standard experimental optical tweezers setup show similar differentiation capabilities for real-time applications, thus opening some opportunities towards technological applications such as intelligent microfluidic devices and solutions for biochemical and biophysical sensing. © Published under licence by IOP Publishing Ltd.

Abstract
To automatically trap, manipulate and probe physical properties of micron-sized particles is a step of paramount importance for the development of intelligent and integrated optomicrofluidic devices. In this work, we aim at implementing an automatic classifier of micro-particles immersed in a fluid based on the concept of optical tweezers. We describe the automation steps of an experimental setup together with the implemented classification models using the forward scattered signal. The results show satisfactory accuracy around 80% for the identification of the type and size of particles using signals of 250 milliseconds of duration, which paves the path for future improvements towards real-time analysis of the trapped specimens.

Abstract
Optical trapping is a versatile and non-invasive technique for single particle manipulation. As such, it can be widely applied in the domains of particle identification and classification and thus used as a tool for monitoring physical and chemical processes. This creates an opportunity for integrating the method seamlessly into optofluidic chips, provided it can be automatized. Yet even though OT is well established in multiple scientific domains, a full stack approach to its integration into other technological devices is still lacking. This calls for solutions in tasks such as automatic trapping and signal analysis. In this manuscript, we describe the implementation of an algorithm seeking autonomous particle location and trapping. The methodology is based upon image-processing, allowing for particle location using real time image segmentation. A local thresholding algorithm is applied, followed by morphological techniques for closing shapes and excluding non-bounded regions - after which only the particles remain on the image. Once the centroid is identified, the stage is translated accordingly by piezo-electric actuators, followed by the laser activation. In this way, trapping is achieved, and one may proceed to analyze the forward scattered optical signal, after which a new particle inside the actuators range may be automatically trapped. This development, when compared with existent solutions involving holographic optical tweezers, allows for similar capabilities without using a spatial light modulator, thus dramatically reducing the setup costs of autonomous OT solutions. Therefore, when combined with particle classification techniques, this method is well suited for integration into possible optofluidic chips for autonomous sensing and monitoring of biochemical samples. © Published under licence by IOP Publishing Ltd.