We present a method to monitor methane at atmospheric concentrations with errors inthe order of tens of parts per billion. We use machine learning techniques and periodic calibrationswith reference equipment to quantify methane from the readings of an electronic nose. The resultsobtained demonstrate versatile and robust solution that outputs adequate concentrations in a varietyof different cases studied, including indoor and outdoor environments with emissions arising fromnatural or anthropogenic sources. Our strategy opens the path to a wide-spread use of low-costsensor system networks for greenhouse gas monitoring.

We present an electronic nose that detects ovarian cancer based on gas emissions from blood plasma. There is currently no test available for screening or diagnostic testing of this disease, whichis therefore often detected at aa late stage, resulting in a poor prognosis. Our approach correctly detected 85 out of 87 ovarian cancers, ranging from borderline to stage IV.

Reducing emissions of the key greenhouse gas methane (CH4) is increasingly highlighted as being important to mitigate climate change. Effective emission reductions require cost-effective ways to measure CH4 to detect sources and verify that mitigation efforts work. We present here a novel approach to measure methane at atmospheric concentrations by means of a low-cost electronic nose strategy where the readings of a few sensors are combined, leading to errors down to 33 ppb and coefficients of determination, R-2, up to 0.91 for in situ measurements. Data from methane, temperature, humidity, and atmospheric pressure sensors were used in customized machine learning models to account for environmental cross-effects and quantify methane in the ppm-ppb range both in indoor and outdoor conditions. The electronic nose strategy was confirmed to be versatile with improved accuracy when more reference data were supplied to the quantification model. Our results pave the way toward the use of networks of low-cost sensor systems for the monitoring of greenhouse gases.

Using a single sensor as a virtual electronic nose, we demonstrate the possibility of obtaininggood results with underperforming sensors that, at first glance, would be discarded. For this aim, wecharacterized chemical gas sensors with low repeatability and random drift towards both dangerousand innocuous volatile organic compounds (VOCs) under different levels of relative humidity. Ourresults show classification accuracies higher than 90% when differentiating harmful from harmlessVOCs and coefficients of determination, R2, higher than 80% when determining their concentrationin the parts per billion to parts per million range.

This work presents a smart mask for real-time monitoring of carbon dioxide (CO2) levels asa reference tool for diagnosis, sports training and mental health status. A printed circuit board wasprojected and fabricated to gain data with real-time visualization and storage on a database, enablingremote monitoring as a needed skill for telemedicine purposes. The electronics were inserted in awearable device—shaped like a mask—and 3D-printed with biocompatible materials. The wholedevice was used for analyzing CO2 on a breath volunteer in three kinds of measurement.

The outbreak of the recent Covid-19 pandemic changed many aspects of our daily life, such as the constant wearing of face masks as protection from virus transmission risks. Furthermore, it exposed the healthcare system’s fragilities, showing the urgent need to design a more inclusive model that takes into account possible future emergencies, together with population’s aging and new severe pathologies. In this framework, face masks can be both a physical barrier against viruses and, at the same time, a telemedical diagnostic tool. In this paper, we propose a low-cost, 3D-printed face mask able to protect the wearer from virus transmission, thanks to internal FFP2 filters, and to monitor the air quality (temperature, humidity, CO2) inside the mask. Acquired data are automatically transmitted to a web terminal, thanks to sensors and electronics embedded in the mask. Our preliminary results encourage more efforts in these regards, towards rapid, inexpensive and smart ways to integrate more sensors into the mask’s breathing zone in order to use the patient’s breath as a fingerprint for various diseases.

The COVID-19 pandemic outbreak, declared in March 2020, has led to several behavioral changes in the general population, such as social distancing and mask usage among others. Furthermore, the sanitary emergency has stressed health system weaknesses in terms of disease prevention, diagnosis, and cure. Thus, smart technologies allowing for early and quick detection of diseases are called for. In this framework, the development of point-of-care devices can provide new solutions for sanitary emergencies management. This work focuses on the development of useful tools for early disease diagnosis based on nanomaterials on cotton substrates, to obtain a low-cost and easy-to-use detector of breath volatiles as disease markers. Specifically, we report encouraging experimental results concerning acetone detection through impedance measurements. Such findings can pave the way to the implementation of VOCs (Volatile Organic Compounds) sensors into smart and user friendly diagnostic devices.

