Nanostructured luminescent chemosensors for environmental and biomedical applications

Nanomaterials are nowadays widely recognised as advantageous sensing tools due to their unique properties. Some natural nanomaterials, such as DNA or hyaluronic acid analysed in this PhD thesis, have an intrinsic biocompatibility that overcomes a series of issues in the field of sensing in biological environments. Therefore, the main aim of this project was to derivatize HA chains with luminescent dyes - both organic and metal complexes - in order to obtain natural polymer-based optical sensors. A derivatization of HA with these moieties was obtained and a photophysical characterization was provided. To prove their sensing ability towards nanomaterials, the interaction with. PluS Nanoparticles, featuring an outer PEG shell, was tested. It was mostly demonstrated that the main features of the luminophores used were present in the HA nanogels as well. For example, HA@Dansyl was proven to be a luminescent probe able to sense different environment polarities. Furthermore, in HA@PA the amount of excimers/monomers emission was found to be relatable to the degree of entanglement of HA chains, that changes upon interactions with nanoparticles. Moreover, two ruthenium bipyridyl derivatives were linked to HA and it was found out that HA interacts with long DNA sequences. Also, the presence of BPA, a small molecule of environmental concern, was detected using (i) an already studied hyaluronic acid derivative with rhodamine (HA@RB) , (ii) a dizinc ruthenium complex coordinating BPA to the metal centres, and (iii) a new probe constituted by PluSNPs@DEAC and HA@RB. Despite all the systems were found to be able to detect BPA, the latter probe presented advantages in terms of sensitivity. Furthermore, the chapter 2 of this thesis is focused on the detection of a NF-κB protein in PC3 cancer cells. via confocal microscopy by following a FRET signal variation on a triplex-hairpin derivatized with a FRET couple of dyes.

Advanced Organic Materials: from Additive Manufacturing to Organic Electronics and Biomedical Applications

This manuscript represents an overview on the studies I was involved in during my PhD at the Industrial Chemistry Department “Toso Montanari”, in the ASOM (Advanced Smart Organic Materials) research group under the supervision of Prof. Letizia Sambri and Prof. Mauro Comes Franchini. Those research have been focused on the development of organic materials for advanced applications in different fields, among which organic electronics, additive manufacturing (3D Printing) and biomedical applications can be underlined.

Metrological characterization of sensors and instrumentation for distribution grid monitoring and electrical asset diagnostics

The Smart Grid needs a large amount of information to be operated and day by day new information is required to improve the operation performance. It is also fundamental that the available information is reliable and accurate. Therefore, the role of metrology is crucial, especially if applied to the distribution grid monitoring and the electrical assets diagnostics. This dissertation aims at better understanding the sensors and the instrumentation employed by the power system operators in the above-mentioned applications and studying new solutions. Concerning the research on the measurement applied to the electrical asset diagnostics: an innovative drone-based measurement system is proposed for monitoring medium voltage surge arresters. This system is described, and its metrological characterization is presented. On the other hand, the research regarding the measurements applied to the grid monitoring consists of three parts. The first part concerns the metrological characterization of the electronic energy meters’ operation under off-nominal power conditions. Original test procedures have been designed for both frequency and harmonic distortion as influence quantities, aiming at defining realistic scenarios. The second part deals with medium voltage inductive current transformers. An in-depth investigation on their accuracy behavior in presence of harmonic distortion is carried out by applying realistic current waveforms. The accuracy has been evaluated by means of the composite error index and its approximated version. Based on the same test setup, a closed-form expression for the measured current total harmonic distortion uncertainty estimation has been experimentally validated. The metrological characterization of a virtual phasor measurement unit is the subject of the third and last part: first, a calibrator has been designed and the uncertainty associated with its steady-state reference phasor has been evaluated; then this calibrator acted as a reference, and it has been used to characterize the phasor measurement unit implemented within a real-time simulator.

