Revealing the distribution and efficient enrichment of cobalt in a Cu–Au skarn mineralization system
Bibliographic record
Abstract
• Cobalt mainly occurs in pyrite, sphalerite and magnetite in the Tonglushan deposit. • High oxygen fugacity and hydrous mantle-derived magma promote cobalt enrichment. • Fluid mixing and cooling reduce oxygen fugacity and lead to cobalt precipitation. Cobalt (Co) has become one of the most indispensable key metals globally, underpinning numerous industries and driving technological breakthroughs, particularly in the field of new energy electric vehicles. Skarn ore deposits are a significant source of cobalt reserves, and in China, cobalt-bearing skarn deposits account for about 28 % of the country’s total cobalt reserves. The Middle–Lower Yangtze River Metallogenic Belt (MLYRB) in eastern China stands as an important Cu–Au–Fe–Co polymetallic ore belt. However, previous research initiatives have focused primarily on cobalt associated with Fe skarn deposits, leaving the exploration of cobalt occurrence and enrichment in Cu–Au skarn deposits within the MLYRB unexplored. The Tonglushan deposit (86.3 Mt @ 1.66 % Cu, 0.94 g/t Au, 39.4 % Fe and 0.012 % Co) is representative Cu–Au polymetallic skarn deposit in the MLYRB, characterized by medium-scale cobalt mineralization. In this study, we conducted a comprehensive investigation into the distribution and enrichment patterns of cobalt at Tonglushan through detailed petrographic observations, SEM, LA–ICP–MS, TEM, and in situ S isotope analysis. The results show that cobalt primarily exists in pyrite (Pyb1 avg. 3827 ppm; Pyb2 avg. 2067 ppm), sphalerite (avg. 653 ppm), and magnetite (avg. 324 ppm) within the skarn mineralization centre at Tonglushan. Elemental correlation analysis and TEM investigations reveal that Co and Ni predominantly substitute for Fe 2+ in magnetite and pyrite, while Co and Fe primarily replace Zn 2+ in sphalerite through isomorphic substitution. Moreover, in the early alteration stages, the high temperature and high salinity of hydrothermal fluids facilitate the efficient migration of cobalt in the form of CoCl 4 2- . In the subsequent ore-forming stage, fluid mixing and cooling lead to a decrease in oxygen fugacity, which is the main factor responsible for cobalt precipitation. Our finding further highlights that the sulfide-rich magnetite ores in the Cu-Au and Fe skarn mineralization centre may hold significant potential for exploration and exploitation of cobalt resources within the MLYRB in eastern China.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".