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Record W2072675563 · doi:10.1021/ie050516x

Precipitation of Silver Powders in the Presence of Ethylenediamine Tetraacetic Acid

2006· article· en· W2072675563 on OpenAlexaff
Eduardo Villegas Ortega, Dimitrios Berk

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2006
Typearticle
Languageen
FieldChemistry
TopicNanomaterials for catalytic reactions
Canadian institutionsMcGill University
Fundersnot available
KeywordsChemistryEthylenediamineNucleationHydrazine (antidepressant)PrecipitationReducing agentAqueous solutionInorganic chemistrySalt (chemistry)Reaction rateArgentometryNuclear chemistryIonPhysical chemistryOrganic chemistryChromatographyCatalysis

Abstract

fetched live from OpenAlex

Silver powders can be produced by the reduction of a silver salt in an aqueous solution. The manipulation of the rates of the reaction allows the control of the properties such as size and size distribution of the produced particles. In the present work, we studied the addition of ethylenediamine tetraacetic acid (EDTA) as a complexation agent to moderate the rate of the reduction of silver cations with hydrazine as the reducing agent. The experiments were carried out in a batch reactor at different concentrations of the reactants. In the absence of EDTA, the reaction between hydrazine and the silver cations is extremely fast. It was found that, in the absence of seeds, the reaction starts by the precipitation of the free silver ions followed by a period where any reduced elemental silver forms sub-micrometer particles by nucleation. The addition of seeds promotes the growth of particles and suppresses nucleation. When the concentration seeds are greater than 5.4 × 10 -1 mM, no nucleation is apparent. This allows the determination of the reaction rate between hydrazine and silver cations. At 20 °C, the empirical rate law for the reduction of silver was r Ag = 9.35 [Ag] 1.3 [N 2 H 4 ] 0.7 [EDTA] -1.1 in mmol of Ag/L−min.

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)0.002
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 score0.003
Threshold uncertainty score0.542

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.050
GPT teacher head0.302
Teacher spread0.253 · 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

Citations15
Published2006
Admission routes1
Has abstractyes

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