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Record W1968648881 · doi:10.1080/01490450802402612

Interfacial Energetics of Bacterial Silicification

2008· article· en· W1968648881 on OpenAlexaff
F. G. Ferris, E. Magalhaes

Bibliographic record

VenueGeomicrobiology Journal · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicMinerals Flotation and Separation Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNucleationNanoparticlevan der Waals forceSolubilitySurface energySuspension (topology)Particle sizeBacteriaChemical engineeringParticle (ecology)Transmission electron microscopyMaterials scienceChemistryMineralogyNanotechnologyGeologyPhysical chemistryOrganic chemistryMolecule

Abstract

fetched live from OpenAlex

Silica nanoparticle size distributions were measured from transmission electron micrographs of bacterial cells in thin-sectioned hot spring sediment samples, as well as specimens from laboratory experiments on bacterial silicification. Diameters of silica nanoparticles on bacterial cells were smaller than corresponding values of those occurring away from bacteria in the same fields of view. Regression analysis of the particle size data and application of the Lifshitz-van der Waals Acid-Base approach to evaluations of solid surface energy established that the size difference extends from a nearly 40% decrease in the mean interfacial energy of silica nanoparticles on bacteria (0.9 mJ/m 2 ) as opposed to free in aqueous suspension (1.2 mJ/m 2 ). In thermodynamic terms, the lower interfacial energy serves to reduce equilibrium solubility values, and enhance nucleation rates, of silica nanoparticles on bacteria cells. The well documented culmination of these events in hot spring sediments and experimental studies is rapid preferential silicification and structural preservation of bacterial cells.

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 categoriesInsufficient payload (model declined to judge)
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 score0.086
Threshold uncertainty score0.995

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)0.0060.000

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 head0.014
GPT teacher head0.228
Teacher spread0.214 · 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.

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

Citations17
Published2008
Admission routes1
Has abstractyes

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