2D Raman spectroscopy study of dolomite and cyanobacterial extracellular polymeric substances from Khor Al‐Adaid sabkha (Qatar)
Bibliographic record
Abstract
Dolomite precipitation, limited at low temperature, appears to be impacted by microbial extracellular polymeric substances (EPS). The presence of dolomites has been reported in the extreme environments of Arabian Gulf sabkhas. Many of these sites are characterized by extensive growth of cyanobacterial mats that are recognized as key producers of EPS. However, no information has been gathered on the cyanobacterial EPS involvement in dolomite precipitation. The objective of this study was to obtain in situ information on the spatial distribution of cyanobacterial EPS and dolomite in Khor Al‐Adaid sabkha (Qatar) sediments by chemical mapping. For this purpose, in situ 2D Raman spectroscopy and atomic force microscopy were applied. Additionally, samples were analyzed with scanning electron microscopy and X‐ray diffraction. Raman fingerprints of dolomite (300, 725, and 1098 cm−1), cyanobacteria, and their EPS (1000, 1130, 1148, and 1508 cm−1) were observed widely distributed in the top 2 cm of the sabkhas sediments. 2D chemical imaging of sediment layers characterized minerals and organic matter of microbial origins at high spatial resolution. Raman mapping indicated small dolomite clusters (<2 µm) embedded in a dense cyanobacterial EPS matrix. The spatial distribution showed that small dolomite clusters are closely associated with cyanobacterial EPS and organic carbon. Our results prove that cyanobacterial molecules are closely related to dolomite in the sabkhas sediments. This study demonstrated that Raman mapping is a robust and sensitive technique for acquisition of in situ information on cell–mineral interactions. Copyright © 2013 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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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 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".