Top Highlights
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Preprocess raw Raman data: crop to 250–1250 cm⁻¹, align spectra to a shared axis, remove narrow spikes, and subtract the baseline.
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Focus on diagnostic bands: isolate 460–540 cm⁻¹ and normalize each spectrum to highlight FAU and GIS signatures.
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Unmix with NNLS: use 18-hour and 144-hour spectra as FAU and GIS references to estimate each phase’s relative contribution over time.
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Track the transformation: results show FAU declining as GIS develops—demonstrating how processed in-situ Raman data can monitor crystallization and other changing processes.
Clean the signal before modeling
Raman data rarely offer a clean route from instrument to insight. First, this Python workflow loads spectra and timestamps from a NeXus file. It then crops the useful range, aligns spectra on a shared Raman-shift axis, removes narrow spikes and subtracts the slowly changing baseline.
Next, it isolates the 460–540 cm⁻¹ region, where key FAU and GIS features appear, and normalizes each spectrum. These steps make comparisons more reliable. However, spike thresholds and baseline settings need visual checks: aggressive correction can erase real spectral features.
Use NNLS to track phase change
Non-Negative Least Squares, or NNLS, models each measured spectrum as a non-negative mix of reference spectra. Here, spectra from 18 and 144 hours represent FAU and GIS. The model estimates each reference’s contribution over time.
The pattern is clear: FAU’s relative Raman contribution declines as GIS’s rises. That offers a direct view of the FAU-to-GIS transformation during crystallization. Still, these values describe relative Raman contributions, not automatically the phases’ exact mass fractions. Reference quality and preprocessing choices matter.
Simple, useful—and worth validating
The workflow uses familiar Python tools and keeps the model easy to inspect. Researchers can adapt it to other reactions or material changes when they have suitable reference spectra. An open dataset and Jupyter notebook also lower the barrier to testing the approach.
Yet NNLS cannot rescue weak data or poor references. For stronger process decisions, teams should compare Raman-based trends with independent measurements, such as X-ray diffraction. Used with that check, this approach can turn complex spectra into timely, practical evidence about crystallization.
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