20 resultados para flow-based
Resumo:
After initial efforts in the late 1980s, the interest in thermochemiluminescence (TCL) as an effective detection technique has gradually faded due to some drawbacks, such as the high temperatures required to trigger the light emission and the relatively low intensities, which determined a poor sensitivity. Recent advances made with the adoption of variably functionalized 1,2-dioxetanes as innovative luminophores, have proved to be a promising approach for the development of reagentless and ultrasensitive detection methods exploitable in biosensors by using TCL compounds as labels, as either single molecules or included in modified nanoparticles. In this PhD Thesis, a novel class of N-substituted acridine-containing 1,2-dioxetanes was designed, synthesized, and characterized as universal TCL probes endowed with optimal emission-triggering temperatures and higher detectability particularly useful in bioanalytical assays. The different decorations introduced by the insertion of both electron donating (EDGs) and electron withdrawing groups (EWGs) at the 2- and 7-positions of acridine fluorophore was found to profoundly affect the photophysical properties and the activation parameters of the final 1,2-dioxetane products. Challenges in the synthesis of 1,2-dioxetanes were tackled with the recourse to continuous flow photochemistry to achieve the target parent compound in high yields, short reaction time, and easy scalability. Computational studies were also carried out to predict the olefins reactivity in the crucial photooxygenation reaction as well as the final products stability. The preliminary application of TCL prototype molecule has been performed in HaCaT cell lines showing the ability of these molecules to be detected in real biological samples and cell-based assays. Finally, attempts on the characterization of 1,2-dioxetanes in different environments (solid state, optical glue and nanosystems) and the development of bioconjugated TCL probes will be also presented and discussed.
Resumo:
The research work described in this thesis concerns materials for both energy storage and sensoristics applications. Firstly, the synthesis and characterization of magnetite (Fe3O4) functionalyzed with [3-(2-propynylcarbamate)propyl]triethoxysilane (PPTEOS) capable to reduce the gold precursor chloroauric acid (HAuCl4) without the need of additional reducing or stabilising agents is described. These nanoparticles were tested to improve performances of symmetric capacitors based on polyaniline and graphite foil. Energy storage applications were investigated also during six months stay at EPFL University of Lausanne where an investigation about different tailored catalysts for Oxygen Evolution Reaction in a particular Redox Flow Battery was carried out. For what concerns sensing applications, new materials based on cellulose modified with polyaniline and poly(2-acrylamido-2-methyl-1-propanesulfonic acid) (PAAMPSA) were synthesized, characterized and applied to monitor pressure, humidity, heart rate and lastly, bread fermentation in collaboration with the University of Fribourg and Zurich. The characterizations of all the materials investigated compriseed numerous techniques such as infrared attenuated total reflectance spectroscopy (IR-ATR), thermogravimetric analysis (TGA), scanning and transmission electron microscopy (SEM and TEM), alongside linear and cyclic voltammetry (LSV and CV), electrochemical impedance spectroscopy (EIS) and chronoamperometric analyses.
Resumo:
Atrial fibrillation (AF) is a widespread arrhythmia, associated with higher risk of stroke, sleep disorders and dementia. In some conditions, electrical cardioversion (ECV) represents the best choice for rhythm control. Nowadays, there is a growing interest in developing new devices for screening and monitoring of AF patients. We aimed to improve acute efficacy of ECV procedure and to explore the feasibility of the use of new wearable devices for monitoring in candidates to AF ECV. We compared antero-apical pads vs antero-posterior patches approach for AF ECV, and we elaborated a decision algorithm to improve acute efficacy. After, we evaluated the feasibility of the use of new wearable devices for monitoring of candidates to AF ECV. In particular, we analysed the effect of AF ECV on heart rate variability and vascular age parameters derived from PPG signals registered with Empatica (CE 1876/MDD 93/42/EEC), and on EEG pattern registered with Neurosteer (Israel). From December 2005 to September 2019, 492 patients were enrolled. We evaluated acute efficacy of the two approaches for AF ECV and we elaborated a decision algorithm based on body surface area, weight, and height. The decision algorithm improved first shock efficacy (93.2% vs. 87.2%, p=0.025). From 1st November 2021 to 1st April 2022, 24 patients were enrolled in PPEEG-AF pilot study. Considering vascular age parameters, a significant reduction in TPR and a wave was observed (p<0.001). Considering sleep patterns, a tendency to higher coherence was observed in registrations acquired during AF, or considering signals registered for each patient independently from AF. The new decision algorithm improved acute efficacy and reduced costs associated with adhesive patches. Significant modifications were observed on vascular age parameters measured before and after ECV, and a possible AF effect on sleep pattern was noticed. More data are necessary to confirm these preliminary results.
