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Record W2904464487 · doi:10.1149/ma2018-02/1/33

An Iodide-Based Complexing Agent for the Zinc-Iodide Flow Battery

2018· article· en· W2904464487 on OpenAlexaff
Fatemeh Shakeri Hosseinabad, Nael Yasri, Roland Roesler, Sladjana Maslovara, Edward P.L. Roberts

Bibliographic record

VenueECS Meeting Abstracts · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsTafel equationFlow batteryIodideTriiodideInorganic chemistryChemistryElectrochemistryElectrolyteCyclic voltammetryZincElectrodePolarization (electrochemistry)Physical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The effect of iodide-based complexing agent on the electrochemical performance of zinc-iodide flow battery was investigated. The addition of a complexing agent that will stabilize triiodide during charging can reduce the self-discharge and increase the charge and voltaic efficiency. The hydrogen evolution and kinetics of the both electrode reactions in the presence of the complexing agent was assessed. Cyclic voltammetry and Tafel analysis were performed to evaluate the influence of complexing agent on zinc electrochemical kinetics. The results indicated that the complexing agent enhances the cell performance by increasing the kinetics, which will enable operation of the battery at increased current density, or increases in the charge-discharge efficiency. At the positive electrode a deposit was observed on the electrode surface. The deposit was characterized by SEM and EDS and the deposition process was found to change significantly in the presence of the complexing agent. The charge-discharge characteristics of the electrolyte system were investigated in an H-type glass cell, and polarization curves were obtained for the two half-cell reactions.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.243
Threshold uncertainty score0.831

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
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 head0.041
GPT teacher head0.298
Teacher spread0.257 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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