Development of an innovative superconducting magnetic energy storage system

The present work is focused on the demonstration of an innovative approach to a superconducting magnetic energy storage system by means of next generation superconducting wires. The device is thought to be integrated in a more complex biomass plant for green energy production which includes an anaerobic digester and a cogenerator for biogas and electrical energy production. Presented technology allows the storage of the green energy produced with a very high efficiency and with a better power quality respect to traditional counterparts

Anti-Reflective Zeolite Coating for Implantable Bioelectronic Devices

Since sunlight is one of the most easily available and clean energy supplies, solar cell development and the improvement of its conversion efficiency represent a highly interesting topic. Superficial light reflection is one of the limiting factors of the photovoltaic cells (PV) efficiency. To this end, interfacial layer with anti-reflective properties reduces this phenomenon, improving the energy potentially available for transduction. Nanoporous materials, because of the correlation between the refractive index and the porosity, allow low reflection, improving light transmission through the coating. In this work, anti-reflective coatings (ARCs) deposited on commercial PV cells, which were fabricated using two different Linde Type A (LTA) zeolites (type 3A and 4A), have been investigated. The proposed technique allows an easier deposition of a zeolite-based mixture, avoiding the use of chemicals and elevated temperature calcination processes. Results using radiation in the range 470–610 nm evidenced substantial enhancement of the fill factor, with maximum achieved values of over 40%. At 590 and 610 nm, which are the most interesting bands for implantable devices, FF is improved, with a maximum of 22% and 10%, respectively. ARCs differences are mostly related to the morphology of the zeolite powder used, which resulted in thicker and rougher coatings using zeolite 3A. The proposed approach allows a simple and reliable deposition technique, which can be of interest for implantable medical devices

Development of Non-Invasive Ventilator for Homecare and Patient Monitoring System

Recently, the incidence of, and interest in, respiratory diseases has been amplified by severe acute respiratory syndrome coronavirus (SARS-CoV-2) and other respiratory diseases with a high prevalence. Most of these diseases require mechanical ventilation for homecare and clinical therapy. Herein, we propose a portable and non-invasive mechanical fan (NIV) for home and clinical applications. The NIV’s core is a turbine for airflow generation, which can provide and monitor a positive two-level pressure of up to approximately 500 lpm at 50 cmH2O according to the inspiration/expiration phase. After calibration, the proposed NIV can precisely set the airflow with a pressure between 4 cmH2O and 20 cmH2O, providing a versatile device that can be used for continuous positive airway pressure (CPAP) or bilevel positive airway pressure (BiPAP). The airflow is generated by a turbine monitored using a mass flow sensor. The whole NIV is monitored with a 16 MHz clock microcontroller. An analog-to-digital converter is used as the input for analog signals, while a digital-to-analog converter is used to drive the turbine. I2C protocol signals are used to manage the display. Moreover, a Wi-Fi system is interfaced for the transmission/reception of clinical and technical information via a smartphone, achieving a remote-controlled NIV

FT-IR saliva analysis for the diagnosis of psoriasis. a pilot study

Psoriasis is a chronic, autoimmune disease with multiple interplaying risk factors. Saliva has gained growing interest as an excellent biological fluid exhibiting a strong diagnostic potential in dermopathies. Saliva profiling through Fourier Transform Infrared Spectroscopy in attenuated Total Reflection (FT-IR ATR) was investigated for the diagnosis of psoriasis. Particularly, multivariate analysis was carried out after a suitable pre-processing, applying unsupervised principal component analysis (PCA) for feature extraction in the Amide I/II, Thiocyanate and within Thiocyanate and bio fingerprint bands. Further, linear discriminant analysis (LDA) and support vector machine (SVM) were trained to establish discrimination models between psoriatic subjects and healthy controls. PCA-LDA evidenced a classification performance in the bio fingerprint region (2150–900 cm− 1 ) of 93.75% accuracy, and a sensitivity and specificity of 87.5% if compared to SVM (87.5% accuracy, with a sensitivity and specificity of 75%). Saliva profiling and multivariate analysis provide a powerful approach in diagnosis and follow-up of inflammatory dermatopathies. FT-IR saliva profiling, signal processing and machine learning algorithms evidenced the possibility of automatic classification of psoriatic patients, with a potentially interesting insight in mass screening and preliminary diagnosi