Resumo:
Bioelectronic interfaces have significantly advanced in recent years, offering potential treatments for vision impairments, spinal cord injuries, and neurodegenerative diseases. However, the classical neurocentric vision drives the technological development toward neurons. Emerging evidence highlights the critical role of glial cells in the nervous system. Among them, astrocytes significantly influence neuronal networks throughout life and are implicated in several neuropathological states. Although they are incapable to fire action potentials, astrocytes communicate through diverse calcium (Ca2+) signalling pathways, crucial for cognitive functions and brain blood flow regulation. Current bioelectronic devices are primarily designed to interface neurons and are unsuitable for studying astrocytes. Graphene, with its unique electrical, mechanical and biocompatibility properties, has emerged as a promising neural interface material. However, its use as electrode interface to modulate astrocyte functionality remains unexplored. The aim of this PhD work was to exploit Graphene-oxide (GO) and reduced GO (rGO)-coated electrodes to control Ca2+ signalling in astrocytes by electrical stimulation. We discovered that distinct Ca2+dynamics in astrocytes can be evoked, in vitro and in brain slices, depending on the conductive/insulating properties of rGO/GO electrodes. Stimulation by rGO electrodes induces intracellular Ca2+ response with sharp peaks of oscillations (“P-type”), exclusively due to Ca2+ release from intracellular stores. Conversely, astrocytes stimulated by GO electrodes show slower and sustained Ca2+ response (“S-type”), largely mediated by external Ca2+ influx through specific ion channels. Astrocytes respond faster than neurons and activate distinct G-Protein Coupled Receptor intracellular signalling pathways. We propose a resistive/insulating model, hypothesizing that the different conductivity of the substrate influences the electric field at the cell/electrolyte or cell/material interfaces, favouring, respectively, the Ca2+ release from intracellular stores or the extracellular Ca2+ influx. This research provides a simple tool to selectively control distinct Ca2+ signals in brain astrocytes in neuroscience and bioelectronic medicine.
Resumo:
In this thesis, the viability of the Dynamic Mode Decomposition (DMD) as a technique to analyze and model complex dynamic real-world systems is presented. This method derives, directly from data, computationally efficient reduced-order models (ROMs) which can replace too onerous or unavailable high-fidelity physics-based models. Optimizations and extensions to the standard implementation of the methodology are proposed, investigating diverse case studies related to the decoding of complex flow phenomena. The flexibility of this data-driven technique allows its application to high-fidelity fluid dynamics simulations, as well as time series of real systems observations. The resulting ROMs are tested against two tasks: (i) reduction of the storage requirements of high-fidelity simulations or observations; (ii) interpolation and extrapolation of missing data. The capabilities of DMD can also be exploited to alleviate the cost of onerous studies that require many simulations, such as uncertainty quantification analysis, especially when dealing with complex high-dimensional systems. In this context, a novel approach to address parameter variability issues when modeling systems with space and time-variant response is proposed. Specifically, DMD is merged with another model-reduction technique, namely the Polynomial Chaos Expansion, for uncertainty quantification purposes. Useful guidelines for DMD deployment result from the study, together with the demonstration of its potential to ease diagnosis and scenario analysis when complex flow processes are involved.