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Record W2603324492 · doi:10.1149/ma2017-01/2/180

A New Membrane-Less Porous Electrode Cell Design for Zinc-Iodide Redox Flow Battery

2017· article· en· W2603324492 on OpenAlexaff
Fatemeh Shakeri Hosseinabad, Daouda Fofana, Jialang Li, Edward P.L. Roberts

Bibliographic record

VenueECS Meeting Abstracts · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsZincElectrolyteFlow batteryIodideElectrodeAnalytical Chemistry (journal)ChemistryInorganic chemistryMembraneRedoxCyclic voltammetryMaterials scienceChemical engineeringElectrochemistryChromatographyOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

A new flow field using a flow through porous electrode for membrane-less flow battery has been evaluated using the zinc-iodide redox system. The flow through the porous electrode enables good mass transport at the zinc electrode, and thus good performance was obtained. The flow through arrangement also allowed separation of the charged species without the use of a membrane. An electrolyte system of zinc-iodide, iodine, and zinc chloride was used. A 5cm2 carbon paper (39AA SIGRACET) was used as the electrode. Cyclic voltammetry studies were carried out for the electrolyte system using a glassy carbon working electrode in the range of -2 to 2V (versus Ag/AgCl) , and the effect of zinc-iodide concentration and different range of scan rate was investigated. Mass transfer modeling of the zinc iodine system during the discharge process was carried out and the concentration profile was obtained. The variation of zinc-iodide concentration along the length of the cathode, resulted in spatial variation in mass flux and current density. The 2-D model is reduced to an equivalent 1-D model for the concentration profile by using realistic boundary conditions at the electrode along with using similarity technique. The effect of Péclet number, zinc-iodide concentration and flow rate on columbic and energy efficiency of the new cell will be presented.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.030
GPT teacher head0.268
Teacher spread0.238 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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