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The acoustic waves of higher orders propagating in a layered structure consisting of a silicon plate coated with piezoelectric ZnO and/or AlN films were used for the development of a sensor with selective sensitivity to liquid viscosity η in the range of 1–1500 cP. In that range, this sensor possessed low sensitivity to liquid conductivity σ and temperature T in the ranges of 0–2 S/m and 0–55 °C, respectively. The amplitude responses insensitive to the temperature instead of the phase were used to provide the necessary selectivity. The sensor was based on a weak piezoactive acoustic wave of higher order. The volume of the probes sufficient for the measurements was about 100 μL. The characteristics of the sensors were optimized by varying the thicknesses of the structure layers, number of layers, wavelength, wave propagation direction, and the order of the acoustic waves. It was shown that in the case of the layered structure, it is possible to obtain practically the same selective sensitivity toward viscosity as for acoustic waves in pure ST, X quartz. The most appropriate waves for this purpose are quasi-longitudinal and Lamb waves of higher order with in-plane polarization. It was found that for various ranges of viscosity η = 1–20 cP, 20–100 cP, and 100–1500 cP, the maximum sensitivity of the appropriate wave is equal to 0.26 dB/cP, 0.087 dB/cP, and 0.013 dB/cP, respectively. The sensitivity of the waves under study toward the electric conductivity of the liquid is much less than the sensitivity to liquid viscosity. These two responses become comparable only for very small η < 2 cP. The waves investigated have shown no temperature responses in contact with air, but in the presence of liquid, they increase depending on liquid properties. The temperature dependence of liquid viscosity is measurable by the same sensors. The results obtained have shown the possibility of designing acoustic liquid viscosity sensors based on multilayered structures. The set of possible acoustic waves in layered structures possesses modified propagation characteristics (various polarization, phase velocities, electromechanical coupling coefficients, and attenuations). It allows choosing an optimal acoustic wave to detect liquid viscosity only.

In this research, beam focusing in lithium niobate plate was studied for fundamental anti-symmetric (A0) and symmetric (S0) Lamb waves, and the shear-horizontal (SH0) wave of zero-order. Using the finite element method, appropriate configuration of the interdigital transducer with arc-like electrodes was modeled accounting for the anisotropy of the slowness curves and dispersion of the modes in the plate. Profiles of the focalized acoustic beams generated by the proposed transducer were theoretically analyzed. Based on the result of the analysis, relevant delay lines were fabricated and transfer functions (insertion loss) of the line were measured for SH0 wave in YX-lithium niobate plate. Using an electron scanning microscope, distribution of the electric fields of the same wave were visualized. The results of this study may be useful for hybrid devices and sensors combining nano and acoustoelectronic principles.

In this study, we propose a low-cost piezoelectric flexible pressure sensor fabricated on Kapton® (Kapton™ Dupont) substrate by using aluminum nitride (AlN) thin film, designed for the monitoring of the respiration rate for a fast detection of respiratory anomalies. The device was characterized in the range of 15–30 breaths per minute (bpm), to simulate moderate difficult breathing, borderline normal breathing, and normal spontaneous breathing. These three breathing typologies were artificially reproduced by setting the expiratory to inspiratory ratios (E:I) at 1:1, 2:1, 3:1. The prototype was able to accurately recognize the breath states with a low response time (~35 ms), excellent linearity (R2 = 0.997) and low hysteresis. The piezoelectric device was also characterized by placing it in an activated carbon filter mask to evaluate the pressure generated by exhaled air through breathing acts. The results indicate suitability also for the monitoring of very weak breath, exhibiting good linearity, accuracy, and reproducibility, in very low breath pressures, ranging from 0.09 to 0.16 kPa. These preliminary results are very promising for the future development of smart wearable devices able to monitor different patients breathing patterns, also related to breathing diseases, providing a suitable real-time diagnosis in a non-invasive and fast way.

