(Institute of Metals and Technology, Ljubljana, Slovenia, 2026) Trifunović, Vanja; Milić, Snežana; Avramović, Ljiljana; Dimitrijević, Silvana; Medić, Dragana; Vuković, Nikola; Jelić, Ivana

Due to the high content of zinc in electric arc furnace (EAF) dust, despite its classification as a hazardous solid industrial waste, it can be used as a secondary raw material for its separation. After the detailed characterization of a representative sample of EAF dust, the two-step leaching process of EAF dust was investigated for the recovery of zinc and other components. The leaching process was carried out in two steps: in the pretreatment step (water leaching) and the sulfuric acid leaching step. With defined optimal process parameters the zinc leaching rate is 90.5 %. After the applied two-step leaching process of the EAF dust, the resulting leaching products were characterized, a kinetic analysis was performed, and the zinc leaching mechanism was determined.

(MDPI, 2026) Đorđević, Ivan; Medić, Dragana; Gajić, Nataša; Nujkić, Maja; Nedelkovski, Vladan; Stanković, Sonja; Cvetković, Aleksandar

This study investigates the leaching behavior of the LiNi0.65Co0.25Mn0.10O2 cathode material in a phosphoric acid medium, with metallic copper recycled from spent battery components serving as a reducing agent. The aim was to develop an efficient approach for the recovery of Li, Ni, Co, and Mn while providing a mechanistic understanding. Leaching experiments were performed by varying key parameters, including copper addition, acid concentration (0.2–0.8 mol·L−1), cathode mass (0.2–1.0 g), stirring rate (0–600 rpm), and temperature (35–80 ◦C). Thermodynamic analysis, supported by Pourbaix and species distribution diagrams, was used to interpret metal behavior. The results show that lithium is readily dissolved, whereas the extraction of Ni, Co, and Mn depends on the presence of copper, which enables their reduction and dissolution. Optimal conditions (0.4 mol·L−1 H3PO4, 0.2 g Cu, 600 rpm, 80 ◦C) enabled rapid extraction, exceeding 90% within 30 min, while near-complete extraction (~100%, 99%, 99%, and 97% for Li, Ni, Co, and Mn) was achieved after 60 min. Structural analysis revealed a transformation from the layered structure to spinel-like intermediates, followed by their dissolution and formation of copper phosphate phases. The proposed system represents an efficient approach for the sustainable recycling of NMC cathodes.

(Univerzitet u Beogradu – Tehnički fakultet u Boru, 2026) Kalinović, Jelena

(University of Belgrade – Technical Faculty in Bor, 2026) Papludis, Aleksandra; Alagić, Slađana; Milić, Snežana; Medić, Dragana; Jelena, Nikolić; Stankov Jovanović, Vesna; Stojanović, Gordana

In this work, the capacity of unwashed aerial parts of plant species: common ivy (CI), lilac (CL), and fig (CF), for DhA detection in biomonitoring purposes was investigated, using GC/MS, and the Pearson’s correlation analysis. Additionally, the detected DhA concentrations were compared with the interrelated lawmaking. GC/MS detected many considerable DhA quantities and documented different capacity of the investigated species for DhA deposition/bioaccumulation, where CI, and CF, showed a better ability for the atmospheric dust retention than CL. However, the highest DhA concentrations were detected in CL parts: 1200.9 μg/kg for stems, and 174.5 μg/kg for leaves, which is even higher than the related USEPA limit values (LVs) for residential soils. Simultaneously, for CL parts, the highest number of locations lacking in DhA was found. Other species showed that almost all selected locations had measurable DhA concentrations, often at the levels that surpassed mentioned USEPA LVs. This means that all locations with such levels may signify possible dermal risks from direct contacts with aerial parts. The results of the Pearson’s analysis confirmed that the industrial center in Bor, with the heating plant and the mining-metallurgical complex, can be one of significant but not the main source of DhA.

