Politecnio die Bari - Catalogo di prodotti della Ricerca
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On the Use of a Water Potential Probe for Suction and Temperature Measurements in Unsaturated Natural Clayey Soil
No full textThe accurate measurement of soil suction is essential for understanding the behavior of unsaturated soils, particularly in soil–vegetation–atmosphere (SVA) interactions, where both energy and hydraulic gradients due to climatic action exhibit their maximum intensity. This study assesses the performance of the TEROS 21 probe, a capacitance-based water potential sensor, for measuring soil matric suction and temperature in clayey soils of the South Apennines, Italy. Laboratory tests were conducted on soil samples with varying moisture contents, and the results were compared with those obtained using the traditional filter paper (FP) method and high-capacity tensiometers (HCTs). The TEROS 21 (METER Group, Inc., Pullman, WA, USA) sensor demonstrated a reliable performance, especially at suction levels between 300 and 2000 kPa, though there was some dependency on the initial sensor conditions (wet or dry). The temperature data obtained from the TEROS 21 were verified by using a thermocouple, showing the high consistency of the readings. This study showed that the filter paper and sensor measurements aligned at a water content lower than 30% but diverged at higher levels due to method-specific accuracy limitations. The consistent sensor results confirmed the measurement’s reliability. The air-entry value (AEV) of the soil water retention data was identified at around 800 kPa, which is consistent with previous findings
A Bluetooth-Enabled Electrochemical Platform Based on Saccharomyces cerevisiae Yeast Cells for Copper Detection
No full textCopper contamination in the environment poses significant risks to both soil and human health, making the need for reliable monitoring methods crucial. In this study, we report the use of the EmStat Pico module as potentiostat to develop a portable electrochemical biosensor for copper detection, utilizing yeast Saccharomyces cerevisiae cells immobilized on a polydopamine (PDA)-coated screen-printed electrode (SPE). By optimizing the sensor design with a horizontal assembly and the volume reduction in the electrolyte solution, we achieved a 10-fold increase in current density with higher range of copper concentrations (0–300 μM CuSO4) compared to traditional (or previous) vertical dipping setups. Additionally, the use of genetically engineered copper-responsive yeast cells further improved sensor performance, with the recombinant strain showing a 1.7-fold increase in current density over the wild-type strain. The biosensor demonstrated excellent reproducibility (R2 > 0.95) and linearity over a broad range of copper concentrations, making it suitable for precise quantitative analysis. To further enhance portability and usability, a Bluetooth-enabled electrochemical platform was integrated with a web application for real-time data analysis, enabling on-site monitoring and providing a reliable, cost-effective tool for copper detection in real world settings. This system offers a promising solution for addressing the growing need for efficient environmental monitoring, especially in agriculture
Active Short Circuit Tolerant Design of Permanent Magnet Assisted Synchronous Reluctance Machines
No full textThe functional safety standard fulfillment could necessitate the active short circuit manoeuvre regardless the pre-operating condition of the electrical machine used in traction applications. This poses an additional and computationally-challenging requirement to the machine design as the permanent magnet (PM) demagnetization risk needs to be evaluated in the worst condition during the short circuit transient. This manuscript proposes a comprehensive design procedure of a PM assisted synchronous reluctance machine able to evaluate the full performance in the torque-speed plane including the short circuit current transient and the worst PM demagnetization condition in a time-efficient way. The computational efficiency is achieved evaluating the flux-current maps with a non-linear magnetic equivalent circuit carefully balancing the compromise between a faithful representation of the machine flux paths and computational effort. The methodology is adopted to perform a parametric design study varying two independent design variables and the number of poles considering the space and performance requirements of a heavy duty electric vehicle application. The compromise between overload capability and PM demagnetization during the short circuit is investigated defining the rationals of the final machine selection. One machine candidate is refined, manufactured and tested and the experimental results support both design procedure and design insights
Efficiency boost of perovskite solar cell in homojunction configuration through tailored band alignment and p-n doping profile
No full textThe relentless quest for sustainable and eco-friendly energy sources can be addressed through solar cells which convert solar energy to electrical energy. In the family of solar cells, perovskite solar cells (PSCs) have seen an astonishing growth in power conversion efficiency (PCE) to which homojunction perovskite is the newest addition. Although numerous studies have examined PCE enhancement through energy band-offset optimization in the intrinsic PSCs, to date, such a study has never been reported in a homojunction configuration. Considering the enormous potential of homojunction PSCs and the importance of band offset, here we numerically identified the optimized range of conduction and valance band offset (CBO and VBO) in conjunction with donor and acceptor density inside the perovskite layer to boost their photovoltaic efficiency. The effect of the CBO and donor density is found to be superior to the VBO and acceptor density. Unlike intrinsic PSCs, the cliff in the energy band diagram drastically reduces the PCE for the CBO. The optimum CBO and donor density are around 0 to +0.1 eV and 2 × 1017 cm−3 respectively. The PCE is higher and nearly constant for a VBO > 0 eV and acceptor density > 7 × 1017 cm−3. With the optimum values of doping density and band offsets, the maximum PCE is calculated to be 25.43% in the presence of all possible losses and an optimum perovskite thickness of 600 nm with a 30:70% ratio of n and p-doped segments
