Microbial reduction of synthetic Biogenic Iron Oxides containing various amounts of Organic Carbon and Silica
Bibliographic record
Abstract
Iron oxides formed in close association with bacteria are referred to as biogenic minerals (BIOS). Both the organic part of BIOS and the iron oxide particles affect the net surface charge of those iron-organic carbon aggregates and offer reactive sites that can immobilize many soluble contaminants (Warren and Haack 2001) making BIOS a contender in bioremediation technologies. However, before using BIOS in bioremediation, it is essential to understand the interactions of impurities such as organic matter and other minor components (including silica) (Dyer et al. 2010). This project involves the synthesis of Biogenic Iron Oxides (BIOS) using various silica contents and different soluble alginate concentrations (as an analogue for bacterial exopolysaccharides) close to natural environmental conditions. The mineralogical, chemical and physical composition of the synthesized samples was determined by X-Ray Diffraction (XRD), Scanning Electron Microscopy (SEM), Field Emission Scanning Electron Microscopy (FESEM), Fourier-transform Infrared Spectroscopy (FTIR), and with a Malvern Zetasizer Nano instrument. The various samples (mainly ferrihydrite) were then reduced in the presence of Shewanella putrefaciens CN32, a well-known iron reducing bacterium. All microbial reduction experiments (see Table 1) with different types of BIOS were performed under anoxic conditions. Results indicate that the ratio of organic matter and silica to Fe (III) in BIOS affects the reduction rate. It is proposed that alginate binds to iron oxide particles and protects them from reduction. However, samples in presence of high concentration of silica (i.e.,> 0.05) showed structural disorder which likely prevented nucleation of well ordered ferrihydrite, which in return increased their rate of reduction. In addition, higher reduction rates of ferrihydrite were reported at higher concentrations of silica in BIOS, even in the presence of alginate.
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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 | 0.000 | 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".