Studying the concentration of polymers in blended microplastics using 2D and 3D Raman mapping.

The combination of different polymers in the form of blended plastics has been used in the plastic industry for a long time. Nevertheless, analyses of microplastics (MPs) have been mainly limited to the study of particles made of single-type polymers. Accordingly, two members of the Polyolefins (POs) family, i.e., Polypropylene (PP) and Low-density Polyethylene (LDPE) are blended and extensively studied in this work due to their applications in industry as well as abundance in the environment. It is shown that 2-D Raman mapping only provides information about the surface of blended MPs (B-MPs). While complimentary 3-D volume analysis is needed to fully understand the presence of various polymers in such complex samples. Therefore, 3-D Raman mapping is applied to visualize the morphology of the distribution of polymers within the B-MPs together with the quantitative estimation of their concentrations. A parameter defined as the concentration estimate error (CEE) evaluates the precision of the quantitative analysis. Furthermore, the impact of four excitation wavelengths 405, 532, 633, and 785 nm is investigated on the obtained results. Finally, the application of a line-shaped laser beam profile (line-focus) is introduced for reducing the measurement time from 56 to 2 h.

Batch analysis of microplastics in water using multi-angle static light scattering and chemometric methods.

Size and concentration are two important parameters for the analysis of microplastics (MPs) in water. The analytical tools reported so far extract this information in a single-particle analysis mode, dramatically increasing the analysis time. Here, we present a combination of multi-angle static light scattering technique, called "Goniophotometry", with chemometric multivariate data processing for the batch analysis of size and concentration of MPs in water. Nine different sizes of polystyrene (PS) MPs with diameters between 500 nm and 20 µm are investigated in two different scenarios with uniform (monodisperse) and non-uniform (polydisperse) size distribution of MPs, respectively. It is shown that Principal Component Analysis (PCA) can reveal the existing relationship between the scattering data of mono- and polydisperse samples according to the size distribution of MPs in mixtures. Therefore, a Linear Discriminant Analysis (LDA) model is constructed based on the PCA of scattering data of PS monodisperse samples and is subsequently employed to classify the size of MPs not only in unknown mono- and polydisperse PS samples, but also for other types of MPs such as Polyethylene (PE) and Polymethylmethacrylate (PMMA). When the size of MPs is classified, their concentration is measured using a simple linear fit. Finally, a Linear Least Square (LLS) model is used to evaluate the reproducibility of the measurements.

Benchmarking Spectroscopic Techniques Combined with Machine Learning to Study Oak Barrels for Wine Ageing.

Due to its physical, chemical, and structural properties, oakwood is widely used in the production of barrels for wine ageing. When in contact with the wine, oak continuously releases aromatic compounds such as lignin, tannin, and cellulose to the liquid. Due to the release process, oak loses its characteristic aromatic compounds in time; hence, the flavour that it gives to the enclosed wine decreases for repeated wine refills and a barrel replacement is required. Currently, the estimation of the maximum number of refills is empirical and its underestimation or overestimation can impose unnecessary costs and impair the quality of the wine. Therefore, there is a clear need to quantify the presence of the aforementioned aromatic compounds in an oak barrel prior to a refill. This work constitutes a study to examine noninvasive optical biosensing techniques for the characterization of an oak barrel used in wine ageing, towards the development of a model to unveil its lifespan without inducing structural damage. Spectroscopic diagnostic techniques, such as reflectance, fluorescence, and Raman scattering measurements are employed to assess the change in the chemical composition of the oakwood barrel (tannin and lignin presence) and its dependence on repeated refills. To our knowledge, this is the first time that we present a benchmarking study of oak barrel ageing characteristics through spectroscopic methods for the wine industry. The spectroscopic data are processed using standard chemometric techniques, such as Linear Discriminant Analysis and Partial Least Squares Discriminant Analysis. Results of a study of fresh, one-time-used, and two-times-used oak barrel samples demonstrate that reflectance spectroscopy can be a valuable tool for the characterization of oak barrels. Moreover, reflectance spectroscopy has demonstrated the most accurate classification performance. The highest accuracy has been obtained by a Partial Least Squares Discriminant Analysis model that has been able to classify all the oakwood samples from the barrels with >99% accuracy. These preliminary results pave a way for the application of cost-effective and non-invasive biosensing techniques based on reflectance spectroscopy for oak barrels assessment.

