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Record W4412672938 · doi:10.1016/j.gca.2025.07.023

Impact of silica on the identity of minerals formed in Archean oceans during Fe cycling by cyanobacteria and iron(III)-reducing bacteria

2025· article· en· W4412672938 on OpenAlexaff
Carolin Dreher, Manuel Schad, Jan‐Peter Duda, Stefan Fischer, Jeremiah Shuster, Yuhao Li, Kurt O. Konhauser, Andreas Kappler

Bibliographic record

VenueGeochimica et Cosmochimica Acta · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeochemistry and Elemental Analysis
Canadian institutionsUniversity of Alberta
FundersDeutsche Forschungsgemeinschaft
KeywordsArcheanCyanobacteriaCyclingBacteriaEnvironmental chemistryGeologyBanded iron formationGeochemistryChemistryPaleontologyGeographyArchaeology

Abstract

fetched live from OpenAlex

Banded Iron Formations (BIF) are iron- and silica-rich marine sediments deposited between 3.8 and 1.85 Ga. With the evolution of cyanobacteria, iron cycling via abiotic Fe(II) oxidation with O 2 produced by cyanobacteria and Fe(III) reduction by Fe(III)-reducing microorganisms is hypothesized to be responsible for BIF mineral deposition and transformation both in the water column and in the resulting sedimentary deposits. However, the impact of silica on iron mineralogy during microbially influenced iron cycling has not been determined experimentally under relevant ancient conditions. Hence, we set up batch incubation experiments with different concentrations of silica (0 to 2.2 mM) in the presence of varying concentrations of Fe(II) (0.5 to 5 mM), a marine cyanobacterium ( Synechococcus PCC7002) and a marine dissimilatory Fe(III)-reducer ( Shewanella colwelliana ). We found that the fluctuation between microbial production of O 2 (cyanobacteria) and the activity of Fe(III)-reducing bacteria led to alternating Fe(II) oxidation and Fe(III) reduction and the precipitation of various Fe(II)- and Fe(III)-bearing minerals. Using a combination of X-ray diffraction and 57 Fe Moessbauer spectroscopy we identified poorly crystalline Fe(III)-bearing minerals (e.g., ferrihydrite), and the well crystalline ones (e.g., goethite, and lepidocrocite) in the absence of silica. Fe minerals precipitated in the presence of silica showed more pronounced reflections in µXRD, indicating higher crystallinity, while 57 Fe Moessbauer spectroscopy suggested the formation of Fe(II) silicate minerals and Fe(III) (oxyhydr)oxide minerals associated with silica. Furthermore, the presence of silica led to higher oxidation and reduction rates but more incomplete Fe(II) oxidation, while the reduction extent was higher in the presence of silica. In summary, our experiments showed that the presence of silica clearly affects Fe cycling and Fe mineral (trans)formation by cyanobacteria and Fe(III)-reducing bacteria, with relevance for the deposition of Precambrian Banded Iron Formations.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.715
Threshold uncertainty score0.771

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.009
GPT teacher head0.249
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2025
Admission routes1
Has abstractyes

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