A Recursive Algorithm for Indoor Positioning Using Pulse-Echo Ultrasonic Signals

Low frequency ultrasounds in air are widely used for real-time applications in short-range communication systems and environmental monitoring, in both structured and unstructured environments. One of the parameters widely evaluated in pulse-echo ultrasonic measurements is the time of flight (TOF), which can be evaluated with an increased accuracy and complexity by using different techniques. Hereafter, a nonstandard cross-correlation method is investigated for TOF estimations. The procedure, based on the use of template signals, was implemented to improve the accuracy of recursive TOF evaluations. Tests have been carried out through a couple of 60 kHz custom-designed polyvinylidene fluoride (PVDF) hemicylindrical ultrasonic transducers. The experimental results were then compared with the standard threshold and cross-correlation techniques for method validation and characterization. An average improvement of 30% and 19%, in terms of standard error (SE), was observed. Moreover, the experimental results evidenced an enhancement in repeatability of about 10% in the use of a recursive positioning system

Deep Submicron EGFET Based on Transistor Association Technique for Chemical Sensing

Extended-gate field-effect transistor (EGFET) is an electronic interface originally developed as a substitute for an ion-sensitive field-effect transistor (ISFET). Although the literature shows that commercial off-the-shelf components are widely used for biosensor fabrication, studies on electronic interfaces are still scarce (e.g., noise processes, scaling). Therefore, the incorporation of a custom EGFET can lead to biosensors with optimized performance. In this paper, the design and characterization of a transistor association (TA)-based EGFET was investigated. Prototypes were manufactured using a 130 nm standard complementary metal-oxide semiconductor (CMOS) process and compared with devices presented in recent literature. A DC equivalence with the counterpart involving a single equivalent transistor was observed. Experimental results showed a power consumption of 24.99 mW at 1.2 V supply voltage with a minimum die area of 0.685 × 1.2 mm2. The higher aspect ratio devices required a proportionally increased die area and power consumption. Conversely, the input-referred noise showed an opposite trend with a minimum of 176.4 nVrms over the 0.1 to 10 Hz frequency band for a higher aspect ratio. EGFET as a pH sensor presented further validation of the design with an average voltage sensitivity of 50.3 mV/pH, a maximum current sensitivity of 15.71 mA1/2/pH, a linearity higher than 99.9%, and the possibility of operating at a lower noise level with a compact design and a low complexity

Laboratory Parameters of Hemostasis, Adhesion Molecules, and Inflammation in Type 2 Diabetes Mellitus: Correlation with Glycemic Control

Background: Type 2 diabetes mellitus (T2DM) is characterized by a prothrombotic state, predisposing to vascular complications. Some related markers, linking thrombophilia to hemostasis and inflammation, however, have been poorly explored in relation to patients’ glycemia. We therefore investigated the association of laboratory hemostatic parameters, circulating adhesion molecules (ADMs), white blood cell (WBC) count, and neutrophil/lymphocyte ratio (NLR) with T2DM and glycemic control. Research design: In this study, 82 subjects, grouped into T2DM patients (n = 41) and healthy individuals (n = 41) were enrolled. To evaluate glycemic control, the T2DM cohort was expanded to 133 patients and sub-classified according to glycated hemoglobin (HbA1c) <7% and ≥ 7% (n = 58 and n = 75, respectively). We assessed glycemia, HbA1c, prothrombin time (PT), activated partial thromboplastin time (aPTT), fibrinogen, plasminogen activator inhibitor-1 (PAI-1), platelet and leukocyte parameters, vascular cell adhesion molecule 1 (VCAM-1), intercellular adhesion molecule 1 (ICAM-1), and selectins (E-, P-, L-). Results: PT % activity, PAI-1, VCAM-1, WBC, and neutrophil counts were significantly higher in T2DM patients than in healthy subjects. Poor glycemic control (HbA1c ≥ 7%) was correlated with increased PT activity (p = 0.015), and higher levels of E-selectin (p = 0.009), P-selectin (p = 0.012), and NLR (p = 0.019). Conclusions: Both T2DM and poor glycemic control affect some parameters of hemostasis, inflammation, and adhesion molecules. Further studies are needed to establish their clinical utility as adjuvant markers for cardio-vascular risk in T2DM patients