Content-aware approach for improving biomedical image analysis: an interdisciplinary study series

Biomedicine is a highly interdisciplinary research area at the interface of sciences, anatomy, physiology, and medicine. In the last decade, biomedical studies have been greatly enhanced by the introduction of new technologies and techniques for automated quantitative imaging, thus considerably advancing the possibility to investigate biological phenomena through image analysis. However, the effectiveness of this interdisciplinary approach is bounded by the limited knowledge that a biologist and a computer scientist, by professional training, have of each other’s fields. The possible solution to make up for both these lacks lies in training biologists to make them interdisciplinary researchers able to develop dedicated image processing and analysis tools by exploiting a content-aware approach. The aim of this Thesis is to show the effectiveness of a content-aware approach to automated quantitative imaging, by its application to different biomedical studies, with the secondary desirable purpose of motivating researchers to invest in interdisciplinarity. Such content-aware approach has been applied firstly to the phenomization of tumour cell response to stress by confocal fluorescent imaging, and secondly, to the texture analysis of trabecular bone microarchitecture in micro-CT scans. Third, this approach served the characterization of new 3-D multicellular spheroids of human stem cells, and the investigation of the role of the Nogo-A protein in tooth innervation. Finally, the content-aware approach also prompted to the development of two novel methods for local image analysis and colocalization quantification. In conclusion, the content-aware approach has proved its benefit through building new approaches that have improved the quality of image analysis, strengthening the statistical significance to allow unveiling biological phenomena. Hopefully, this Thesis will contribute to inspire researchers to striving hard for pursuing interdisciplinarity.

Data assimilation for advanced cross-scale unstructured-grid ocean modeling.

The coastal ocean is a complex environment with extremely dynamic processes that require a high-resolution and cross-scale modeling approach in which all hydrodynamic fields and scales are considered integral parts of the overall system. In the last decade, unstructured-grid models have been used to advance in seamless modeling between scales. On the other hand, the data assimilation methodologies to improve the unstructured-grid models in the coastal seas have been developed only recently and need significant advancements. Here, we link the unstructured-grid ocean modeling to the variational data assimilation methods. In particular, we show results from the modeling system SANIFS based on SHYFEM fully-baroclinic unstructured-grid model interfaced with OceanVar, a state-of-art variational data assimilation scheme adopted for several systems based on a structured grid. OceanVar implements a 3DVar DA scheme. The combination of three linear operators models the background error covariance matrix. The vertical part is represented using multivariate EOFs for temperature, salinity, and sea level anomaly. The horizontal part is assumed to be Gaussian isotropic and is modeled using a first-order recursive filter algorithm designed for structured and regular grids. Here we introduced a novel recursive filter algorithm for unstructured grids. A local hydrostatic adjustment scheme models the rapidly evolving part of the background error covariance. We designed two data assimilation experiments using SANIFS implementation interfaced with OceanVar over the period 2017-2018, one with only temperature and salinity assimilation by Argo profiles and the second also including sea level anomaly. The results showed a successful implementation of the approach and the added value of the assimilation for the active tracer fields. While looking at the broad basin, no significant improvements are highlighted for the sea level, requiring future investigations. Furthermore, a Machine Learning methodology based on an LSTM network has been used to predict the model SST increments.

Development of effect-based bio-chemiluminescent cell bioassays for nutraceutical and biomedical applications

The final goal of the bioassay developed during the first two years of my Ph.D. was its application for the screening of antioxidant activity of nutraceuticals and for monitoring the intracellular H2O2 production in peripheral blood mononuclear cells (PBMCs) from hypercholesterolemic subjects before and after two months treatment with Evolocumab, a new generation LDL-cholesterol lowering drug. Moreover, a recombinant bioluminescent protein was developed during the last year using the Baculovirus expression system in insect cells. In particular, the protein combines the extracellular domain (ECD) of the Notch high affinity mutated form of one of the selective Notch ligands defined as Jagged 1 (Jag1) with a red emitting firefly luciferase since a pivotal role of “aberrant” Notch signaling activation in colorectal cancer (CRC) was reported. The probe was validated and characterized in terms of analytical performance and through imaging experiments, in order to understand if Jagged1-FLuc binding correlates with a Notch signaling overexpression and activation in CRC progression.