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Record W2570142455 · doi:10.1021/acs.langmuir.6b03978

Evidence of Faradaic Reactions in Electrostatic Atomizers

2017· article· en· W2570142455 on OpenAlexaff
A. N. Sankaran, Christopher Staszel, Rakesh P. Sahu, Alexander L. Yarin, Farzad Mashayek

Bibliographic record

VenueLangmuir · 2017
Typearticle
Languageen
FieldEngineering
TopicElectrohydrodynamics and Fluid Dynamics
Canadian institutionsMcMaster University
FundersDivision of Chemical, Bioengineering, Environmental, and Transport Systems
KeywordsAnodeNozzleCathodeElectrodeBrassMechanicsContactorCurrent (fluid)Electric currentConical surfaceElectrostaticsElectric chargeMaterials scienceChemistryComposite materialAnalytical Chemistry (journal)Atomic physicsCopperThermodynamicsPhysicsMetallurgyPhysical chemistry

Abstract

fetched live from OpenAlex

Any rational theory of electrostatic atomizers (EAs) would require a detailed understanding of the nature of the polarized layer near the electrode, since this is the source of the electric charge carried by the jets issued from the EAs. The polarized layer either is driven out as the electrically-driven Smoluchowski flow and/or entrained by the viscous shear imposed by the bulk flow. The standard Gouy-Chapman theory of polarized diffuse layers implies zero electric current passing across the layer, which is impossible to reconcile with the fact that there are leak currents in the EAs. Here, we show that the electric current through the EA is controlled by faradaic reactions at the electrodes. The experiments were conducted with stainless steel or brass pin-like cathodes and three different anode (the conical nozzle) materials, which were copper, stainless steel, and brass. The different electrode materials resulted in different spray, leakage, and total currents in all the cases. Accordingly, it is shown that the total electric current generated by EAs can be controlled by the cathode and anode materials, i.e., by faradaic reactions on them. This lays the foundation for a more detailed understanding and description of the operation of EAs.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.885
Threshold uncertainty score0.278

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 head0.013
GPT teacher head0.248
Teacher spread0.235 · 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 designSimulation or modeling
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

Citations9
Published2017
Admission routes1
Has abstractyes

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