Alessandro Sanginario

Solar-driven CO2 reduction technologies face unique challenges when transitioning from laboratory to prototype scale. In the European Union-funded SunCoChem project, efforts focused on designing and validating a reactor for CO2 conversion to syngas with a suitable ratio, with an area up to 2,400 cm2, for subsequent upgrading into a hydroformylation unit. In addition to selectivity, ensuring process durability remains critical for economic viability. Yet practical implementation at the lab scale remains difficult. To address this, we combined operando electrochemical impedance spectroscopy (EIS) with stress testing, enabling early identification of degradation pathways based on productivity-related parameters. Importantly, we overcame the intrinsic limitations of conventional EIS analysis based on equivalent-circuit modelling by using relaxation-time distribution analysis. We present an integrated approach that successfully couples in-operando EIS with a 120 cm2 prototype, providing a diagnostic framework for real-time monitoring and quantification of failure modes. We discuss practical approaches to addressing current technological bottlenecks and give a perspective on future optimization strategies.

In the last decade, research in developing systems for monitoring clinical parameters has grown exponentially, driven by the increasing demand for continuous and remote observation. Monitoring cardiovascular diseases (CVDs) is crucial, as they remain the leading cause of mortality worldwide. Pulse Wave Velocity (PWV) is a key clinical indicator for assessing arterial stiffness, highly correlated with CVDs. Current methods for measuring PWV require acquiring pulse wave signals at the carotid and femoral sites, configurations that are widely validated in the literature. However, accessing these specific anatomical points presents a significant challenge for developing wearable devices for continuous or home-based PWV monitoring. The present study proposes an innovative approach using a regression algorithm to predict the carotid-femoral PWV value from pulse waves acquired at more accessible sites, including the carotid, radial, and tibial, compatible with wearable use. The study was conducted on a cohort of 90 voluntary healthy participants at the Candiolo Cancer Institute FPO-IRCCS. The best-performing carotid-radial models show a mean error (ME) of less than 0.1 m/s, with a standard deviation error (SDE) below 1 m/s. Similarly, the radial-tibial models exhibit a mean error of the same magnitude but with an even smaller standard deviation error, below 0.9 m/s. These results highlight the strong predictive capabilities of the developed models, offering promising prospects for future clinical implementation aimed at non-invasive and potentially continuous vascular health monitoring.

Water is a not-so-renewable resource. Agriculture is impacting for more than 70% of fresh water use worldwide. Considering the increase of population it is fundamental to act in order to reduce water usage. The WAPPFRUIT project aims to design an automatic irrigation system, based on data of water availability in the soil gathered directly in the orchards. Matric potential data are used to determine the exact water demand of the trees, thanks to specific thresholds adapted to the actual soil and crop type. Furthermore, an electronic system based on simple, small, and ultra-low-power devices works together an automatic algorithm to manage the watering events. We tested this approach in three orchards in north-west Italy, comparing our approach to the one used by the farmers. The results show an average water saving of nearly 50% keeping the fruit production comparable to the reference solution. This approach is a clear example of how electronics and technology can really impact agriculture and food production.

Pressure sensing is a crucial technique for various biomedical applications, where it can provide valuable information about the health and function of different organs and systems. This paper reports the development of a novel integrated pressure sensor based on modified rGO, a graphene-derivative material with enhanced piezoresistive properties. The sensor is fabricated on a flexible PCB substrate and conditioned by a smart current-based Wheatstone bridge circuit, which enables high sensitivity, wide detection range, fast response and recovery, and good stability under cyclic loading. The sensor achieves a measured sensitivity of 0.281 kPa ^-1 (at 0.5 kPa load). A mechanical system is also designed to adapt the sensor to different anatomical sites and to improve its elastic recovery. The sensor’s functionality is initially demonstrated through its response to controlled mechanical stimulation, achieving a signal-to-noise ratio (SNR) of 25 dB. Subsequently, in a practical application, physiological signals from the carotid and femoral arteries of volunteers were acquired. The system effectively captured the pulse waveforms with high fidelity and accuracy (23.5 dB SNR) and measured the pulse transit time, an important parameter for estimating the pulse wave velocity and arterial stiffness. The sensor is not limited to this specific application and can be easily extended to other domains where pressure sensing is required. In conclusion, it offers a low-cost, flexible, and user-friendly solution for non-invasive biomedical monitoring and diagnosis.

