Driven by cleaner energy demands, environmental regulations, and technological advances, coal science is rapidly evolving, creating the need to understand its transition and transformation within the global energy research landscape. Building upon earlier national- and topic-specific bibliometric studies, this study presents a comprehensive long-term global bibliometric analysis of coal research (1975–2024), based on 272,370 Web of Science records, applying the Cross-Disciplinary Publication Index (CDPI), the Technology–Economic Linkage Model (TELM), VOSviewer, and Excel to assess research growth, structural shifts, and interdisciplinary integration. Results show that coal research is dominated by articles (74%) with publication output peaking at ~19,500 in 2024, reflecting fluctuations in global coal prices due to energy transition market dynamics. CDPI results highlight Energy & Fuels (0.83), Chemical Engineering (0.80), Environmental Sciences (0.77), Materials Science (0.74), and Geosciences (0.66), showing coal’s central role across technology, environment, and geological research domains and revealing a clear shift toward sustainability-oriented and advanced material applications. China leads output (122,130 publications), with strong contributions from the China University of Mining and Technology and the Chinese Academy of Sciences, while the USA, Australia, and Europe maintain strong international collaboration networks. The evolution of coal research can be divided into three major phases: conventional mining, coal preparation, combustion, and coalbed methane commercialization (1975–2004; ~64,000 publications); integrated gasification combined cycle (IGCC) and carbon capture and storage (CCS) technologies (2005–2014; ~58,707 publications); and a recent phase dominated by by-product valorization, carbon capture utilization and storage (CCUS), and digital technologies (AI, IoT, ML) (2015–2024; ~146,174 publications). Contemporary coal research spans three interconnected domains: energy supply (≈36% of global electricity generation and ~15 Gt CO₂ emissions), resource and geoscience applications (including large-scale fly ash utilization and critical element recovery), and environmental and health impacts related to greenhouse gas and pollutant emissions. The findings demonstrate that coal science is transitioning from a conventional fossil fuel-centered discipline toward an integrated, interdisciplinary energy research field, emphasizing emission reduction, resource efficiency, digitalization, and circular economy applications, thereby extending prior bibliometric studies through unprecedented temporal coverage, global scope, and the combined application of CDPI and TELM frameworks, providing critical insights for future energy strategies and policy development.

The Carboniferous coal seams in Northeast Kazakhstan remain insufficiently investigated, with a lack of comprehensive mineralogical and geochemical assessments necessary to understand the geological processes controlling coal quality. This study examines 15 coal samples from the Karagandy Coal Formation (KCF) at the Saradyr and Bogatyr mines using proximate and ultimate analyses, FTIR, XRD, SEM–EDS, ED-XRF, and ICP-OES, providing the first detailed comparison of mineralogical and geochemical characteristics—including depositional signals and inorganic constituent distribution—between these mines within the KCF. The coals exhibit an average ash yield of 24.1% on a dry basis, volatile matter of 21.6% on a dry and ash-free basis, and low moisture content of 1.1% (air-dry), with low sulfur levels of 0.7% in whole coal across both mines. Mineralogical composition is dominated by quartz and clay minerals, with minor pyrite, apatite, chalcopyrite, and rutile. Major oxides in the coal ash average 68.2% SiO₂ and 19.5% Al₂O₃, followed by Fe₂O₃, K₂O, and TiO₂ (3–12.1%). Among the 24 identified trace elements, Sm is the most abundant at 6.3 ppm with slight enrichment (CC = 2.8), Lu remains at normal levels (CC < 1), and most other elements are depleted (CC < 0.5). The Al₂O₃/TiO₂ ratios (3.8–10.8) indicate contributions from intermediate to mafic parent materials. The detrital mineralogy, parting compositions, and elevated ash content indicate significant accommodation space development during or shortly after peat accumulation, likely within a vegetated alluvial plain depression. These findings provide new insights into the depositional environment and coal-forming processes of the KCF and contribute to regional assessments of coal quality and resource potential.

Petrophysical characterization of rocks is essential for the reliable interpretation of gravity and magnetic fields in ore districts where sedimentary, volcanogenic, and intrusive rocks occur in complex structural relationships. This study uses a database comprising 643 bulk-density determinations and 650 magnetic-susceptibility determinations obtained from drill-core and hand-specimen samples from the southeastern part of the Sarysu–Teniz uplift, Central Kazakhstan. In this study, these measurements are presented and interpreted in an aggregated form by the main lithostratigraphic and lithological groups; for each group, the number of determinations, weighted-average values, and diagnostic petrophysical characteristics are reported. The unit-level weighted-average density values range from 2.14 to 2.73 g/cm³, whereas mean magnetic-susceptibility values vary from 0 to 913 × 10⁻⁵ SI. Carbonate-dominated units, including limestones, marls, dolomites, and dolomitized limestones, are non-magnetic to practically non-magnetic, mostly at 0–14 × 10⁻⁵ SI. The highest values are recorded in andesite-basalts and quartz syenite porphyries, reaching 766–913 × 10⁻⁵ SI; granodiorites have an average value of approximately 452 × 10⁻⁵ SI. Density values partly overlap between sedimentary and intrusive rocks, especially within the interval range of 2.48–2.66 g/cm³, whereas magnetic susceptibility provides a more reliable criterion for distinguishing carbonate host rocks from magnetite-bearing magmatic assemblages. The combined density–magnetic susceptibility framework indicates that exploration targeting should prioritize bodies with elevated magnetic susceptibility or sharp magnetic gradients, especially where χ > 450 × 10⁻⁵ SI, provided that these features coincide with faults, lithological contacts, practically non-magnetic carbonate host rocks, and independent geochemical evidence or known mineral occurrences. Consequently, magnetic anomalies are interpreted as indirect structural–lithological indicators rather than as direct evidence of ore bodies.

