Submicron-Scale Heterogeneities in Nickel Sorption of Various Cell–Mineral Aggregates Formed by Fe(II)-Oxidizing Bacteria
Bibliographic record
Abstract
Fe(II)-oxidizing bacteria form biogenic cell-mineral aggregates (CMAs) composed of microbial cells, extracellular organic compounds, and ferric iron minerals. CMAs are capable of immobilizing large quantities of heavy metals, such as nickel, via sorption processes. CMAs play an important role for the fate of heavy metals in the environment, particularly in systems characterized by elevated concentrations of dissolved metals, such as mine drainage or contaminated sediments. We applied scanning transmission (soft) X-ray microscopy (STXM) spectrotomography for detailed 3D chemical mapping of nickel sorbed to CMAs on the submicron scale. We analyzed different CMAs produced by phototrophic or nitrate-reducing microbial Fe(II) oxidation and, in addition, a twisted stalk structure obtained from an environmental biofilm. Nickel showed a heterogeneous distribution and was found to be preferentially sorbed to biogenically precipitated iron minerals such as Fe(III)-(oxyhydr)oxides and, to a minor extent, associated with organic compounds. Some distinct nickel accumulations were identified on the surfaces of CMAs. Additional information obtained from scatter plots and angular distance maps, showing variations in the nickel-iron and nickel-organic carbon ratios, also revealed a general correlation between nickel and iron. Although a high correlation between nickel and iron was observed in 2D maps, 3D maps revealed this to be partly due to projection artifacts. In summary, by combining different approaches for data analysis, we unambiguously showed the heterogeneous sorption behavior of nickel to CMAs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 teacher head, 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".