Exhaled breath analysis has become more and more popular as a supplementary tool for medical diagnosis. However, the number of variables that have to be taken into account forces researchers to develop novel algorithms for proper data interpretation. This paper presents a system for analyzing exhaled air with the use of various sensors. Breath simulations with acetone as a diabetes biomarker were performed using the proposed e-nose system. The XGBoost algorithm for diabetes detection based on artificial breath analysis is presented. The results have shown that the designed system based on the XGBoost algorithm is highly selective for acetone, even at low concentrations. Moreover, in comparison with other commonly used algorithms, it was shown that XGBoost exhibits the highest performance and recall.

Copper oxide (CuO) ultra-thin films were obtained using magnetron sputtering technology with glancing angle deposition technique (GLAD) in a reactive mode by sputtering copper target in pure argon. The substrate tilt angle varied from 45 to 85° and 0°, and the sample rotation at a speed of 20 rpm was stabilized by the GLAD manipulator. After deposition, the films were annealed at 400 °C/4 h in air. The CuO ultra-thin film structure, morphology, and optical properties were assessed by X-ray diffraction (XRD), energy-dispersive X-ray spectroscopy (EDX), X-ray reflectivity (XRR), and optical spectroscopy. The thickness of the films was measured post-process using a profilometer. The obtained copper oxide structures were also investigated as gas-sensitive materials after exposure to acetone in the sub-ppm range. After deposition, gas-sensing measurements were performed at 300, 350, and 400 °C and 50% relative humidity (RH) level. We found that the sensitivity of the device is related to the thickness of CuO thin films, whereas the best results are obtained with an 8 nm thick sample.

The p-n heterostructures of CuO-Ga2O3 obtained by magnetron sputtering technology in a fully reactive mode (deposition in pure oxygen) were tested under exposure to low acetone concentrations. After deposition, the films were annealed at previously confirmed conditions (400 °C/4 h/synthetic air) and further investigated by utilization of X-ray diffraction (XRD), X-ray reflectivity (XRR), energy-dispersive X-ray spectroscopy (EDS). The gas-sensing behavior was tested in the air/acetone atmosphere in the range of 0.1–1.25 ppm, as well as at various relative humidity (RH) levels (10–85%). The highest responses were obtained for samples based on the CuO-Ga2O3 (4% at. Ga).

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This paper investigates the effect of multiwalled carbon nanotubes on the mechanical and electrical properties of epoxy resins and epoxy composites. The research concerns multiwalled carbon nanotubes obtained by catalytic chemical vapor deposition, subjected to purification processes and covalent functionalization by depositing functional groups on their surfaces. The study included the analysis of the change in DC resistivity, tensile strength, strain, and Young’s modulus with the addition of carbon nanotubes in the range of 0 to 2.5 wt.%. The effect of agents intended to increase the affinity of the nanomaterial to the polymer on the aforementioned properties was also investigated. The addition of functionalized multiwalled carbon nanotubes allowed us to obtain electrically conductive materials. For all materials, the percolation threshold was obtained with 1% addition of multiwalled carbon nanotubes, and filling the polymer with a higher content of carbon nanotubes increased its conductivity. The use of carbon nanotubes as polymer reinforcement allows higher values of tensile strength and a higher strain percentage to be achieved. In contrast, Young’s modulus values did not increase significantly, and higher nanofiller percentages resulted in a drastic decrease in the values of the abovementioned properties.

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Despite the temporomandibular joint (TMJ) being a well-known anatomical structure its diagnosis may become difficult because physiological sounds accompanying joint movement can falsely indicate pathological symptoms. One example of such a situation is temporomandibular joint hypermobility (TMJH), which still requires comprehensive study. The commonly used official research diagnostic criteria for temporomandibular disorders (RDC/TMD) does not support the recognition of TMJH. Therefore, in this paper the authors propose a novel diagnostic method of TMJH based on the digital time–frequency analysis of sounds generated by TMJ. Forty-seven volunteers were diagnosed using the RDC/TMD questionnaire and auscultated with the Littmann 3200 electronic stethoscope on both sides of the head simultaneously. Recorded TMJ sounds were transferred to the computer via Bluetooth® for numerical analysis. The representation of the signals in the time–frequency domain was computed with the use of the Python Numpy and Matplotlib libraries and short-time Fourier transform. The research reveals characteristic time–frequency features in acoustic signals which can be used to detect TMJH. It is also proved that TMJH is a rare disorder; however, its prevalence at the level of around 4% is still significant.