Geochemical Characteristics of Rare-Metal, Rare-Scattered, and Rare-Earth Elements and Minerals in the Late Permian Coals from the Moxinpo Mine, Chongqing, China
Bibliographic record
Abstract
The rare-metal, rare-scattered (dispersed), and rare-earth elements (TREs) and minerals of the K2 coal from the Moxinpo mine, Chongqing, were analyzed using inductively coupled plasma mass spectrometry and scanning electron microscopy (SEM) with energy-dispersive X-ray spectrometry, respectively. In addition, three-fold geochemical classification of TREs (Li–Be–Rb(Cs)–Sr–Ge–Se–Tl, Zr(Hf)–Nb(Ta)–Ga(In)–Te–Re–Cd, and REY–Sc) was used to determine the vertical distribution. Compared to world bituminous coals, the K2 coal is enriched in Li, Be, Zr, Nb, Hf, Ta, Ga, Se, Cd, In, Te, Re, Sc, and REY elements (except Eu and Tm), especially in Zr, Nb, Ta, Se, Re, and Sc (>5 times). Compared with the upper continental crust, the K2 coal is enriched with REY, and the fractionation of individual light-REY is higher than that of heavy-REY except for few cases. The value of δEu showed a well-pronounced negative anomaly and that of δCe showed a slightly negative anomaly, which indicate that the K2 coal-seam was intermittently affected by seawater. Minerals in the K2 coal are mainly represented by kaolinite, pyrite, and quartz, while rutile, bastnäsite, and xenotime were observed under SEM. The migration, enrichment, and occurrence of TREs in coal are determined by many factors, such as terrigenous rocks, coal-forming microfacies, and paleo vegetation. The correlation analysis showed that rare-metal and rare-earth elements in K2 coal mainly occur in aluminosilicate minerals due to their high correlation coefficients with SiO2 and Al2O3; traces of rare-earth elements mainly occur in bastnäsite and xenotime; and rare-scattered elements (Ge, Se, Cd, In, and Te) are hosted in sulfide minerals (pyrite).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".