(Institut za tehnologiju nuklearnih i drugih mineralnih sirovina, 2026) Trumić, Milan; Milošević, Vladan; Čarapić, Jelena; Nikolić, Vladimir

Modern trends in mining require the integration of technical and technological solutions that simultaneously optimize economic parameters and minimize anthropogenic environmental impacts. Cemented Paste Backfill (CPB) technology represents an effective method for stabilizing underground mined-out areas and ensuring sustainable tailings management. This paper analyzes the key environmental and economic aspects of CPB application from the perspective of rock mass geomechanical stability, protection of groundwater and surface waters, and operational cost structure. In addition to a comparative analysis between CPB systems and conventional surface tailings disposal, global and domestic case studies are considered in assessing the justification of initial capital investments (CAPEX).

(Serbian Chemical Society, 2026) Radojević, Ana; Bogdanović, Anđelija; Kalinović, Tanja; Kalinović, Jelena; Nujkić, Maja; Jordanović, Jelena

A Random Forest (RF) model, rapid field assessment tool in machine learning, uses a collection of decision trees to make predictions. RF is widely used in biomonitoring for high-dimensional, non-normally distributed datasets, to reduce overfitting, and to identify key predictors of environmental stress. The Bor area is well known for pollution resulting from miningmetallurgical production of copper. Sampling of plant and soil material was conducted before the modernization of the smelter and reflects a high, long-term environmental load. The dataset included total concentrations of potentially toxic elements (As, Cr, Cu, Mn, Ni, Pb, Zn) in soil, roots, and washed leaves/needles (µg/g dw) across three plant species (oak, hazelnut, spruce) and 12 sampling sites, along with soil properties (pHKCl, pHH2O, %SOM), distance of the sites from the smelter (m), and directions. In the dataset, 20% comprised random testing data, and the remainder was used for training. Concentrations in leaves/needles were used as response variables, while other parameters served as predictors. The model was built with 200 trees and evaluated using Leave-One-Out Cross-Validation (LOO-CV), while performance was assessed using root mean square error (RMSE), and coefficient of determination (R²). Data were standardized using StandardScaler, with scaling parameters calculated on the training set within each cross-validation fold to avoid data leakage. RF models were built using scikit-learn library in Python 3.12.3. Predictive performance was categorized as weak (R²<0.25), moderate (0.25–0.50), good (0.50–0.75), and high (>0.75). Overall, the model showed weak to moderate predictive power (R2<0.3), resulting from the small sample size of 12 sites per plant. Spruce, among analysed species, is suggested as the most reliable bioindicator, particularly for Cu and Pb (~0.32–0.34). The concentrations of As in needles were too low to observe the 12-point pattern. At the urban-industrial site, ~1.5 km in the SW downwind from the smelter, up to 96 times higher concentration of Cu and 62 times higher concentration of As were observed compared to the background site. Direction and distance of the sites contributed approximately 25–35% of predictive power, while soil parameters account for the remainder, suggesting that bioavailability plays a role, but direct spatial proximity to the smelter remains the dominant factor.

(University of Belgrade - Technical Faculty in Bor, 2026) Nedeljković, Milan; Mladenović, Srba; Ćosović, Vladan; Marković, Ivana; Petrović, Jasmina; Stamenković, Uroš

The increasing demand for environmentally friendly electronic materials has encouraged the development of lead-free solder alloys and sustainable production technologies. Despite numerous alternatives, the eutectic Sn-0.7Cu alloy has been recognized as a promising candidate for lead-free solder applications due to its low cost, acceptable mechanical properties, and compliance with RoHS and WEEE regulations. This study investigates the effect of graphene nanoparticles, in the form of graphene nanosheets (GNS), on the microstructural and mechanical properties of Sn-0.7Cu matrix composites produced via powder metallurgy. GNS was incorporated into the matrix in small weight fractions (0, 0.02, 0.04, 0.06, 0.08, and 0.10 wt.%) to promote microstructural refinement and enhance the mechanical properties of the composites. Optical microscopy was used to characterize the microstructure of the sintered samples, while microhardness was measured using the Vickers method. The results indicate that increasing the graphene content significantly refines the grains, reducing the average grain size by approximately 54 % and increasing the microhardness by about 16 % compared to the base alloy.