Textile-based sensors are regarded as an attractive investigation field because they can provide cost-effective, easy to manufacture and disposable devices. In this work we present the activation of multiwalled-carbon nanotubes (MWCNTs) and their deposition on cotton textile for ammonia (NH3) detection. Indeed, COOH terminations of MWCNTs allow for ammonia binding by amide bonds predicating on a resistivity change upon ammonia interaction with the active sites. We found that deposition of MWCNTs caused a homogeneous textile resistance drops (from 1010 Ω to 103 Ω) with respect to the pristine cotton sample. Monitoring of the resistance variation over time after pipetting the analyte suggest an initial ammonia diffusion throughout the porous CNTs film deposited over the textile which determines a resistance increase and a subsequent resistance reduction due to the ammonia high volatility. These results suggest the feasibility of MWCNTs deposited on cotton fabric as sensor for ammonia detection.

Objective and reliable post‑mortem meat inspection is a key factor in ensuring adequate assessment and quality control of meat intended for human consumption. Early identification of issues that may impact public health and animal health and welfare, such as the presence of chemical contaminants in meat, is critical. In this study, we propose a novel method to modernize meat inspection using an electronic nose combined with machine learning (ML), with focus on pig meat as a case study. We explored its potential as a complementary tool to traditional sensory evaluation and analytical methods, aiming to enhance the efficiency and effectiveness of current inspections. We employed a metal‑oxide based gas sensor array of commercially available chemoresistive sensors, functioning as an electronic nose, to differentiate between various categories of 100 pig meat samples collected at a slaughterhouse based on their odor characteristics, including a urine‑like smell and post‑mortem aging. Using the Optimizable Ensemble model, we achieved a sensitivity of 96.5% and specificity of 95.3% in categorizing fresh and urine‑contaminated meat samples. The model demonstrated robust predictive performance with a Kappa value of approximately 0.926, indicating near‑perfect agreement between the predictions and actual classifications. Furthermore, our developed ML model demonstrated the ability to distinguish between nominally fresh pig meat and meat aged for one to two additional days with an accuracy of 93.5% and can also correctly identify meat aged 3–31 days or 17–31 days. Based on the consensus of preliminary decisions from each individual sensor element, the algorithm effectively determined the final status of the meat. This research lays the groundwork for practical applications within the meat inspection process in slaughterhouses and as quality assurance throughout the meat supply chain. As we continue to refine and validate this method, its potential for real‑world implementation becomes increasingly evident.

Gas sensing has been drawing attention over the years in terms of environmental remediation and health diagnostic potential. Volatile organic compounds (VOCs) qualitative/quantitative monitoring is indicative of air quality and people health status. However, VOC analysis is often performed by means of expensive and time-consuming lab-based equipment. Thus, this study proposes the fabrication and characterization steps for the development of a simple, eco-friendly and relatively inexpensive gas impedentiometric sensor. The sensing layer is comprised of gold nanoparticles (AuNPs) obtained from gold tetra chloric acid (HAuCl4) and sodium citrate by means of a green synthesis approach. The analyte under investigation is acetone, one of the principal breath volatiles, also reported by plenty of studies as a potential biomarker of various diseases. Cotton textiles, with specific texture and grammage, were used as substrate. Conductive traces over the samples were patterned either with carbon/8B pencil or with a stencil-applied conductive ink. The sensor working principle is predicated on the correlation between substrate impedance changes and Van der Waals interactions between acetone and citrate-functionalized AuNPs. Charge injection and transport processes originating from acetone adsorption on the sensor reflected electrical properties variation. Impedance magnitude for the pristine sample turned out to be around 1010 Ω, while AuNPs functionalization brought impedance down to 107 Ω range. On the other hand, when acetone was dripped on the sensor, a two orders of magnitude variation (from 107 Ω to 105 Ω) was observed. Hence, the sensor can be considered amenable to acetone detection by means of functionalized textiles.

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.