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Record W3038770383 · doi:10.2138/am-2020-7216

An experimental approach to examine fluid-melt interaction and mineralization in rare-metal pegmatites

2020· article· en· W3038770383 on OpenAlexaff
Alysha G. McNeil, Robert L. Linnen, R. L. Flemming, Mostafa Fayek

Bibliographic record

VenueAmerican Mineralogist · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsUniversity of ManitobaWestern University
Fundersnot available
KeywordsColumbiteTantalumPegmatiteNiobiumSolidusSolubilityMineralogyMaterials scienceGeologyMetallurgyChemistryCeramic

Abstract

fetched live from OpenAlex

Abstract Niobium and tantalum, rare metals and high field strength elements (HFSEs) that are essential to modern technologies, are concentrated among others in lithium-cesium-tantalum (LCT) pegmatites and rare metal granites. The most important hosts for Nb-Ta in these types of deposits are the columbite group minerals (columbite-tantalite), but at some ore deposits significant Ta is also contained in wodginite, microlite, and tapiolite. Previous solubility experiments of HFSE minerals have been limited to high temperatures because of the slow diffusivities of HFSEs in granitic melts. An experiment protocol is described herein that allows HFSE mineral solubilities to be determined at lower temperatures, more in line with the estimated solidus temperatures of LCT pegmatites and rare metal granites. This is achieved through the interaction of a melt that is enriched in high field strength elements (e.g., P and Nb or Ta) with a fluid enriched in a fluid-mobile element (FME, e.g., Mn). A starting glass enriched in a slow diffusing HFSE was synthesized, and HFSE mineral saturation is obtained via the diffusion of a FME into the melt via interaction with a fluid. This interaction can occur at much lower temperatures in reasonable experimental durations than for experiments that require diffusion of niobium and tantalum. The solubility product of columbite-(Mn) from the fluid-melt interaction experiment in a highly fluxed granitic melt at 700 °C is the same as those from dissolution and crystallization (reversal) experiments at the same P-T conditions. Thus, both methods produce reliable measurements of mineral solubility, and the differences in the metal concentrations in the quenched melts indicates that the solubility of columbite-(Mn) follows Henry's Law. Results show that columbite-(Mn) saturation can be reached at geologically reasonable concentrations of niobium in melts and manganese in hydrothermal fluids. This experimental protocol also allows the investigation of HFSE mineral crystallization by fluid-melt interactions in rare-metal pegmatites. Magmatic origins for columbite group minerals are well constrained, but hydrothermal Nb-Ta mineralization has also been proposed for pegmatite-hosted deposits such as Tanco, Greenbushes, and granite-hosted deposits such as Cínovec/Zinnwald, Dajishan, and Yichun. This study shows that columbite-(Mn), lithiophilite, and a Ca-Ta oxide mineral (that is likely microlite) crystallized from experiments in fluid-melt systems at temperatures as low as 650 °C at 200 MPa. It is important to note that HFSE minerals that crystallize from fluid-melt interactions texturally occur as euhedral crystals as phenocrysts in glass, i.e., are purely magmatic textures. Therefore, crystallization of HFSE minerals from fluid-melt interactions in rare metal granites and pegmatite deposits may be more widespread than previously recognized. This is significant because the formation of these deposits may require magmatic-hydrothermal interaction to explain the textures present in deposits worldwide, rather than always being the result of a single melt or fluid phase.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.149
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.021
GPT teacher head0.239
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations29
Published2020
Admission routes1
Has abstractyes

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