The concentration of wild-type tumour suppressor p53wt in cells and blood has a clinical significance for early diagnosis of some types of cancer. We developed a disposable, label-free, field-effect transistor-based immunosensor (BioFET), able to detect p53wt in physiological buffer solutions, over a wide concentration range. Microfabricated, high-purity gold electrodes were used as single-use extended gates (EG), which avoid direct interaction between the transistor gate and the biological solution. Debye screening, which normally hampers target charge effect on the FET gate potential and, consequently, on the registered FET drain-source current, at physiological ionic strength, was overcome by incorporating a biomolecule-permeable polymer layer on the EG electrode surface. Determination of an unknown p53wt concentration was obtained by calibrating the variation of the FET threshold voltage versus the target molecule concentration in buffer solution, with a sensitivity of 1.5 ± 0.2 mV/decade. The BioFET specificity was assessed by control experiments with proteins that may unspecifically bind at the EG surface, while 100pM p53wt concentration was established as limit of detection. This work paves the way for fast and highly sensitive tools for p53wt detection in physiological fluids, which deserve much interest in early cancer diagnosis and prognosis.

The monitoring of some parameters, such as pressure loads, temperature, and glucose level in sweat on the plantar surface, is one of the most promising approaches for evaluating the health state of the diabetic foot and for preventing the onset of inflammatory events later degenerating in ulcerative lesions. This work presents the results of sensors microfabrication, experimental characterization and FEA-based thermal analysis of a 3D foot-insole model, aimed to advance in the development of a fully custom smart multisensory hardware–software monitoring platform for the diabetic foot. In this system, the simultaneous detection of temperature-, pressure- and sweat-based glucose level by means of full custom microfabricated sensors distributed on eight reading points of a smart insole will be possible, and the unit for data acquisition and wireless transmission will be fully integrated into the platform. Finite element analysis simulations, based on an accurate bioheat transfer model of the metabolic response of the foot tissue, demonstrated that subcutaneous inflamed lesions located up to the muscle layer, and ischemic damage located not below the reticular/fat layer, can be successfully detected. The microfabrication processes and preliminary results of functional characterization of flexible piezoelectric pressure sensors and glucose sensors are presented. Full custom pressure sensors generate an electric charge in the range 0–20 pC, proportional to the applied load in the range 0–4 N, with a figure of merit of 4.7 ± 1 GPa. The disposable glucose sensors exhibit a 0–6 mM (0–108 mg/dL) glucose concentration optimized linear response (for sweat-sensing), with a LOD of 3.27 µM (0.058 mg/dL) and a sensitivity of 21 µA/mM cm2 in the PBS solution. The technical prerequisites and experimental sensing performances were assessed, as preliminary step before future integration into a second prototype, based on a full custom smart insole with enhanced sensing functionalities.

In the paper, the results of production of Ag inkjet printed interdigital transducers to the acoustic delay line based on Y-cut X-propagation direction of lithium niobate plate for the frequency range from 1 to 14 MHz are presented. Additionally, morphological, structural, and electro-physical characteristics of the obtained electrodes were investigated. Mathematical modeling of the excitation of acoustic waves by these electrode structures was carried out. Comparison of the theoretical results with experimental ones showed their qualitative and quantitative coincidences. It was shown that conventional inkjet printing can replace the complex photolithographic method for production of interdigital transducers for acoustic delay lines working up to 14 MHz. The resulting electrode structures make it possible to efficiently excite acoustic waves with a high value of electromechanical coupling coefficient in piezoelectric plates.

Microbial fuel cells (MFCs) are a variety of bioelectrocatalytic devices that utilize the metabolism of microorganisms to generate electric energy from organic matter. This study investigates the possibility of using a novel PEDOT:PSS/graphene/Nafion composite in combination with acetic acid bacteria Gluconobacter oxydans to create a pure culture MFC capable of effective municipal wastewater treatment. The developed MFC was shown to maintain its activity for at least three weeks. The level of COD in municipal wastewater treatment was reduced by 32%; the generated power was up to 81 mW/m2 with a Coulomb efficiency of 40%. Combining the MFC with a DC/DC boost converter increased the voltage generated by two series-connected MFCs from 0.55 mV to 3.2 V. A maximum efficiency was achieved on day 8 of MFC operation and was maintained for a week; capacitors of 6800 µF capacity were fully charged in ~7 min. Thus, G. oxydans cells can become an important part of microbial consortia in MFCs used for treatment of wastewaters with reduced pH.