Questo lavoro si concentra sulla possibile applicazione di nanoparticelle d'oro su tessuti di cotone flessibili come substrati sensibili all'acetone e all'etanolo mediante misurazioni di impedenza. Nello specifico, nanoparticelle d'oro (NP Au) funzionalizzate con citrato e polivinilpirrolidone (PVP) sono state sintetizzate utilizzando procedure verdi e consolidate e depositate su tessuto di cotone. Una caratterizzazione strutturale e morfologica completa è stata condotta utilizzando la spettroscopia UV-VIS e infrarossa a trasformata di Fourier (FT-IR), la microscopia a forza atomica (AFM) e la microscopia elettronica a scansione (SEM). Una caratterizzazione dielettrica dettagliata del substrato vuoto ha rivelato effetti di polarizzazione interfacciale legati sia alle NP Au che alla loro specifica funzionalizzazione superficiale. Ad esempio, rivestendo interamente il tessuto di cotone (ovvero creando una matrice più isolante), è stato riscontrato che il PVP aumenta la resistenza del campione, ovvero diminuisce l'interconnessione elettrica delle NP Au rispetto al campione funzionalizzato con citrato. Tuttavia, è stato osservato che la funzionalizzazione del citrato ha fornito una distribuzione uniforme delle NP Au, che ha ridotto la loro spaziatura e, quindi, facilitato il trasporto degli elettroni. Per quanto riguarda il rilevamento dei composti organici volatili (COV), le misurazioni della spettroscopia di impedenza elettrochimica (EIS) hanno mostrato che il legame idrogeno e la risultante impedenza di migrazione protonica sono fondamentali per distinguere l'etanolo dall'acetone. 

This work focuses on the possible application of gold nanoparticles on flexible cotton fabric as acetone- and ethanol-sensitive substrates by means of impedance measurements. Specifically, citrate- and polyvinylpyrrolidone (PVP)-functionalized gold nanoparticles (Au NPs) were synthesized using green and well-established procedures and deposited on cotton fabric. A complete structural and morphological characterization was conducted using UV-VIS and Fourier transform infrared (FT-IR) spectroscopy, atomic force microscopy (AFM), and scanning electron microscopy (SEM). A detailed dielectric characterization of the blank substrate revealed interfacial polarization effects related to both Au NPs and their specific surface functionalization. For instance, by entirely coating the cotton fabric (i.e., by creating a more insulating matrix), PVP was found to increase the sample resistance, i.e., to decrease the electrical interconnection of Au NPs with respect to citrate functionalized sample. However, it was observed that citrate functionalization provided a uniform distribution of Au NPs, which reduced their spacing and, therefore, facilitated electron transport. Regarding the detection of volatile organic compounds (VOCs), electrochemical impedance spectroscopy (EIS) measurements showed that hydrogen bonding and the resulting proton migration impedance are instrumental in distinguishing ethanol and acetone. Such findings can pave the way for the development of VOC sensors integrated into personal protective equipment and wearable telemedicine devices. This approach may be crucial for early disease diagnosis based on nanomaterials to attain low-cost/low-end and easy-to-use detectors of breath volatiles as disease markers.

This study aims to identify effective engagement tools and strategies that may strengthen student learning processes with a long-term impact. The context of learning plays an active role in student performance and needs to be carefully considered when designing collaborative learning environments. In the framework of a CDIO course entitled Project Course in Applied Physics (12 ECTS), master’s students in applied physics, electrical engineering, biomedical engineering, material science and nanotechnology work in groups of four to seven people for realizing their own project idea given three broad requirements: (i) use gas sensors, (ii) manage a certain maximum budget to purchase components, and (iii) build a working prototypefor any indoor air quality monitoring application of interest for them and their customer. Groupsare generally multicultural and multidisciplinary. Qualified supervision and skills training activities are adapted to facilitate the students’ progress and guarantee the success of their project work. Based on observations, feedback, and results over a five-year period, this approach appears more engaging and inspiring for both students and teachers compared to more defined projects. Encouraging the students to conceive their own original ideas, involving them in the co-creation of the learning process, and building knowledge, understanding, and skills through a variety of engaging experiences, helps their motivation, interest, active participation, and creativity with a direct impact on the quality of their learning. As an example of successful project work, here we report on two groups of students at Linköping University, Sweden, who have recently designed, developed, and tested an innovative sensor system prototype for smart monitoring of gas and particle emissions from cooking activities. The project course has received 5.0/5.0 as an overall students’ evaluation.