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Record W4407599480 · doi:10.1016/j.gca.2025.02.015

Near-equilibrium kinetics in the Fe(II)-silicate system and the significance of nanoparticle greenalite in Archaean Iron Formations

2025· article· en· W4407599480 on OpenAlexafffund
Serhat Sevgen, Anika Retzmann, Michael Nightingale, Juan Carlos de Obeso, Qin Zhang, Ian N. Fleming, Rosalie Tostevin, Nicholas J. Tosca, Benjamin M. Tutolo

Bibliographic record

VenueGeochimica et Cosmochimica Acta · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsArcheanSilicateKineticsNanoparticleGeochemistryGeologyAstrobiologyChemistryChemical engineeringMaterials scienceNanotechnologyPhysicsOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

As the products of chemical sedimentation in the Archean oceans, Banded Iron Formations (BIFs) have been interpreted to record (bio)geochemical transitions in Earth’s ancient biosphere. Nonetheless, the effects of diagenesis and metamorphism over the long history of these rocks make it difficult to identify the minerals involved in the earliest stages of BIF formation. A series of recent studies has suggested that greenalite (Fe 2+ 3 Si 2 O 5 (OH) 4 ), formed through hydrothermal fluid-seawater interactions, was among the primary mineral components of BIFs. However, the reactivity of greenalite as a function of relevant environmental parameters has not yet been mechanistically studied. The plausibility of its role in forming BIF deposits therefore remains speculative. Here, we fill this knowledge gap by conducting a series of kinetic experiments using a novel Si isotope doping method with hydrated, amorphous Fe(II)-silicate (a precursor to crystalline greenalite). The advantage of this technique is that it permits simultaneous determination of near-equilibrium forward and reverse reaction rates of Fe(II)-silicate-fluid interaction in plausible Archean ocean compositions. Reaction rate calculations indicate that the system’s behavior is governed by Fe(II)-silicate saturation state, with SiO 2 sorption becoming dominant once a saturation threshold is exceeded. Combining kinetic data and thermodynamic calculations for the Fe-silicate-seawater system permits determination of a new solubility product for amorphous Fe(II)-silicate as log( K ) = 24.9 ± 0.25. This value indicates maximum Fe 2+ concentrations in Archean ocean waters at 25 °C would range from ∼ 1 mmol/kg at pH 7 to ∼ 10 µmol/kg at pH 8. Combining these observations with calculations of Stokes’ settling velocity implies that long-distance transport of greenalite nanoparticles – e. g., from deep-ocean hydrothermal vent sources to loci of BIF deposition – would have been feasible. Coupled with SiO 2 sorption behavior on greenalite surfaces and the background SiO 2 flux associated with the unique styles of Archean chert deposition, these results suggest that periodic waxing and waning of greenalite nanoparticle transport to BIF depositional environments can help to explain the Fe- and Si-enriched layers preserved in BIFs. Our results also provide a mechanistic underpinning for the exceptional preservation of greenalite in Archean sediments and its frequent association with chert. Ultimately, the readiness with which greenalite would have precipitated from Archean seawater and its apparent ability to be preserved despite transport across ocean basins suggests that it is time to reassess the traces of Earth’s early oceans recorded in BIFs and the ways in which these may be interpreted in light of new depositional models.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.010
GPT teacher head0.211
Teacher spread0.200 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2025
Admission routes2
Has abstractyes

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