Quantification of mineral carbon capture with Raman spectroscopy and machine learning

[Geoengineering News]

https://www.sciencedirect.com/science/article/pii/S2772656826001144#sec0027a

Authors: Ginger W. Brown, Serena Y. Chin, Natalia V. Solomatova, Eric Wynands, Sadegh Shokatian, Edward Grant

01 September 2026

https://doi.org/10.1016/j.ccst.2026.100681

Highlights

•A Raman spectroscopy and machine learning method was developed for high-throughput quantification of CO2 uptake in mineral carbonation systems.

•Conventional Raman spectroscopy enables accurate carbon quantification under low-fluorescence conditions but degrades under higher fluorescence interference.

•Shifted-Excitation Raman Difference Spectroscopy (SERDS) restores chemically informative signals and enables accurate quantification in high-fluorescence environments.

•The method establishes a Raman monitoring strategy that addresses a key bottleneck in scalable, non-destructive carbon mineralization quantification.

Abstract

Carbon mineralization converts CO2 into stable carbonate minerals through reactions with magnesium- and calcium-rich feedstocks derived from geological and industrial sources. Despite its promise for long-term carbon storage, large-scale deployment remains limited by the lack of rapid, non-destructive, and scalable methods for quantifying carbon uptake under realistic conditions. Conventional analytical techniques, including X-ray diffraction, thermogravimetric analysis, and combustion-based measurements, are accurate but slow, destructive, and unsuitable for in situ or high-throughput monitoring. To address this need, the present work implements Raman spectroscopy to quantify mineral carbon capture with multivariate machine learning (ML) regression. We compare conventional single-laser Raman spectroscopy with dual-laser Shifted-Excitation Raman Difference Spectroscopy (SERDS), which suppresses fluorescence to enable Raman measurements in chemically-complex samples. In samples of carbonated brucite (Mg(OH)2), we show that the chemical complexity of brucite carbonation requires the use of multivariate machine learning quantification. Our results show that single-laser, conventional Raman spectroscopy can accurately predict CO2 uptake in low-fluorescence samples, but the performance of conventional Raman spectroscopy degrades significantly under fluorescence interference. SERDS recovered chemically informative spectral features and improved cross-validated prediction performance in the presence of fluorescence. This work establishes a Raman strategy for monitoring mineral carbon capture with applications in both geological and industrial settings.

Source: ScienceDirect