This study deals with diamond films grown via the microwave plasma-enhanced chemical vapor deposition technique (MWPECVD) on molybdenum (Mo) substrates of different roughness. This work is motivated by the necessity of overcoming the poor adhesion of diamond films on smooth Mo substrates, to ensure their effective application as cathodes for aerospace propulsion. The deposition process was monitored in situ using pyrometric interferometry (PI), thus enabling the real-time monitoring of both the rate and the temperature of deposition. The characterization of the obtained diamond films was performed using different techniques, such as Raman spectroscopy, X-ray diffraction (XRD), and scanning electron microscopy (SEM). The poor adhesion of diamond films on Mo substrates was solved by roughening their surface, which promotes residual stress reduction in the diamond films. In this work, the PI technique was also exploited to support the prediction of the adhesion and stability of diamond films before their exposure in air through the monitoring of the deposition temperature. This represents a novel point of our work that has never been discussed in other research papers, as pyrometric interferometry is generally mainly used to assess the rate and the temperature of deposition.

The demand for a “green” approach to the synthesis of nanomaterials is becoming increasingly pressing. In response to this need, we present, for the first time, the use of spark ablation as an environmentally friendly deposition technique to obtain nanoparticles of copper nitride, a material that is gaining increasing attention in the field of photovoltaic advanced technologies. This method involves the ablation of pure copper electrodes in nitrogen atmosphere while a spark current is tuned. The overall result is the co-presence of nitride and oxide nanoparticle agglomerates with different sizes according to the spark current, as confirmed by X-ray diffraction, Raman spectroscopy, X-ray photoelectron spectroscopy and energy-dispersive spectroscopy techniques. Scanning probe microscopy and scanning electron microscopy show an increase in the number and size of nanoparticle agglomerates with an increasing current, while the nanoparticle size is always about sub-10 nm. The findings of this work promote spark ablation as a simple, versatile, cost-effective, environmentally friendly deposition method to obtain nitride-based nanoparticles. Furthermore, it is compatible with many types of materials and substrates, increasing the possible combinations of metals/semiconductors and carrier gas types to obtain completely innovative materials with unique compositions and properties.

Recently, the scientific community has shown a great interest about the Organ-on-Chip (OoC) devices, a special kind of micro-fabricated platforms capable of recapitulating the human physiology implementing the traditional cell culture methods and the concept of in vivo studies. Copper ions represent a cellular micronutrient that must be monitored for its potential hazardous effects. The application of electrochemical analysis for heavy metal ions detection and quantification in commercial cell culture media presents several issues due to electrolyte complexity and interferents. In fact, to the best of our knowledge, there is a lack of applications and OoC devices that implement the Anodic Stripping Voltammetry as an ion dosing technique due to the reasons reported above. In fact, considering just the peak intensity value from the measurement, it turns out to be challenging to quantify ion concentration since other ions or molecules in the media may interfere with the measurement. With the aim to overcome these issues, the present work aims to develop an automated system based on machine learning algorithms and demonstrate the possibility to build a reliable forecasting model for copper ion concentration on three different commercial cell culture media (MEM, DMEM, F12). Effectively, combining electrochemical measurements with a multivariate machine learning algorithm leads to a higher classification accuracy. Two different pH media conditions, i.e., physiological (pH 7.4) and acidic (pH 4), were considered to establish how the electrolyte influences the measurement. The experimental datasets were obtained using square-wave anodic stripping voltammetry (SWASV) and were used to carry out a machine learning trained model. The proposed method led to a significant improvement in Cu2+ concentration detection accuracy (96.6% for the SVM model and 93.1% for the NB model in MEM) as well as being able to monitor the pH solution.