The scope of this study consists of setting up of an integrated cost-effective sampling & laboratory analyses procedure which delineates sampling, sub-sampling and analytical uncertainties in case of fine-grained extractive waste deposits. This procedure is designed to support the decision makers towards fine-grained waste deposits upcycling and land reclamation. This procedure consists of a balanced replicated sampling design (BRSD) coupled with a three split levels ANOVA data processing. The paper provides the readership with the mathematical backgrounds of the three split level ANOVA analysis (3L-ANOVA) and an Excel algorithm for its implementation. Also, the paper presents an example of implementation of the developed methods in the case of a Romanian iron ore tailings (IOT) old pond. The findings of the paper consist of: a) argues, based on OM, SEM-EDS, XRFS and XRD observations, that classical TOS is ineffective for fine-grained waste deposits; b) BRSD in conjunction with 3L-ANOVA analysis is the only approach fit for reliable characterization of the fine-grained stockpiles; c) sampling uncertainty is the critical factor of the uncertainty budget of the analyte concentration; d) Lilliefors approach is adequate for the hypothesis testing where or not the measurand is normal distributed; e) The outcomes of the BRDSD&3L-ANOVA investigations carried on Teliuc tailings, estimated at circa 5.5* 106 m3, consist mainly of mineral quantification at lot level i.e. quartz ∼54% (±7%), hematite ∼15% (±3%), calcite ∼11% (±3%), MgO 3% (±1%), Al2O3 9% (±2%). The concentrations of some CRMs like Ti, V, Ba, Y, W were found at ACE limits and their associated relative expanded uncertainties overpass 50%. Thus, the expanded uncertainties clearly depict the reliability of acquired data for the decision makers regarding waste valorization. f) The IOT into Teliuc can be upcycled as minerals for cement and ceramic industries as well as for geopolymer manufacture. Also, IOT can be downcycles as filler in road construction and mine closure. Finally, the Teliuc yard can be rehabilitated with zero-waste left behind. The data exactness provided by this procedure can be increased to any desirable level through increasing the number of collected items, but the cost of sampling and analyses increases proportionally. In such circumstances, the posted approach can be tailored at the stakeholder request as to safely underpin the decision to turn finegrained by-products into valuable secondary resources, facilitating a greater circularity of the mining industry.

The Bi-Sn, Bi-Sn-Ag, and Bi-Sn-Sb solder alloy systems represent lead-free, environmentally friendly alternatives for reliable electronic assembly. These alloys comply with increasingly strict environmental and health regulations, while offering low melting points suitable for soldering temperature-sensitive components. Microstructural analysis revealed distinct phase segregation in all alloys, with Sb promoting coarse Sn₂Sb₃ intermetallic compounds and Ag inducing fine needle-like Ag₃Sn precipitates. Eutectic refinement and compositional contrast were confirmed by SEM-BSE and EDS mapping. Vickers microhardness measurements revealed increased hardness in Sb- and Ag-modified Bi–Sn alloys, with Ag₃Sn dispersion yielding the highest strengthening effect, indicating enhanced mechanical potential. This study also reports the thermal and electrical conductivities of Bi60Sn40, Bi60Sn35Ag5, and Bi60Sn35Sb5 alloys over the 25–140 °C range. Bi60Sn40 showed an increase in thermal conductivity across the full temperature range from 16.93 to 26.93 W/m·K, while Bi60Sn35Ag5 reached 18.28 W/m·K at 25 °C, and Bi60Sn35Sb5 exhibited 13.90 W/m·K. These findings underline the critical influence of alloying elements on microstructure, phase stability, and thermophysical behavior, supporting their application in low-temperature soldering technologies.

Romania has some huge ferrous slag stockpiles that are secondary resources of minerals. Although a sizable portion of ferrous slags is recycled for use in building roads and other infrastructure, a sizable portion is still dumped. By November 2026, Member States must submit information on the quantity of critical raw materials (CRMs) in their secondary resources, as well as the quantification techniques employed, in accordance with Regulation (EU) 2024/1252. Therefore, the XRFS reliable measurements carried on ferrous slags were addressed to prevent dissipative loss of CRMs in cases where an improper slag recycling route is operated. The main novelty of this paper is the method of ensuring the reliability of the XRFS results based on weighted arithmetic mean and on the maximum likelihood approach. Secondly, the XRFS measurements carried on ferrous slags demonstrate that they contain CRMs like Ba, Sr, Y, etc.; however, below the minimum cut-off grade for CRMs, recovery XRFS cannot detect light CRMs. Our preliminary LIBS measurements on ferrous slags disclosed the presence of Li and Be. The drawbacks of the XRFS technique impose further research to develop an integrated XRFS, LIBS, and XRD procedure for comprehensive and trustworthy CRMs screening in extractive waste piles.

Solid waste treatment and resourceization critically depend on waste characterization. Heavy metals and critical raw materials are found as trace elements in solid waste dumps, and their reliable quantification plays a critical role for decision risk regarding effective waste management. The reliable quantification of trace elements is a very challenging issue. Hence, in this study, a new conservative approach for data analysis in screening for trace elements in waste dumps is presented. We propose a theoretical model for statistical data interpretation to overcome the drawbacks of conventional approaches based on unproven hypotheses, such as binomial, Poisson, or Gaussian distributions of the particles carrying the analyte. Our model addresses concentration values close to the limit of quantification (LOQ) of an analytical method. This model fills the gap of data analysis when a set of analytical results are uniformly distributed. Our approach deals with results reported as being lower than the LOQ. The model was applied on XRFS results from studies carried out on tailings to emphasize the differences among classic, robust, and conservative data analyses. Classical analyses overestimate the concentration values and underestimate the associated uncertainties increasing the decision risk. This study demonstrates that a conservative approach is mandatory when screening for trace elements if the concentration values are uniformly distributed. The proposed model can be applied to any solid waste dump, regardless of the analytical method used for trace element screening.