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Record W2328854913 · doi:10.1021/cm2020498

Synthesis of Porous Metallic Monoliths via Chemical Reduction of Au(I) and Ag(I) Nanostructured Sheets

2011· article· en· W2328854913 on OpenAlex
Gilles R. Bourret, Paul J. G. Goulet, R. Bruce Lennox

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueChemistry of Materials · 2011
Typearticle
Languageen
FieldMaterials Science
TopicNanoporous metals and alloys
Canadian institutionsMcGill University
Fundersnot available
KeywordsMaterials sciencePorosityChemical engineeringElectrocatalystChemical reductionNanotechnologyNanofiberMetalYield (engineering)FabricationNanoscopic scaleCatalysisConductivityComposite materialElectrochemistryMetallurgyElectrodeChemistryOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

The facile fabrication of free-standing conductive Au(0) and Ag(0) nanostructured monoliths via the chemical reduction of sheets composed respectively of AuCl( n -butylamine) nanofibers and aggregated AgCl nanocubes is reported. This preparation can be performed on a large scale (hundreds of milligrams) with high yield (>74%). The product sheets have large dimensions (several cm 2 ), high conductivity (>1800 S m –1 for the Au(0) sheets), and large surface areas (2 m 2 g –1 or 400 m 2 mol –1 for the Au(0) sheets). The macroscopic structure of the Au(I) and Ag(I) sheets is preserved during the chemical reduction process. At the nanoscale the Au(I) fibers are converted into Au(0) ribbons and fibers, and the AgCl cubes are converted into porous Ag(0) cubes. The resulting porous metal sheets provide physically stable Au(0) and Ag(0) foams that are highly sought-after in catalysis, sensing, and electrocatalysis applications.

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.

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 score0.003
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0020.000

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 head0.212
Teacher spread0.198 · 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