In situ sulfur isotopes in texturally characterized pyrite, pyrrhotite and chalcopyrite by LA-ICPMS/MS for application in sulfide bearing systems
Bibliographic record
Abstract
The demand for in situ sulfur isotopes analysis has increased in recent years. Yet, no rapid method for collecting high-resolution texturally-constrained data with minimal sample preparation time has been available. In this study, we develop an analytical technique using LA-ICP-MS/MS to rapidly acquire in situ δ 34 S measurements in thin section-hosted sulfides by removing interferences on the 32 S and 34 S mass via mass shifting. This new method required the development of matrix-matched reference material for pyrite, pyrrhotite, and chalcopyrite. New reference materials have been petrographically and chemically characterized, prior to being analysed by both high-precision bulk SF 6 isotope ratio mass spectrometry (IRMS) and in situ secondary ion mass spectrometry (SIMS) S isotope methods. The new reference materials include Iberia pyrite (δ 34 S = 8.9 ± 1.1 ‰; Δ 33 S = −0.02 ± 0.06 ‰), Sullivan pyrite (δ 34 S = 6.0 ± 0.6 ‰; Δ 33 S = −0.05 ± 0.05 ‰), 1869 chalcopyrite (δ 34 S = 9.4 ± 0.2 ‰; Δ 33 S = −0.03 ± 0.04 ‰), Pierre pyrrhotite (δ 34 S = 4.7 ± 0.2 ‰; Δ 33 S = −0.01 ± 0.06 ‰), and Montpellier pyrrhotite (δ 34 S = 3.3 ± 0.2 ‰; Δ 33 S = 0.18 ± 0.07 ‰). Using the developed method, the reproducibility of the primary reference material during each analytical run approximates ±1.5–2.0 ‰. The method was tested on sulfides previously measured by SIMS, with δ 34 S values yielded being within-error of these, confirming the robustness of the technique. We apply the method to case study pyrite-enargite chalcopyrite veins from Cerro de Pasco deposit, Peru, a porphyry related epithermal polymetallic deposit. The results yield heavier sulfur isotope signatures in trace element (TE)-enriched pyrite domains (δ 34 S = 2.2 ± 1.1 ‰) and lighter values (δ 34 S = −1.2 ± 1.7 ‰) in TE-depleted domains. The grain-scale S isotope variation is coupled to microstructure and TE internal zonation, and is interpreted to indicate successive pulses of condensed magmatic vapour. This study opens new analytical opportunities for rapid in situ sulfur isotope analysis in petrographically-characterized thin sections in various sulfide-bearing geological systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 source (direct Gemma or distilled Codex), 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".