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Record W2312312845 · doi:10.1166/jnn.2010.3715

Nanosilver Particle Formation on a High Surface Area Titanate

2010· article· en· W2312312845 on OpenAlexaff
Meng Shi, Christopher Lin, Lan Wu, Christopher M. Holt, David Mitlin, Steven M. Kuznicki

Bibliographic record

VenueJournal of Nanoscience and Nanotechnology · 2010
Typearticle
Languageen
FieldChemistry
TopicNanomaterials for catalytic reactions
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMaterials scienceSilver nanoparticleTransmission electron microscopyScanning electron microscopeChemical engineeringTitanateNanoparticleParticle (ecology)TitaniumParticle sizeSubstrate (aquarium)Ion exchangeNanotechnologyIonCeramicMetallurgyComposite materialChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Titanium based molecular sieves, such as ETS-10, have the ability to exchange silver ions and subsequently support self assembly of stable silver nanoparticles when heated. We report that a high surface area sodium titanate (resembling ETS-2) displays a similar ability to self template silver nanoparticles on its surface. Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) show high concentrations of silver nanoparticles on the surface of this sodium titanate, formed by thermal reduction of exchanged silver cations. The nanoparticles range in size from 4 to 12 nm, centered at around 6 nm. In addition to SEM and TEM, XRD and surface area analysis were used to characterize the material. The results indicate that this sodium titanate has a high surface area (>263 m2/g), and high ion exchange capacity for silver (30+ wt%) making it an excellent substrate for the exchange and generation of uniform, high-density silver nanoparticles.

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 score0.005
Threshold uncertainty score0.338

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 head0.010
GPT teacher head0.228
Teacher spread0.218 · 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

Citations4
Published2010
Admission routes1
Has abstractyes

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