Food security, producing enough food for every person on the planet, is becoming a significant issue. Increasing world population and climate change are setting new challenges to food production. Water stress can cause severe damage to crops, and detecting and preventing this threat is crucial. Smart agriculture and the use of sensors directly on the field is a promising and rapidly evolving solution. Data collected by a large number of sensors must be analyzed and efficiently interpreted. In this context, machine learning is an effective solution. This article conducts a comparative analysis of several well-established machine learning models, all trained on a dataset enriched with a novel parameter for the assessment of plant health, the stem electrical impedance (modulus and phase). This feature gives promising results since it is a direct parameter of the plant itself. Moreover, the inclusion of the stem impedance parameter significantly boosted the model's performance, notably enhancing the effectiveness, particularly evident in the case of the top-performing model in this study, the random forest algorithm. When incorporating stem electrical impedance, this model achieved an impressive F1 score of 98%, markedly surpassing the 88% obtained in its absence. As a complementary analysis, a permutation feature performance analysis was conducted, highlighting the potential of stem impedance modulus as a promising feature for evaluating plant watering conditions. The removal of impedance modulus from the training model resulted in an average classification performance loss of 25% in terms of F1 score, suggesting how impedance monitoring is a promising approach for plant health management.

This work introduces a novel wireless sensor for clinical Pulse Wave Velocity (PWV) assessment. Two pen-shaped acquisition probes have been designed to incorporate a custom printed circuit board (PCB), a rechargeable battery, and a commercial piezoresistive load cell as the sensing element. By using the Bluetooth Low Energy (BLE) 5.2 communication protocol, each probe transmits collected data to the USB dongle integrated into the receiving station realized for this application. A graphical user interface (GUI) has been designed to provide clinicians with real-time PWV values and feedback regarding the quality of the acquired signal. To assess the correct behavior of all system units, extensive validation of the developed firmware routines for data transfer and handling was conducted. The reliability of the PWV estimation was assessed in a pre-clinical study against the gold-standard device for noninvasive PWV (SphygmoCor), showing a robust linear correlation yielding a value of R ² =0.89. The Bland-Altman plot computed w.r.t the reference system reports a mean difference equal to -0.65 ± 0.41 m/s. Finally, electromagnetic compatibility and clinical usability tests were conducted to determine the device’s pre-compliance, according to the clinical safety rules and guidelines described in the IEC-60601 standard for medical devices.

A step forward in smart agriculture is moving to direct monitoring plants and crops instead of their environment. Understanding plant status is crucial in improving food production and reducing the usage of water and chemicals in agriculture. Here, we propose a “plant-wearable,” low-cost, and low-power method to measure in-vivo green plant stem frequency as the indicator for plant watering stress status. Our method is based on measuring the frequency of a digital signal obtained with a relaxation oscillator where the plant is a part of the feedback loop. The frequency was correlated with the soil water potential, used as a critical indicator of plant water stress, and an 85% correlation was found. In this way, the measuring system matches all the requirements of smart agriculture and Internet of Things (IoT): ultra-low-cost, low-complexity, ultra-low-power, and small sizes, introducing the concept of wearability in plant monitoring. The proposed solution exploits the plant and the soil as a communication channel: the signal carrying the plant watering stress status information is transmitted to a receiving system connected to a different plant. The system's current consumption is lower than 50 $\bm {\mu }$ A during the transmission in the plant and 40 mA for wireless communication. During inactivity periods, the total current consumption is lower than 15 $\bm {\mu }$ A. Another important aspect is that the system has to be energy autonomous. Our proposal is based on energy harvesting solutions from multiple sources: solar cells and plant microbial fuel cells. This way, the system is batteryless, thanks to supercapacitors as a storage element. The system can be deployed in the fields and used to monitor plants directly in their environment.