Shaohao Zou

The Au-Ag alloys show distinct statistical patterns across different types of gold deposits, yet the underlying thermodynamic control remains unknown. The difficulty arises from low accuracy in the mixing thermodynamic models for alloys and limited understanding of newly identified silver and gold species in hydrothermal fluids. To address these issues, we introduce an updated thermodynamic mixing model using a modified double defect method (DDM) and Monte Carlo simulations, and identify key factors influencing Au-Ag alloy composition under the mineralization conditions of sulfur content (0–2 wt%), fO2 (−42 to –32), salinity (0–15 wt% NaCl), pressure (500–5000 bar), and fluid temperatures (200–500 °C). Thermodynamic calculations suggest that elevated fS2, fO2, salinity, and temperature in hydrothermal fluids significantly reduce the Ag content in Au-Ag alloys. Conversely, a rise in pressure and pH results in an opposite effect. The observed double-hump pattern across a wide compositional range (0–80%) of Au-Ag alloys in epithermal deposits is primarily attributed to the much varied sulfidation states of the hydrothermal fluids, in combination with their progressive cooling and decompression under high salinity and intermediate temperatures of 300 to 250 °C. In contrast, the continuous compositional distribution within a relatively narrow range (0–40%) in orogenic deposits is mainly driven by progressive cooling and decompression of hydrothermal fluids with a low sulfidation state, low salinity, and higher temperatures ranging from 450 to 350 °C. Through linking the thermodynamic traits with the mineralization process in varying deposit types, the composition of Au-Ag alloys could serve as a pragmatic indicator for gold exploration.

The physicochemical controls on gold precipitation in orogenic gold deposits remain poorly constrained, with traditional fluid inclusion and isotopic studies often yielding ambiguous results due to overprinting or incomplete records. This study addresses this challenge using chlorite—a sensitive mineral proxy for fluid conditions—as a quantitative sensor in the Jinshan orogenic gold deposit (>200 t Au) of the Jiangnan orogenic belt, South China. Hosted in Neoproterozoic phyllite within NE–NNE-trending ductile–brittle shear zones, Jinshan features auriferous quartz–polymetallic sulfide veins with prominent chlorite alteration. Integrating high-resolution SEM-EPMA analyses of multi-generational chlorite with thermodynamic modeling, we reconstruct the temporal evolution of temperature, oxygen fugacity (fO₂), pH and sulfur fugacity (fS₂) during ore formation. Four paragenetic stages are identified: Stage 1 (ankerite–quartz), Stage 2 (pyrite–arsenopyrite–quartz), Stage 3 (quartz–gold–polymetallic sulfide), and Stage 4 (chlorite–carbonate–quartz). Electron microprobe analysis reveals that the chlorite composition changes from Fe-rich chamosite (Stage 2) to Mg-rich clinochlore (Stage 3) and then to Fe-rich chamosite (Stage 4). Chlorite from Stage 2 (Chl-1) formed metasomatically at low fluid/rock ratios, while Stage 3 and 4 chlorites (Chl-2 and Chl-3) precipitated directly from higher fluid/rock ratio fluids. Chlorite compositions record a critical Stage 2–3 transition involving cooling from ~320 °C to ~260 °C, reduction (log fO₂ from −33.6 to −39.7), and alkalinization, and sulfur fugacity remained stable within a narrow range (log fS₂ = −13.6 to −8.0), followed in Stage 4 by minor reheating to ~280 °C, re-acidification, and a slight rebound in oxygen fugacity. Thermodynamic simulations reveal that the destabilization of Au(HS)₂⁻ complexes, primarily driven by the synergistic effects of cooling, pH increase, and decreasing oxygen fugacity, triggered gold precipitation during the main ore stage. Results demonstrate that abrupt cooling coupled with fluid alkalinization and reduction exerted the dominant control on gold precipitation in Jinshan, resolving long-standing debates on ore-forming mechanisms and highlighting chlorite as a robust quantitative sensor for fluid evolution.

