Characterization of Iron-Oxides Formed by Oxidation of Ferrous Ions in the Presence of Various Bacterial Species and Inorganic Ligands
Bibliographic record
Abstract
The oxidation of ferrous ions in the presence of an excess of dissolved oxygen at neutral pH generally leads to the formation of lepidocrocite. The effect of inorganic ligands (PO4, SO4, or Si(OH)4) in concentrations typical of those in sediment pore waters, and of microorganisms (Escherichia coli K12, Pseudomonas aeruginosa PA01, Bacillus subtilis or Bacillus licheniformis) on the mineralogy, chemical composition, morphology and spatial distribution of the iron-oxides were examined using various complementary techniques, including TEM, XRD, and EXAFS. The presence of inorganic ligands during the oxidation can affect the mineralogy as well as the size and structure of the Fe-oxide particles. While the presence of sulfate (SO4/Fe = 0.5) had little effect on the outcome of the Fe-oxide synthesis, low quantities of phosphate (PO4/Fe = 0.05) inhibited lepidocrocite and large quantities of aqueous silica (Si/Fe = 5) favored the formation of 2-line ferrihydrite. The presence of any of the four representative species of bacterial cells in the various systems did not modify the mineralogy of the Fe-oxides. However, the size of the Fe-oxide particles tended to be reduced, and the presence of the cells also affected the spatial organization and the morphology of the particles. In addition, in some systems, some of the iron remains adsorbed on the cells and does not contribute to the formation of mineral phases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 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".