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Record W2910409069 · doi:10.1021/acs.langmuir.8b02846

Liquid Cell Transmission Electron Microscopy Sheds Light on The Mechanism of Palladium Electrodeposition

2019· article· en· W2910409069 on OpenAlexafffund
Jie Yang, Carmen M. Andrei, Yuting Chan, B. Layla Mehdi, Nigel D. Browning, Gianluigi A. Botton, Leyla Soleymani

Bibliographic record

VenueLangmuir · 2019
Typearticle
Languageen
FieldChemistry
TopicElectrochemical Analysis and Applications
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaFaraday Institution
KeywordsNucleationPalladiumTransmission electron microscopyScanning electron microscopeElectrolyteMaterials scienceNanotechnologyElectrochemistryElectrochemical cellChemical engineeringFabricationMicroscopyScanning transmission electron microscopyElectrodeChemistryOpticsComposite materialCatalysisPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Electrodeposition is widely used to fabricate tunable nanostructured materials in applications ranging from biosensing to energy conversion. A model based on 3D island growth is widely accepted in the explanation of the initial stages of nucleation and growth in electrodeposition. However, there are regions in the electrodeposition parameter space where this model becomes inapplicable. We use liquid cell transmission electron microscopy along with post situ scanning electron microscopy to investigate electrodeposition in this parameter space, focusing on the effect of the supporting electrolyte, and to shed light on the nucleation and growth of palladium. Using a collection of electron microscopy images and current time transients recorded during electrodeposition, we discover that electrochemical aggregative growth, rather than 3D island growth, best describes the electrodeposition process. We then use this model to explain the change in the morphology of palladium electrodeposits from spherical to open clusters with nonspherical morphology when HCl is added to the electrolyte solution. The enhanced understanding of the early stages of palladium nucleation and growth and the role of electrolyte in this process provides a systematic route toward the electrochemical fabrication of nanostructured materials.

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 score0.030
Threshold uncertainty score0.914

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)0.0010.000

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 head0.003
GPT teacher head0.210
Teacher spread0.207 · 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

Citations32
Published2019
Admission routes2
Has abstractyes

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