Amphiboles are key rock-forming minerals and play a significant role in the subduction process. Grunerite (Mg,Fe)7Si8O22(OH)2, as an important ferromagnesian amphibole, is known to undergo a phase transition from monoclinic C2/m to P21/m structure at elevated pressures. This study investigates the phase transition of grunerite under high pressure using a diamond-anvil cell combined with in situ Raman spectroscopy, aiming to identify pressure-induced phase transitions and associated structural changes. We have identified mode splits of the OH stretching modes indicative of the C2/m to P21/m phase transition at ∼1.46 GPa and observed another possible phase transition suggested by a discontinuity in the OH modes at ∼17.81 GPa. The splitting of the OH stretching modes resulted from the distinct environments of two OH positions in the P21/m structure, in comparison to the crystallographically identical OH positions in the C2/m structure. We have also identified additional M-O modes in the lower-wavenumber spectrum range. The results provide new insights into the structural stability of grunerite under extreme geological conditions, contributing to a deeper understanding of metamorphic petrology and geodynamics.

Magmatic oxygen fugacity (fO2) is a fundamental property to understanding the long-term evolution of the Earth’s atmosphere and the formation of magmatic-hydrothermal mineral deposits. Classically, the magmatic fO2 is estimated using mineral chemistry, such as Fe-Ti oxides, zircon, and hornblende. These methods, however, are only valid within certain limits and/or require a significant amount of a priori knowledge. In this contribution, a new oxybarometer, constructed by data-driven machine learning algorithms using trace elements in zircon and their corresponding independent fO2 constraints, is provided. Seven different algorithms are initially trained and then validated on a data set that was never utilized in the training processes. Results suggest that the oxybarometer constructed by the extremely randomized trees model has the best performance, with the largest R2 value (0.91 ± 0.01), smallest RMSE (0.45 ± 0.03), and low propagated analytical error (~0.10 log units). Feature importance analysis demonstrates that U, Ti, Th, Ce, and Eu in zircon are the key trace elements that preserve fO2 information. This newly developed oxybarometer has been applied in diverse systems, including arc magmas and mid-ocean ridge basalts, fertile and barren porphyry systems, and global S-type detrital zircon, which provide fO2 constraints that are consistent with other independent methods, suggesting that it has wide applicability. To improve accessibility, the oxybarometer was developed into a software application aimed at enabling more consistent and reliable fO2 determinations in magmatic systems, promoting further research.

Large and easily accessible porphyry Cu deposits have already been identified, exploited, and gradually exhausted. Novel strategies, therefore, are required to identify new, deeply buried deposits. Previous studies have proposed several lithogeochemical and mineralogical approaches for identifying porphyry Cu systems. Most of these methods, however, require significant a priori knowledge of the exploration region and are, generally, of low effectiveness. In this study, machine learning models using Random Forest and Deep Neural Network algorithms are utilized to characterize magma fertility. The two models have first been trained using a large trace‐element data set of magmatic zircon and then validated on unseen data set during the training process. The performance of both models was evaluated using a fivefold cross‐validation technique, which demonstrates that the models provide consistent results and yield good classification accuracy (∼90% on average) with low false positive rates. Feature importance analysis of the models suggests that Eu/Eu*, Eu/Eu*/Y, Ce/Nd, Ce/Ce*, Dy, Hf, and Ti are the important parameters that distinguish fertile and barren zircons. The real‐world applicability of the validated models was evaluated using two well‐characterized porphyry Cu deposits in subduction and postcollisional settings—the Highland Valley porphyry Cu district (south‐central British Columbia, Canada) and the southern Gangdese belt (Tibet, China), respectively. The results demonstrate that our generalized models can discriminate zircon from igneous rocks associated with porphyry Cu deposits from those in nonmineralized systems with high accuracy and independent of geological setting, suggesting that this new approach can be used effectively in greenfield and brownfield exploration.