Machine Learning Assisted Classification of Ca-Organic Matter Complexation Using X-ray Spectroscopies
EMSL Project ID
60173
Abstract
Soils and sediments host large stores of organic carbon, which can be released to the atmosphere upon mineralization. Ca has the potential to play a key role in preventing mineralization ofthis organic carbon by enhancing mineral protection, as it has long been recognized that Ca forms cation bridges that link together negatively charged functional groups from organics and mineral surfaces. Ca bridging is thought to underlie the relationship between exchangeable Ca, increased soil organic matter (OM) content, and decreased OM mineralization. However, there is little experimental insight into the molecular-scale mechanisms and under what conditions they occur. This is in part due to the complexity of organic matter, which is a macromolecular assembly containing numerous functional groups capable of binding metals. Molecular insight into Ca-OM binding is essential to provide predictive knowledge on the conditions under which Ca stabilizes organic matter within sediments. Here, we propose to shed light on Ca binding in sediments and the role that it plays in OM sequestration by utilizing recently developed machine learning (ML) tools combined with X-ray spectroscopies. Specifically, we will use this approach to identify sensitive spectral fingerprints that can be used to distinguish which OM functional groups participate in Ca bridging. The structural and electronic structure information gained will be used to develop a comprehensive chemical and structural classification of Ca-organic matter complexation. We believe that this state-of-the-art approach can be used to aid the elucidation of the following hypotheses: a) Ca forms inner-sphere complexes with OM functional groups; and b) Ca is bound predominantly by carboxylate and catechol functional groups. Computational and experimental capabilities at EMSL and SSRL will be used to achieve this goal.
Project Details
Project type
Exploratory Research
Start Date
2021-12-01
End Date
2023-01-31
Status
Closed
Released Data Link
Team
Principal Investigator
Co-Investigator(s)
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