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The Ph.D. thesis presented here was developed in the framework of the European FET-Open Project called “SensApp”. SensApp stands for “Super-sensitive detection of Alzheimer’s disease biomarkers in plasma by an innovative droplet split-and stack approach”. The main goal of the project was the development of a new biosensing device based on an innovative technology, called “droplet split and stack” (DSS), able to dispense and accumulate very tiny droplets of body fluid samples and to detect biomarkers of the Alzheimer’s disease, such as b-amyloid and Tau proteins, with high sensitivity (≤ 30 pg/mL).

Micron size fiber fragments (MFFs), both natural and synthetic, are ubiquitous in our life, especially in textile clothes, being necessary in modern society. In the Earth's aquatic ecosystem, microplastic fibers account for ~91% of microplastic pollution, thus deserving notable attention as one of the most alarming ecological problems. Accurate automatic identification of MFFs discharges in specific upstream locations is highly demanded. Computational microscopy based on Digital Holography (DH) and machine learning has been demonstrated to identify microplastics in respect to microalgae. However, DH is a non-specific optical tool, meaning it cannot distinguish different types of plastic materials. On the other hand, materials-specific assessments are pivotal to establish the environmental impact of different textile products and production processes. Spectroscopic assays can be employed to identify microplastics for their intrinsic specificity, although they are generally low-throughput and require large concentrations to enable effective measurements. Conversely, MFFs are usually finely dispersed within a water sample. Here we rely on a polarization-resolved holographic flow cytometer in a Lab-on-Chip (LoC) platform for analysing MFFs. We demonstrate that two important objectives can be achieved, i.e. adding material specificity through polarization analysis while operating in a microfluidic stream modality. Through a machine learning numerical pipeline, natural fibers (i.e. cotton and wool) can be clearly separated from synthetic microfilaments, namely PA6, PA6.6, PET, PP. Moreover, the proposed system can accurately distinguish between different polymers under investigation, thus fulfilling the specificity goal. We extract and select different features from amplitude, phase and birefringence maps retrieved from the digital holograms. These are shown to typify MFFs without the need for sample pre-treatment or large concentrations. The simplicity of the DH method for identifying MFFs in LoC-based flow cytometers could promote the use of polarization resolved field-portable analysis systems suitable for studying pollution caused by washing processes of synthetic textiles.

The presence of dispersed plastic particulates in the environment is an ecological problem of increasing relevance, posing in addition potential health risks as they enter the food chain. In the last years, increasing efforts are being spent to study and characterize the microplastics found in nature, particularly in aquatic environments. At the same time, there is a need to advance the instruments and techniques necessary to perform fast, quantitative, and reliable analysis of microplastics. In this framework, we present a compact and cost-effective holographic microscope, integrated with a commercial microfluidic chip to visualize and evaluate microplastic samples in flowing conditions. The preliminary results show that different microplastic samples can be clearly imaged in-flow through a Digital Holography microscope. This is the first step for assessing novel strategies for the detection and identification of microplastics.

Pyroelectric Potential Decay vs LiNbO3Crystal Dimensions

The goal of the SensApp FET-Open project is to develop an innovative super-sensor that will be able to detect Alzheimer’s disease (AD) biomarkers (β-amyloid, Tau and pTAU) in peripheral blood. Considering that nowadays an accurate diagnosis of AD requires the highly invasive withdrawal and analysis of cerebrospinal fluid, SensApp will represent a breakthrough in the field of AD diagnosis thanks to the ability to detect the early stage of the disease by a simple blood collection. We call Droplet-Split-and-Stack (DSS) the new technology that will emerge from SensApp. The achievement of SensApp goal is enabled by the interdisciplinary cooperation between different research institutions and one company involved in the key fields of the project, Vrije Universiteit Brussels, VTT Technical Research Centre of Finland, University of Linz, Ginolis Ltd, IRCCS Centre “Bonino Pulejo”, under the coordination of CNR-Institute of Applied Sciences and Intelligent Systems. This communication will illustrate the progress of the activities.

First prototype of the three individual layers for successive full integration

The effective detection of low-concentrated molecules in small volumes represents a significant challenge in many sectors such as biomedicine, safety, and pollution. Here, we show an easy way to dispense liquid droplets from few mu l volume (0.2-0.5 mu l) of a mother drop, used as reservoir, by using a pyro-electrohydro-dynamic jetting (p-jet) dispenser. This system is proposed for multi-purpose applications such as printing viscous fluids and as a biosensor system. The p-jet system is based on the pyroelectric effect of polar dielectric crystals such as lithium niobate (LN). The electric field generated by the pyroelectric effect acts electro-hydrodynamically on the sample of liquid, allowing the deposition of small volumes. The p-jet approach allows to obtain the dispensing of drops of very small volumes (up to tenths of a picoliter) avoiding the use of syringes and nozzles generally used in standard technologies. The reliability of the technique as a biosensor is demonstrated both in the case of oligonucleotides and in a sample of clinical interest, namely gliadin. The results show the possibility of detecting these biomolecules even when they are low abundant, i.e. down to attomolar. The results show a marked improvement in the detection limit (LOD) when compared with the conventional technique (ELISA). Moreover, it has been presented the possibility of using the p-jet as a useful tool in the detection of biomarkers, present in the blood but currently not detectable with conventional techniques and related to neurodegenerative diseases such as Alzheimer.

The effect of rheological properties on pyroelectrodynamic jets

Design and fabrication of the integrated pyroelectric source