Rapid 3D chemical‐specific imaging of minerals using stimulated Raman scattering microscopy
Bibliographic record
Abstract
Raman microscopy, which offers chemical‐specific imaging, has important applications in geological sciences. Conventional Raman imaging, however, is challenged by long acquisition times and can be overwhelmed by sample fluorescence. Here, we present the first applications of stimulated Raman scattering (SRS) microscopy, a nonlinear optical Raman technique, to samples of mineralogical interest. Combined with second harmonic generation microscopy, SRS offers a multimodal tool for rapid imaging of mineral samples with chemical specificity, structural sensitivity, and excellent three‐dimensional resolution. Our spectral focusing implementation allows for very rapid scanning of Raman spectral lineshapes, with an adjustable spectral resolution (set here to 25 cm−1) and an overall tuning range of 400–4500 cm−1. In mineralogical applications, this wide‐tuning range offers hyperspectral imaging of both trapped organics, via the CH region (~2900 cm−1), and the lower frequency (<1000 cm−1) ‘fingerprint’ modes important for mineral identification. The simultaneously acquired second harmonic generation image reveals details of the local crystallinity of non‐centrosymmetric minerals such as quartz. As opposed to single‐spectral‐point imaging, we emphasize the importance of tuning over the Raman lineshape while imaging, to unambiguously distinguish the resonant Raman response from nonresonant background signals. Based on the range of samples studied here, we believe that multimodal SRS microscopy will become a valuable imaging tool in the earth sciences, particularly in mineralogy, petroleum, and mineral resources research. ©2017 Her Majesty the Queen in Right of Canada Journal of Raman Spectroscopy ©2017 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".