Abstract
Optical tweezers use light to trap and manipulate mesoscopic scaled particles with high precision making them a useful tool in a plethora of natural sciences, with emphasis on biological applications. In principle, the Brownian-like dynamics reflect trapped particle properties making it a robust source of information. In this work, we exploit this information by plotting histogram based images of 250ms of position or displacement used as input to a Convolution Neural Network. Results of 2-fold stratified cross-validation show satisfying classifications between sizes or types of particles: Polystyrene and Polymethilmethacrylate thus highlighting the potential of CNN approaches in faster and non-invasive applications in intelligent opto and microfluidic devices using optical trapping tools.

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.