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Record W3000349238 · doi:10.1039/c9nr09419a

Effect of the driving force on nanoparticles growth and shape: an opto-electrochemical study

2020· article· en· W3000349238 on OpenAlexaff
Jean‐Marc Noël, Mathias Miranda Vieira, Vitor Brasiliense, Jean‐François Lemineur, Catherine Combellas, Frédéric Kanoufi

Bibliographic record

VenueNanoscale · 2020
Typearticle
Languageen
FieldChemistry
TopicElectrochemical Analysis and Applications
Canadian institutionsCanadian Nautical Research Society
FundersUniversité de ParisCentre National de la Recherche ScientifiqueUniversité Paris DiderotAgence Nationale de la Recherche
KeywordsElectrochemistryNanoparticleMaterials scienceNanotechnologyChemistryElectrode

Abstract

fetched live from OpenAlex

Most protocols developed to synthesize nanoparticles (NPs) and to control their shape are inspired from nucleation and growth theories. However, to rationalize the mechanisms of the shape-selective synthesis of NPs, experimental strategies allowing to probe in situ the growth of NPs are needed. Herein, metal Au or Ag nanoparticles (NPs) are produced by reaction of a metallic ion precursor with a reversible redox reducer. The process is explored by an oxidative electrosynthesis strategy using a sacrificial Au or Ag ultramicroelectrode to both trigger the metallic ion generation and control the local concentrations of the different reactants. The effect of the driving force for the metallic ion reduction over metal NP growth dynamics is inspected in situ and in real time at the single NP level by high-resolution optical microscopy from the tracking of the Brownian trajectories of the growing NPs in solution. The NP reductive growth/oxidative etching thermodynamics, and consequently the NP shape, are shown to be controlled electrochemically by the reversible redox couple, while the intervention of an Au(i) intermediate ion is suggested to account for the formation of gold nanocubes.

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 score0.021
Threshold uncertainty score0.323

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 head0.006
GPT teacher head0.240
Teacher spread0.234 · 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
Published2020
Admission routes1
Has abstractyes

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