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.

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.

We report on phase and strain changes in Ti_1-xAl_xN (0 ≤ x ≤ 0.61) coatings on cutting tools during turning recorded in operando by high-energy x-ray diffractometry. Orthogonal cutting of AISI 4140 steel was performed with cutting speeds of 360–370 m/min. Four positions along the tool rake face were investigated as a function of time in cut. Formation of γ-Fe in the chip reveals that the temperature exceeds 727 °C between the tool edge and the middle of the contact area when the feed rate is 0.06 mm/rev. Spinodal decomposition and formation of wurtzite AlN occurs at the positions of the tool with the highest temperature for the x ≥ 0.48 coatings. The strain evolution in the chip reveals that the mechanical stress is largest closest to the tool edge and that it decreases with time in cut for all analyzed positions on the rake face. The strain evolution in the coating varies between coatings and position on the rake face of the tool and is affected by thermal stress as well as the applied mechanical stress. Amongst others, the strain evolution is influenced by defect annihilation and, for the coatings with highest Al-content (x ≥ 0.48), phase changes.

For many applications, gas sensors need to be very sensitive, selective and exhibit a good stability. Moreover, they should also be cheap and small, and allow a fast response time. Usually, sensors are optimized for specific applications with a compromise between the mentioned criteria. Here, we show a method that allows very sensitive, but rather slow, graphene metal oxide hybrid sensors to be used in a much faster and more effective way with a focus on targeting trace level concentrations of some common toxic air pollutants. By exploiting the first-order time-derivative of the measured resistance signal after a concentration step, the response peak is achieved much faster, while also being more robust against sensor exposure and relaxation times, and concomitantly maintaining the very high sensitivities inherent to graphene. We propose to use this method to generate an additional signal to allow using sensors that are normally rather slow in applications where steep concentration changes need to be detected with much faster time constants.

Printed Edition of the Special Issue Published in Crystals.

2D materials offer a unique platform for sensing with extreme sensitivity, since minimal chemical interactions cause noticeable changes in the electronic state. An area where this is particularly interesting is environmental monitoring of gases that are hazardous at trace levels. In this study, SiC is used as a base for epitaxial growth of high quality, uniform graphene, and for templated growth of atomically thin layers of platinum, with potential benefits in terms of the ability to operate at higher temperature and to serve as a more robust template for fiinctionalization compared to graphene. Fiinctionalization with nanoparticles allows tuning the sensitivity to specific molecules without damaging the 2D sensor transducer. With this platform we demonstrate detection of nitrogen dioxide, formaldehyde, and benzene at trace concentrations. This, combined with smart sensor signal evaluation allowing fast response times, could allow real-time monitoring of these toxic pollutants at concentrations of relevance to air quality monitoring.

Two-dimensional materials may constitute key elements in the development of a sensing platform where extremely high sensitivity is required, since even minimal chemical interaction can generate appreciable changes in the electronic state of the material. In this work, we investigate the sensing performance of epitaxial graphene on Si-face 4H-SiC (EG/SiC) for liquid-phase detection of heavy metals (e.g., Pb). The integration of preparatory steps needed for sample conditioning is included in the sensing platform, exploiting fast prototyping using a 3D printer, which allows direct fabrication of a microfluidic chip incorporating all the features required to connect and execute the Lab-on-chip (LOC) functions. It is demonstrated that interaction of Pb²⁺ ions in water-based solutions with the EG enhances its conductivity exhibiting a Langmuir correlation between signal and Pb²⁺ concentration. Several concentrations of Pb²⁺ solutions ranging from 125 nM to 500 µM were analyzed showing good stability and reproducibility over time.