Here is reported about the design of a Soft Sensor (SS) able to monitor hazardous gases in industrial plants. The SS is designed to estimate the gas concentrations by means of the measurements coming from an array of sensors, avoiding at the same time the humidity and temperature influence on array outputs. The SS has been designed with a data-driven approach, using a set of experimental data acquired in lab. Two different SSs design methodologies are compared, with the aim of obtaining both good performance and a low computational complexity. As a first approach, a Principal Component Analysis (PCA) has been performed to exploit the high correlation among some of the measures coming from the sensor array. A classical Multi-Layer Perceptron neural network is then trained to estimate the relationships between the PCA outputs and the gas concentrations. As a second approach, a Deep Belief Network (DBN), has been considered. The data here reported show good accuracy in the evaluation of several gas concentrations, even in presence of noised measurements, allowing an efficient risk warning. Even if both methods gave similar performance, a lower number of model parameters and a lower noise sensitivity is obtained when using DBNs.

The laser surface texturing (LST) technique has recently been used to enhance adhesion bond strength in various coating applications and to create structures with controlled hydrophobic or superhydrophobic surfaces. The texturing processing parameters can be adjusted to tune the surface’s polarity, thereby controlling the ratio between the polar and dispersed components of the surface free energy and determining its hydrophobic character. The aim of this work is to systematically select appropriate laser and scan head parameters for high-quality surface topography of metal-based materials. A correlation between texturing parameters and wetting properties was made in view of several technological applications, i.e., for the proper growth of conformal layers onto laser-textured metal surfaces. Surface analyses, carried out by scanning electron microscopy and profilometry, reveal the presence of periodic microchannels decorated with laser-induced periodic surface structures (LIPSS) in the direction parallel to the microchannels. The water contact angle varies widely from about 20° to 100°, depending on the treated material (titanium, nickel, etc.). Nowadays, reducing the wettability transition time from hydrophilicity to hydrophobicity, while also changing environmental conditions, remains a challenge. Therefore, the characteristics of environmental dust and its influence on the properties of the picosecond laser-textured surface (e.g., chemical bonding of samples) have been studied while monitoring ambient conditions.

Zinc oxide nanoparticles (ZnO NPs) with varying levels of nitrogen (N) doping were synthesized using a straightforward sol–gel approach. The morphology and microstructure of the N-doped ZnO NPs were examined through techniques such as SEM, XRD, photoluminescence, and Raman spectroscopy. The characterization revealed visible changes in the morphology and microstructure resulting from the incorporation of nitrogen into the ZnO lattice. These N-doped ZnO NPs were used in the fabrication of conductometric gas sensors designed to operate at room temperature (RT) for detecting low concentrations of NO₂ in the air, under LED UV-Vis irradiation (λ = 400 nm). The influence of nitrogen doping on sensor performance was systematically studied. The findings indicate that N-doping effectively enhances ZnO-based sensors’ ability to detect NO₂ at RT, achieving a notable response (S = R/R₀) of approximately 18 when exposed to 5 ppm of NO₂. These improvements in gas-sensing capabilities are attributed to the reduction in particle size and the narrowing of the optical band gap.

This paper presents the design, fabrication, and characterization of a self-calibrating resonant sensor for the non-invasive dielectric characterization of materials. It is based on a ring resonator operating in the sub-gigahertz range and it exploits the mode-splitting phenomenon to perform differential measurements, wherein one resonance is sensible to the dielectric properties of a sample under test, while the orthogonal resonance serves as an internal reference. This strategy effectively mitigates common-mode perturbations, such as temperature drift. The prototype fabrication was carried out by inkjet-printing a conductive ink into a Rogers RO4003C substrate. The resulting prototype exhibits a nominal resonant frequency near 750 MHz. To characterize the sensor, several samples with known dielectric properties were placed on its surface, and the scattering parameters were measured over the 550-950 MHz frequency range. An analytical fitting procedure was employed to extract the central frequency, amplitude, and quality factor of each resonant peak in the magnitude of the forward transmission coefficient, and these parameters were observed to vary as a function of the samples dielectric properties. Additional tests demonstrated the sensor ability to reject common-mode disturbances. The promising results of these investigations are discussed in detail within the paper.

The purpose of this paper is to present a detailed design procedure for a highly efficient sequential load-modulated balanced amplifier (SLMBA) to provide an in-depth analysis of this complex power amplifier (PA) architecture. SLMBA’s basic theory is presented and discussed. An SLMBA with a frequency range from 2.45 GHz to 2.65 GHz was implemented and then measured in order to validate the proposed design methodology. In both saturation and back-off states, the fabricated SLMBA exhibits extremely high efficiency and output power. It delivers a maximum output power of 43~44.4 dBm and a drain efficiency (DE) of 71.6~75% at saturation, a DE of 63.5~66% at 6 dB output back-off (OBO) state, a DE of 61.8~66% at 10 dB OBO state, and a DE of more than 51% at 12 dB OBO state in the targeted frequency band. The achieved results demonstrate the effectiveness of the proposed design procedure.