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Record W1985614254 · doi:10.1128/aem.00490-09

Extracellular Iron Biomineralization by Photoautotrophic Iron-Oxidizing Bacteria

2009· article· en· W1985614254 on OpenAlexaff
Jennyfer Miot, Karim Benzerara, Martin Obst, Andreas Kappler, Florian Hegler, Sebastian Schädler, Camille Bouchez, François Guyot, Guillaume Morin

Bibliographic record

VenueApplied and Environmental Microbiology · 2009
Typearticle
Languageen
FieldEnergy
TopicIron oxide chemistry and applications
Canadian institutionsMcMaster UniversityCanadian Light Source (Canada)
Fundersnot available
KeywordsBiomineralizationOxidizing agentGoethiteIron bacteriaMagnetotactic bacteriaMagnetiteChemistryBacteriaRedoxChemical engineeringInorganic chemistryMaterials scienceBiologyMetallurgyOrganic chemistry

Abstract

fetched live from OpenAlex

Iron oxidation at neutral pH by the phototrophic anaerobic iron-oxidizing bacterium Rhodobacter sp. strain SW2 leads to the formation of iron-rich minerals. These minerals consist mainly of nano-goethite (alpha-FeOOH), which precipitates exclusively outside cells, mostly on polymer fibers emerging from the cells. Scanning transmission X-ray microscopy analyses performed at the C K-edge suggest that these fibers are composed of a mixture of lipids and polysaccharides or of lipopolysaccharides. The iron and the organic carbon contents of these fibers are linearly correlated at the 25-nm scale, which in addition to their texture suggests that these fibers act as a template for mineral precipitation, followed by limited crystal growth. Moreover, we evidence a gradient of the iron oxidation state along the mineralized fibers at the submicrometer scale. Fe minerals on these fibers contain a higher proportion of Fe(III) at cell contact, and the proportion of Fe(II) increases at a distance from the cells. All together, these results demonstrate the primordial role of organic polymers in iron biomineralization and provide first evidence for the existence of a redox gradient around these nonencrusting, Fe-oxidizing bacteria.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score0.881

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.004
GPT teacher head0.171
Teacher spread0.168 · 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

Citations172
Published2009
Admission routes1
Has abstractyes

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