Testing the feasibility of soil phytoremediation requires the development of models applicable on a large scale. Phytoremediation mechanisms include advanced rhizosphere biodegradation, phytoaccumulation, phytodegradation, and phytostabilization. The aim of this study was to evaluate the phytoremediation potential of the Sinapis alba. Identification of the factors influencing the extraction process of metals from contaminated soils in a laboratory system suitable for evaluating the phytoavailability of these metals in three solutions (M1-CaCl₂, M2-DTPA, and M3-EDTA) included the following: distribution of metals in solution (Kd), soil properties and mobile fractions (SOC, CEC, pH), response surface methodology (RSM), and principal component analysis (PCA). The evaluation of the phytoremediation potential of the Sinapis alba plant was assessed using bioaccumulation coefficients (BACs). The accumulation of heavy metals in plants corresponds to the concentrations and soluble fractions of metals in the soil. Understanding the extractable metal fractions and the availability of metals in the soil is important for soil management. Extractable soluble fractions may be more advantageous in total metal content as a predictor of bioconcentrations of metals in plants. In this study, the amount of metal available in the most suitable extractors was used to predict the absorption of metals in the Sinapis alba plant. Multiple regression prediction models have been developed for estimating the amounts of As and Cd in plant organs. The performance of the predictive models generated based on the experimental data was evaluated by the adjusted coefficient of determination (aR2), model efficiency (RMSE), Durbin–Watson (DW) test, and Shapiro–Wilk (SW) test. The accumulation of the analyzed metals followed the pattern Root > Pods > Leaves > Seeds, stems > Flowers for As and Leaves > Root > Stem > Pods > Seeds > Flowers for Cd in soil contaminated with different metal concentrations. The obtained results showed a phytoremediation potential of the Sinapis alba plant.

Veterinary antibiotics have become a major concern due to potential environmental effects. This study presents an investigation of the exposure and environmental effects of the veterinary medicinal product in powder form, administered in the drinking water of piglets, chickens and turkeys, containing 250 mg/g penicillin G (benzylpenicillin potassium), performed according to the European Medicines Agency (EMEA) guideline and the results obtained by an analytical method based on online solid-phase extraction, ultra-high performance liquid chromatography coupled to a triple quadrupole mass spectrometer (SPE-online-UHPLC-MS/MS). The study presents the determination of the environmental risk and through an original, interactive, fast software method, created on the basis of a proprietary calculation algorithm that goes through all the prescriptions and recommendations of the EMEA guide. The results demonstrated that the concentration value for penicillin G determined in surface water by SPE-online-UHPLC-MS/MS is much lower than that predicted by calculation (predictable concentration in surface water, PECsurface water = 37.66 µg/L and the concentration SPE-online-UHPLC-MS/MS = 0.032 µg/L). Both results lead to a sub-unit risk quotient (R) indicating that the treatment carried out with the considered veterinary product does not present any risk to the environment.

Polymers are frequently used as contact materials in industrial applications that exploit the triboelectric effect. The aim of this paper is to investigate, with the help of 3D printing manufacturing technologies, how tribocharging of polymers is affected by their surface morphology. The experiments were performed using a linear tribometer to create the electric charge through friction between two samples. To highlight the correlation of surface texture with the level of triboelectric charge, the Polylactic acid (PLA) samples were manufactured with three different surface geometries, referred to as “lines”, “zigzag” and “waves”. The first series of experiments consisted in charging the surface of the PLA samples with the tribocharging bench for 20 and 40 cycles, with an Aluminum bar. The cartography of the electric potential at the surface was then performed, using a 2D mapping system to entrain the capacitive probe of an electrostatic voltmeter. In the second series of experiments, after tribocharging, the PLA samples were placed under the probe of the electrostatic voltmeter for 10 min to determine the surface potential decay (SPD) curve. The range of surface potential values was smaller for the “lines”-textured samples than for the other two, pointing out that the geometry of the texture significantly influences the outcome of the triboelectric charging. When comparing the SPD curves of the three textures for 20 cycles, it can be noted that ones for PLA with “lines” and “zigzags” are almost the same, but different from those of “waves”-type samples.

Machine components are subject to a wide range of damage and failure processes, and their correct identification is essential for reliable operation, effective maintenance, and accurate diagnosis. However, a persistent gap exists between morphology-based classification systems, used in international standards, and the mechanism-based interpretations developed in tribology and mechanics. This review analyzes the evolution and current practice of damage classification for machine components, with emphasis on rolling bearings as a representative case. The study is based on a structured analysis of international standards (e.g., ISO 15243), complemented by tribological literature and manufacturers’ documentation. The review focuses on how several damage modes such as spalling, pitting, and fretting are defined, interpreted, and applied in practice. The results highlight systematic ambiguities arising from the separation between visual descriptors and underlying failure mechanisms, particularly in the case of fatigue-related surface damage. Through selected case studies, the review demonstrates how reliance on morphology alone may obscure causal interpretation and lead to inconsistent diagnosis. The study further discusses emerging trends, including digital damage atlases and artificial-intelligence-based diagnostic tools, emphasizing how inconsistencies in standardized terminology may affect their reliability. It is concluded that morphology-based standards should be regarded as complementary diagnostic tools rather than substitutes for mechanical analysis. A closer alignment between standardized terminology and mechanistic understanding is necessary to improve failure diagnosis, support engineering education, and enhance the reliability of machine components.