In this paper, multi-hollow surface dielectric barrier discharge generated by a perforated ceramic substrate in a configuration with the air-exposed electrode was investigated. The electrical characteristics (discharge power, peak, and average amplitude of current pulses) and optical characteristics (emission intensity) of the discharge were evaluated under various conditions of applied voltage (peak voltage 3–6 kV, frequency 200–2000 Hz), air flow rate (0.5–2.4 L/min), and air relative humidity (0%–80%). Temperature of ceramic substrate was also monitored. Statistical analysis of current pulses was also performed, and histograms of amplitudes of current pulses were calculated. The results showed that discharge characteristics strictly depend on given working conditions. The analysis of current pulses showed opposite trends in average overall number of positive and negative pulses with an increase of discharge power: number of positive current pulses gradually increased, while number of negative current pulses slightly decreased. The highest peak currents were found at 4 kV (1.8 W). With further increase of peak voltage, peak current decreased and beginning of detection of current pulses upon a rising (declining) slope of applied voltage was slightly shifted toward earlier times. At the highest applied peak voltage, pulses appeared even before polarity of applied voltage reversed. Therefore, we suppose that a residual charge accumulated on dielectric surface plays a crucial role in characteristics of the current pulses. Significant influence on current pulses and discharge emission intensity was also found with a change of air relative humidity, while the effect of air flow rate was found weaker.

The ink can be a significant piece of evidence in the evaluation of written documents. In this work we present a new way of detection and classification of inks from ballpoint pens of different producers, using the ion mobility spectrometry (IMS) technique and artificial intelligence methods. IMS enables fast, sensitive, on-site collection of spectrometric data, which can be immediately analyzed using machine learning methods. In this study, an Advanced IMS (AIMS) instrument was employed to gather ink data, and several machine learning classifiers (including Extra Trees, Gradient Boosting, Random Forest, and Support Vector Classifier) were applied, some of which were integrated into an ensemble voting classifier for enhanced performance. The method demonstrated a high accuracy of 95,23% in classifying inks and provided promising results in estimating the age of written text without compromising the integrity of the sample. These findings highlight the potential applications of the proposed method in forensic investigations, offering a portable, efficient, and non-destructive solution for ink analysis and ageing.

A highly efficient and sensitive ion mobility spectrometry (IMS) system with laser desorption sampling was applied for rapid explosive detection using different surface materials. This portable IMS detector, powered by a battery, offers mobility and is suitable for use in the field or combat zones. The laser desorption (LD) sampling of common explosives (Trinitrotoluene—TNT; Dinitrotoluenes—DNTs; Hexogene—RDX; pentaerythritol tetranitrate—PETN; plastic explosives—Compound 4 (C-4) and Semtex) on a wide range of common surface materials, such as metal, ceramic, plastic, glass, drywall, paper, wood, and textiles, was studied. Successful detection was achieved on nearly all surfaces except flammable materials (paper, wood, and textiles). The limit of detection (LOD) was determined for each explosive and specific surface, demonstrating an impressive LOD of 7 ng/mm² for TNT. RDX, C-4, PETN, and Semtex achieved LOD values of 15 ng/mm², while DNTs showed an LOD of approximately 50 ng/mm².