Impact of the operating conditions on the OH* distribution and its correlation with the heat release rate in hydrogen–air flames
No full textOH* chemiluminescence is widely used as heat release rate (HRR) marker in combustion experiments. Still, its suitability for hydrogen–air flames has not been extensively assessed for a wide range of operative conditions and flame archetypes. In the present work, correlations between OH* and HRR spatial distributions are first investigated in one-dimensional unstretched laminar premixed flames at variable pressure ([1; 20] atm), unburned gas temperature ([300; 900] K), and equivalence ratio ([0.3; 3.0]). At atmospheric pressure and unburned gas temperature, two main differences are observed: a characteristic shift between the OH* and HRR peak positions and, for equivalence ratios close to stoichiometry, the presence of a non-negligible concentration of OH* in the post-flame zone, where the HRR value is zero. When pressure and unburned gas temperature are increased, the peak shift is attenuated, while the OH* concentration in the burned gases is favored. For lean (φ=0.35) and stoichiometric mixtures, the effects of strain and curvature contributions of flame stretch are analyzed in one-dimensional counterflow flames and two-dimensional expanding flames. Higher strain rates slightly affect the peak shift, but sensibly enhance the production of OH* in the burned gas region. Stretch strongly impacts on expanding lean flames, for which the onset of intrinsic instabilities worsens the OH*-HRR correlation, in terms both of distribution shape and intensity. Finally, nonpremixed one-dimensional counterflow diffusion flames and a more complex two-dimensional triple flame are analyzed. In both configurations, a significant reduction of the peak shift is observed when the combustion occurs in diffusion-controlled regimes, sustaining the adequacy of OH* as HRR marker for hydrogen–air diffusion flames under various operating conditions. Novelty and significance statement This work presents a systematic investigation of the OH* distribution and its correlation with the heat release rate in several canonical premixed and nonpremixed laminar hydrogen–air flames under various operating conditions, extending the current literature on the subject. The chemical pathways leading to the peculiar behavior observed for stoichiometric flames are investigated, and the interaction of OH* with intrinsic thermodiffusive instabilities in lean flames is analyzed, showing how the correlation with the heat release rate is worsened. A coherent methodology to quantitatively assess heat release surrogates, which can be extended to other measurable quantities, is provided too. This knowledge is significant as it underlines how the parametric variation of operating conditions can differently affect the correlation between the OH* and the heat release rate distributions, highlighting the limits of OH*chemiluminescence as a combustion diagnostic technique to validate numerical simulations
Understanding the Importance of Feature Groups for Clinical Outcome Predictions with Machine Learning in Post-Stroke Robotic-Assisted Rehabilitation
No full textOutcome predictions in post-stroke rehabilitation are a key element to personalize the treatment to the needs of the patient, finally enhancing effectiveness of the therapy. They can form the basis of Decision Support Systems, helping clinicians to progressively tune the therapy depending on patients' clinical status and progress. Diverse data sources, such as clinical, demographic, kinematic and time-related data in robotic-assisted rehabilitation, can provide different prediction results. Understanding which data source, or combination thereof, contains useful information for outcome predictions can improve the development of machine learning tools, Decision Support Systems, and even clinical setups designed to record these useful data. The presented work investigates different feature groups and machine learning methods, using data recorded within a robotic-assisted rehabilitation treatment including 44 stroke patients. Results highlight the effectiveness of using multi-dimensional feature groups to predict poststroke rehabilitation. While clinical data alone can already achieve a solid basis for predictive modeling, the integration of kinematic and time-related data can significantly improve prediction accuracy of the patient outcome
A Survey Investigating Skills, Job Profiles, and Education in Circular Economy: A Practice Perspective
No full textThe manufacturing sector requires a highly skilled workforce able to integrate sustainable business models in line with technological advancement. So far, it is still not clear how company deal with the enhancement of the set of skills needed to ease the twin green and digital transition. Without an understanding of the current state of practice, it could be tough for educational institutions to provide educational and training courses capable to meet the actual requests of companies approaching this transition. This research, grounded on an online survey, aims to identify the industrial and practical needs associated with the required skills and professional roles, necessary to address the transition towards a Circular Economy (CE) model. The results of this study (complemented by a literature, empirical interviews, and market investigation) will be exploited to develop courses in the twin green and digital transition at Higher Education (HE) and Vocational Education and Training (VET) levels
Design and Experimental Validation of Secure Controller Area Network Messaging with the Advanced Encryption Standard
No full textController Area Network (CAN) lacks native cryptography, yet many embedded control loops impose millisecond-level deadlines. We implement software-only AES-128 on commodity Cortex-M devices and quantify its real-time cost on classic CAN without changing identifiers, frame lengths, or bus occupancy. Using a purpose-built bench with one STM32F4 master and three STM32F1 slaves, protecting a 16-byte payload increases request–response latency from 540 μs to 829 μs, remaining within a 5 ms application budget. The approach preserves the physical layer, sustaining disturbances up to 15.3 Vpp, and fits within 11.2 kB flash and 1.8 kB RAM. These results provide a standards-compliant migration path to confidentiality (and replay freshness) on legacy CAN networks, establishing a compute/latency baseline that supports subsequent adoption of authenticated encryption and production key management
