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Record W4399072832 · doi:10.5382/econgeo.5067

Tellurium Transport and Enrichment in Volcanogenic Massive Sulfide Deposits: Numerical Simulations of Vent Fluids and Comparison to Modern Sea-Floor Sulfides

2024· article· en· W4399072832 on OpenAlexaff
Nicole Hurtig, Alexander Gysi, Thomas Monecke, Sven Petersen, Mark D. Hannington

Bibliographic record

VenueEconomic Geology · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsSulfideGeologySeabedGeochemistryTelluriumOceanographyMining engineeringMetallurgyMaterials science

Abstract

fetched live from OpenAlex

Abstract Volcanogenic massive sulfide deposits may represent a significant future source of Te, which is a critical element important for the green energy transition. Tellurium is enriched in these settings by up to 10,000 times over its crustal abundance, indicating that fluids in sea-floor hydrothermal systems may transport and precipitate Te. The major element composition of these hydrothermal fluids is controlled by fluid-rock interaction and is well documented based on experimental, modeling, and natural studies; however, controls on Te mobility are still unknown. To better understand Te enrichment in this deposit type, numerical simulations of the mafic-hosted Vienna Woods and the felsic-hosted Fenway sea-floor vents in the Manus basin were performed to predict Te mobility in modern sea-floor hydrothermal vent fluids and Te deposition during sulfide formation. These simulations demonstrate that the mobility of Te in sea-floor hydrothermal systems is primarily controlled by fluid redox and temperature. Tellurium mobility is low in reduced hydrothermal fluids, whereas mobility of this metal is high at oxidized conditions at temperatures above 250°C. Numerical simulations of the reduced vent fluids of the mafic-hosted Vienna Woods site at the back-arc spreading center in the Manus basin yielded Te concentrations as low as 0.2 ppt. In contrast, the more oxidized model fluids of the felsic-hosted Fenway site located on Pual Ridge in the eastern Manus basin contain 50 ppt Te. The models suggest that Te enrichment in these systems reflects rock-buffer control on oxygen fugacity, rather than an enriched source of Te. In fact, the mafic volcanic rocks probably contain more Te than felsic volcanic rocks. The association of elevated Te contents in the felsic-hosted Fenway system likely reflects magmatic volatile input resulting in lower pH and higher Eh of the fluids. More generally, analysis of sulfide samples collected from modern sea-floor vent sites confirms that redox buffering by the host rocks is a first-order control on Te mobility in hydrothermal fluids. The Te content of sulfides from sea-floor hydrothermal vents hosted by basalt-dominated host rocks is generally lower than those of sulfides from vents located in felsic volcanic successions. Literature review suggests that this relationship also holds true for volcanogenic massive sulfides hosted in ancient volcanic successions. Results from reactive transport simulations further suggest that Te deposition during sulfide formation is primarily temperature controlled. Modeling shows that tellurium minerals are coprecipitated with other sulfides at high temperatures (275°–350°C), whereas Te deposition is distinctly lower at intermediate (150°–275°C) and low temperatures (100°–150°C). These predictions agree with geochemical analyses of sea-floor sulfides as Te broadly correlates positively with Cu and Au enrichment in felsic-hosted systems. The findings of this study provide an important baseline for future studies on the behavior of Te in hydrothermal systems and the processes controlling enrichment of this critical mineral in polymetallic sulfide ores.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.221
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.010
GPT teacher head0.218
Teacher spread0.209 · 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.

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

Citations6
Published2024
Admission routes1
Has abstractyes

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