Mineral prospectivity mapping (MPM) increasingly relies on machine learning classifiers, but model performance may be overstated when spatial dependence, extrapolation beyond the training domain, and poorly calibrated prediction scores are not adequately addressed. This study presents a reproducible workflow for regional Au-Ag prospectivity mapping in Nevada that integrates positive–unlabeled learning with Random Forest models and spatial GroupKFold cross-validation. Predictions were restricted to the Area of Applicability (AOA), defined using the 0.95 quantile of a training-derived feature-space dissimilarity index, resulting in areal coverage of 54.331% for Ag and 73.118% for Au. Discrimination based on pooled out-of-fold (OOF) predictions was strong for both commodities (Ag:AP = 0.745, ROC-AUC = 0.908; Au:AP = 0.777, ROC-AUC = 0.912). Post hoc OOF calibration with isotonic regression reduced Brier scores and improved probability reliability within the supported prediction domain. Area-based validation showed that the highest-ranked 10% of the AOA captured 85.6% of the Ag and 84.2% of the Au pooled-OOF positives, corresponding to targeting efficiencies of 8.6-fold and 8.4-fold relative to random spatial selection. Ensemble dispersion was used to map predictive uncertainty and to identify areas where additional data may be most useful for reducing uncertainty. SHAP importance and partial-dependence diagnostics consistently highlighted proximity to Quaternary faults, elevated slip and dilation tendency, and potential-field edges associated with intrusive or volcanic contacts, while surface heat flow acted mainly as a permissive control. The resulting AOA-masked prospectivity maps, uncertainty layers, and top-k targeting products provide a transparent regional framework for prioritizing follow-up exploration and allocating reconnaissance effort.

Clay minerals in coal play a key role in controlling mineralogical composition, geochemical signatures, and the industrial behavior of coal and its combustion residues. This study investigates the occurrence, provenance, and potential applications of clay minerals in Carboniferous ash-rich coals from the Bogatyr, Lenin, and Saradyr coal mines in northeastern Kazakhstan. A total of 60 coal samples were analyzed using XRD, SEM–EDS/BSE, XRF, and ICP-OES following acid leaching. Based on ash yield, 52 samples were classified as coal (50% ash). Mineralogical assemblages show clear variability among the studied mines. Saradyr samples are strongly quartz-dominated with lower clay proportions, Bogatyr samples exhibit highly heterogeneous quartz–clay–mica assemblages, whereas Lenin samples are relatively more clay-rich and dominated by kaolinite and illite-group minerals. Across all samples, kaolinite is the dominant clay mineral (16.6–46 wt.%), occurring mainly as authigenic pore- and cell-filling aggregates. Minor phases include illite–muscovite (7.1–29.9 wt.%), illite–smectite (up to 7.6 wt.% in Bogatyr), and smectite–montmorillonite (0.4–0.7 wt.%). Clay minerals occur as discrete particles, coatings, and pore fillings, contributing to ash formation; however, their correlation with ash yield is weak (R = 0.03–0.05), reflecting heterogeneous mineral inputs and diagenetic overprinting. All geochemical data are reported on a high-temperature coal ash (HTA) basis (815 °C). Geochemical indices (CIA, CIW, CIX) and Al₂O₃/TiO₂ ratios (1.8–17.4) indicate variable provenance and moderate to high weathering intensity, reflecting mixed mafic to intermediate source rocks. A total of 23 trace elements were identified. Au occurs at trace levels (up to 0.02 ppm), while selected rare earth elements (REE: Ce, Dy, Eu, La, Nd, Sm, Y, Yb) average 0.2–0.3 ppm, indicating negligible economic recovery potential. REEs show a strong positive correlation with clay minerals (r = 0.93), indicating adsorption and minor structural incorporation. In contrast, Au correlates with As, V, Zn, Cu, Ni, and Nb, suggesting sulfide association. HTA is enriched in SiO₂–Al₂O₃ phases dominated by kaolinite and quartz, indicating strong potential for cement, geopolymer, ceramic, and zeolite applications.

The southeastern Sarysu–Teniz Uplift in Central Kazakhstan is partly concealed beneath Cenozoic sediments, which complicates the correlation and age assignment of its Paleozoic magmatic rocks. This study combines new zircon U–Pb ages from four felsic volcanic samples with whole-rock major-element data for volcanic and intrusive rocks to constrain the timing of magmatism and assess their compositional relationships. The principal zircon populations yield concordia ages of 414 ± 3, 415 ± 2, 421 ± 2, and 414 ± 2 Ma, with uncertainties reported at 2σ, indicating Late Silurian–Early Devonian magmatism. Older populations at 440 ± 6 and 438 ± 6 Ma and a single ca. 2.67 Ga zircon document older components, although their provenance remains unresolved. Comparison with previously published intrusive ages supports partial temporal overlap between volcanic and plutonic activity, but does not establish a single magmatic system. Major-element variations are compatible with differentiation involving fractional crystallization, while crustal melting, assimilation, magma mixing, and source heterogeneity remain possible contributors. The available geochemical data do not uniquely identify source lithologies or tectonic setting. These results refine the chronology of the sampled volcanic rocks and provide a basis for revising their regional correlation and relationship with intrusive magmatism.