Quantification of Solute Composition in H2O-NaCl-CaCl2 Solutions Using Cryogenic 2D Raman Mapping
Bibliographic record
Abstract
Various analytical techniques have been developed to determine the solution composition of fluid inclusions, including destructive, non-destructive, single-inclusion, and bulk-inclusion methods. Cryogenic Raman spectroscopy, as a non-destructive and single-inclusion method, has emerged as a potentially powerful tool of quantitative analysis of fluid inclusion composition. A method of point analysis using cryogenic Raman spectroscopy has been previously proposed to quantitatively estimate the solute composition of H2O-NaCl-CaCl2 solutions, but there are uncertainties related to heterogeneity of frozen fluid inclusions and potential bias in the processing of Raman spectra. A new method of quantitative analysis of solute composition of H2O-NaCl-CaCl2 solutions using Raman mapping technology is proposed in this study, which can overcome the problems encountered in the point analysis. It is shown that the NaCl/(NaCl + CaCl2) molar ratio of the solution, X(NaCl, m), can be related to the area fraction of hydrohalite over hydrohalite plus antarcticite, Fhydrohalite, by the equation X(NaCl, m) = 1.1435 Fhydrohalite − 0.0884, where Fhydrohalite = hydrohalite area/(hydrohalite area + antarcticite area). This equation suggests that the molar fraction of a salt component may be estimated from the fraction of the Raman peak area of the relevant hydrate. This study has established a new way of estimating solute composition of fluid inclusions using cryogenic Raman mapping technique, which may be extended to other solutions.
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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 | 0.000 | 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".