Abstract
Optical tweezers are an interesting tool to enable single cell analysis, especially when coupled with optical sensing and advanced computational methods. Nevertheless, such approaches are still hindered by system operation variability, and reduced amount of data, resulting in performance degradation when addressing new data sets. In this manuscript, we describe the deployment of an automatic and intelligent optical tweezers setup, capable of trapping, manipulating, and analyzing the physical properties of individual microscopic particles in an automatic and autonomous manner, at a rate of 4 particle per min, without user intervention. Reproducibility of particle identification with the help of machine learning algorithms is tested both for manual and automatic operation. The forward scattered signal of the trapped PMMA and PS particles was acquired over two days and used to train and test models based on the random forest classifier. With manual operation the system could initially distinguish between PMMA and PS with 90% accuracy. However, when using test datasets acquired on a different day it suffered a loss of accuracy around 24%. On the other hand, the automatic system could classify four types of particles with 79% accuracy maintaining performance (around 1% variation) even when tested with different datasets. Overall, the automated system shows an increased reproducibility and stability of the acquired signals allowing for the confirmation of the proportionality relationship expected between the particle size and its friction coefficient. These results demonstrate that this approach may support the development of future systems with increased throughput and reliability, for biosciences applications.

Abstract
Laser-induced breakdown spectroscopy is a spectroscopic technique that allows for fast elemental mapping of heterogeneous samples. Yet, detailed maps need high-resolution sampling grids, which can turn the task into a time-consuming process and can increase sample damage. In this work, we present the implementation of an imaged-based intelligent mesh algorithm that makes use of superpixel segmentation to optimize elemental mapping processes. Our results show that the approach can increase the elemental mapping resolution and decrease acquisition times, fostering opportunities for applications that benefit from minimal sample damage such as heritage analysis, or timely analysis such as industrial applications. © 2022 The Author(s)

Abstract
Hydrogen peroxide is usually added to products to delay the development of microorganisms mainly in milk, hence increasing its stability over time, however the side effects can become devastating to human health.A technique is presented consisting of detecting hydrogen peroxide as an adulterant in milk through a sensor where pretreatment of the sample is not necessary, using a single use membrane. The detection of hydrogen peroxide in fresh-raw, whole, semi-skimmed and skimmed milk was performed using a luminol chem-iluminescence reaction.For hydrogen peroxide water solutions, a linear response was attained from 1.0 x 10-4 to 9.0 x 10-3 %w/w and an LOD (limit of detection) of 3.0 x 10-5 %w/w was determined. An R-squared value of 0.97 and a relative standard deviation lower than 10%, were achieved.Hydrogen peroxide concentration as low as 1.0 x 10-3 %w/w was measured for fresh-raw, skim and whole milk and for semi-skimmed milk, as low as 2.0 x 10-3 %w/w.The methodology presented, as long as our knowledge, is original, rapid, ecological and inexpensive. In regard of the sensitivity obtained, the methodology has great possibility to be applied in the detection of hydrogen peroxide in several areas. It is envisaged monitoring of food quality, agriculture systems and environment pollution.

Abstract
Imaging the spatial distribution of chemical elements at a sample surface is a common application of laserinduced breakdown spectroscopy with vast scientific and technological applications. Yet, typical imaging solutions only explore the creation of two-dimensional maps, which can limit the interpretability of the results and further diagnostics in three-dimensional settings. Within this context, this work explores the combination of spectral imaging techniques and photogrammetry to deploy a versatile solution for the creation of threedimensional spectral imaging models. First, by making use of a numerical algorithm that is able to match features in the spectral image with those of the three-dimensional model, we show how to match the mesh from distinct sensor modalities. Then, we describe a possible visualization workflow, making use of dedicated photogrammetry and visualization software to easily deploy interactive models. Overall, the results demonstrate the versatility of our approach and pave for the development of novel spectral imaging diagnostic strategies that are able to deliver better qualitative analysis and insight in the three-dimensional space.

Abstract
Over the past decade, molecular imprinting (MI) technologyhasmade tremendous progress, and the advancements in nanotechnology havebeen the major driving force behind the improvement of MI technology.The preparation of nanoscale imprinted materials, i.e., molecularlyimprinted polymer nanoparticles (MIP NPs, also commonly called nanoMIPs),opened new horizons in terms of practical applications, includingin the field of sensors. Currently, hydrogels are very promising forapplications in bioanalytical assays and sensors due to their highbiocompatibility and possibility to tune chemical composition, size(microgels, nanogels, etc.), and format (nanostructures, MIP film,fibers, etc.) to prepare optimized analyte-responsive imprinted materials.This review aims to highlight the recent progress on the use of hydrogelMIP NPs for biosensing purposes over the past decade, mainly focusingon their incorporation on sensing devices for detection of a fundamentalclass of biomolecules, the peptides and proteins. The review beginsby directing its focus on the ability of MIPs to replace biologicalantibodies in (bio)analytical assays and highlight their great potentialto face the current demands of chemical sensing in several fields,such as disease diagnosis, food safety, environmental monitoring,among others. After that, we address the general advantages of nanosizedMIPs over macro/micro-MIP materials, such as higher affinity towardtarget analytes and improved binding kinetics. Then, we provide ageneral overview on hydrogel properties and their great advantagesfor applications in the field of Sensors, followed by a brief descriptionon current popular routes for synthesis of imprinted hydrogel nanospherestargeting large biomolecules, namely precipitation polymerizationand solid-phase synthesis, along with fruitful combination with epitopeimprinting as reliable approaches for developing optimized protein-imprintedmaterials. In the second part of the review, we have provided thestate of the art on the application of MIP nanogels for screeningmacromolecules with sensors having different transduction modes (optical,electrochemical, thermal, etc.) and design formats for single use,reusable, continuous monitoring, and even multiple analyte detectionin specialized laboratories or in situ using mobiletechnology. Finally, we explore aspects about the development of thistechnology and its applications and discuss areas of future growth.