Raman spectroscopy for determination of compositions in liquid–liquid dispersions
Bibliographic record
Abstract
Raman spectroscopy is widely applied for monitoring compositions of chemicals in liquid systems. However, its applications to liquid-liquid dispersions, especially regarding the full composition range, remain limited. Feasibility is in question due to the inherent heterogeneity and the resulting light scattering effects of dispersions. To address this problem, we analyze a uniformly mixed binary liquid mixture of 2-methyltetrahydrofuran and water in both homogeneous phases and their disperse state. We identify effects of heterogeneity on Raman spectra and minimize their impact on quantification through pretreatment. Three alternative quantification methods are compared: peak integration, indirect hard modeling, and partial least-squares regression. For indirect hard modeling, impact of model flexibility on the model fit of the standard two-component model is discussed. Motivated by molecular association observed during spectra analysis, an alternative model with a third component for hydrates of 2-methyltetrahydrofuran is developed. Our results indicate that the accuracy of the models is similar for the aqueous phase and disperse state. Best predictions for these two regions are achieved by indirect hard modeling with three components, which additionally gives reliable predictions of compositions in the organic phase. These insights enable further research on the application of Raman spectroscopy in liquid-liquid dispersions.
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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 | 0.001 |
| 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) | 0.000 | 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".