Increasing the Microfabrication Performance of Synthetic Hydrogel Precursors through Molecular Design.

Implementation of hydrogel precursors in two-photon polymerization (2PP) technology provides promising opportunities in the tissue engineering field thanks to their soft characteristics and similarity to extracellular matrix. Most of the hydrogels, however, are prone to post-fabrication deformations, leading to a mismatch between the computer-aided design and the printed structure. In the present work, we have developed novel synthetic hydrogel precursors to overcome the limitations associated with 2PP processing of conventional hydrogel precursors such as post-processing deformations and a narrow processing window. The precursors are based on a poly(ethylene glycol) backbone containing urethane linkers and are, on average, functionalized with six acrylate terminal groups (three on each terminal group). As a benchmark material, we exploited a precursor with an identical backbone and urethane linkers, albeit functionalized with two acrylate groups, that were reported as state-of-the-art. An in-depth characterization of the hexafunctional precursors revealed a reduced swelling ratio (<0.7) and higher stiffness (>36 MPa Young's modulus) compared to their difunctional analogs. The superior physical properties of the newly developed hydrogels lead to 2PP-based fabrication of stable microstructures with excellent shape fidelity at laser scanning speeds up to at least 90 mm s-1, in contrast with the distorted structures of conventional difunctional precursors. The hydrogel films and microscaffolds revealed a good cell interactivity after functionalization of their surface with a gelatin methacrylamide-based coating. The proposed synthesis strategy provides a one-pot and scalable synthesis of hydrogel building blocks that can overcome the current limitations associated with 2PP fabrication of hydrogel microstructures.

AFM1 Detection in Milk by Fab' Functionalized Si3N4 Asymmetric Mach-Zehnder Interferometric Biosensors.

Aflatoxins (AF) are naturally occurring mycotoxins, produced by many species of Aspergillus. Among aflatoxins, Aflatoxin M1 (AFM1) is one of the most frequent and dangerous for human health. The acceptable maximum level of AFM1 in milk according to EU regulation is 50 ppt, equivalent to 152 pM, and 25 ppt, equivalent to 76 pM, for adults and infants, respectively. Here, we study a photonic biosensor based on Si 3 N 4 asymmetric Mach-Zehnder Interferometers (aMZI) functionalized with Fab' for AFM1 detection in milk samples (eluates). The minimum concentration of AFM1 detected by our aMZI sensors is 48 pM (16.8 pg/mL) in purified and concentrated milk samples. Moreover, the real-time detection of the ligand-analyte binding enables the study of the kinetics of the reaction. We measured the kinetic rate constants of the Fab'-AFM1 interaction.

Asymmetric Mach-Zehnder Interferometer Based Biosensors for Aflatoxin M1 Detection.

In this work, we present a study of Aflatoxin M1 detection by photonic biosensors based on Si3N4 Asymmetric Mach-Zehnder Interferometer (aMZI) functionalized with antibodies fragments (Fab'). We measured a best volumetric sensitivity of 104 rad/RIU, leading to a Limit of Detection below 5 × 10(-7) RIU. On sensors functionalized with Fab', we performed specific and non-specific sensing measurements at various toxin concentrations. Reproducibility of the measurements and re-usability of the sensor were also investigated.

Design and Optimization of SiON Ring Resonator-Based Biosensors for Aflatoxin M1 Detection.

In this article, we designed and studied silicon oxynitride (SiON) microring-based photonic structures for biosensing applications. We designed waveguides, directional couplers, and racetrack resonators in order to measure refractive index changes smaller than 10-6 refractive index units (RIU). We tested various samples with different SiON refractive indexes as well as the waveguide dimensions for selecting the sensor with the best performance. Propagation losses and bending losses have been measured on test structures, along with a complete characterization of the resonator's performances. Sensitivities and limit of detection (LOD) were also measured using glucose-water solutions and compared with expected results from simulations. Finally, we functionalized the resonator and performed sensing experiments with Aflatoxin M1 (AFM1). We were able to detect the binding of aflatoxin for concentrations as low as 12.5 nm. The results open up the path for designing cost-effective biosensors for a fast and reliable sensitive analysis of AFM1 in milk.