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Record W4386855094 · doi:10.1149/ma2023-013784mtgabs

Considerations for Ionic Diffusion in Slurry Electrolytes for Redox Flow Batteries

2023· article· en· W4386855094 on OpenAlexaboutno aff
Vincent Tam, Jesse S. Wainright

Bibliographic record

VenueECS Meeting Abstracts · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsnot available
Fundersnot available
KeywordsElectrolyteSlurryElectrodeFlow batteryChemistryChemical engineeringMaterials scienceOverpotentialInorganic chemistryElectrochemistryComposite material

Abstract

fetched live from OpenAlex

Slurry electrodes have been proposed as a means to enhance the scalability of hybrid redox flow battery (RFB) chemistries for better usability in utility scale energy storage applications1–3. In conventional hybrid RFB’s, scalability is limited due the spatial constraints of the flow cell and the metal deposited by the negative half-reaction on charge1. By using a slurry electrode, the solid metal can be deposited onto electrically conductive particles dispersed in the electrolyte instead of on the stationary electrode within the flow cell. In this way, hybrid RFB chemistries can achieve the same scalability as more commonly studied true RFB chemistries, such as all-vanadium. Due to the high abundance, low cost, and low toxicity of iron electrolytes, the all-iron RFB chemistry is of particular interest for use with a slurry electrode2,4. The usefulness of the slurry electrode depends on the current distribution of the plating reaction. To successfully decouple the storage and power capacities of the RFB and thus enhance its scalability5, the faradaic current of the plating reaction must occur predominantly on the mobile slurry particles, as opposed to on the stationary current collector1. This current distribution is dependent on a variety of factors, such as the applied overpotential, the electrical conductivity of the slurry, the ionic conductivity of the electrolyte, the kinetics of the reaction, and the rate of ionic mass transport to reaction sites. Ionic mass transport in electrolytes containing slurry electrodes may differ from ionic transport in neat electrolyte in interesting and important ways. Due to the volume fraction of the electrolyte occupied by solid particles, the effective concentration of the ionic species may be lower than in neat electrolyte. Further, the solid particle volume fraction hinders ionic diffusion by introducing diffusion path tortuosity. This effect is more severe in higher slurry particle loadings. In this work, the effect of varying dispersed solid particle loading on ionic diffusivity is investigated via voltammetry using a rotating disk electrode. The diffusivities of ionic iron species are measured as a function of the volume fraction of solids dispersed in the electrolyte. Comparisons with the Bruggeman correlation6,7 are made and amendments to the Levich equation are considered. (1) Petek, T. J.; Hoyt, N. C.; Savinell, R. F.; Wainright, J. S. Slurry Electrodes for Iron Plating in an All-Iron Flow Battery. J. Power Sources 2015, 294, 620–626. https://doi.org/10.1016/j.jpowsour.2015.06.050. (2) Petek, T. J. Enhancing the Capacity of All-Iron Flow Batteries: Understanding Crossover and Slurry Electrodes. Ph.D. Thesis 2015, No. May. (3) Narayanan, T. M.; Zhu, Y. G.; Gençer, E.; McKinley, G.; Shao-Horn, Y. Low-Cost Manganese Dioxide Semi-Solid Electrode for Flow Batteries. Joule 2021, 5 (11), 2934–2954. https://doi.org/10.1016/j.joule.2021.07.010. (4) Dinesh, A.; Olivera, S.; Venkatesh, K.; Santosh, M. S.; Priya, M. G.; Inamuddin; Asiri, A. M.; Muralidhara, H. B. Iron-Based Flow Batteries to Store Renewable Energies. Environ. Chem. Lett. 2018, 16 (3), 683–694. https://doi.org/10.1007/s10311-018-0709-8. (5) Weber, A. Z.; Mench, M. M.; Meyers, J. P.; Ross, P. N.; Jeffrey, T.; Liu, Q. Redox Flow Batteries , a Review Environmental Energy Technologies Division , Lawrence Berkeley National Laboratory , Department of Mechanical , Aerospace and Biomedical Engineering , University of Tennessee , Department of Chemical Engineering , McGill Un. 1–72. (6) Tjaden, B.; Cooper, S. J.; Brett, D. J.; Kramer, D.; Shearing, P. R. On the Origin and Application of the Bruggeman Correlation for Analysing Transport Phenomena in Electrochemical Systems. Curr. Opin. Chem. Eng. 2016, 12, 44–51. https://doi.org/10.1016/j.coche.2016.02.006. (7) Chung, D. W.; Ebner, M.; Ely, D. R.; Wood, V.; Edwin García, R. Validity of the Bruggeman Relation for Porous Electrodes. Model. Simul. Mater. Sci. Eng. 2013, 21 (7). https://doi.org/10.1088/0965-0393/21/7/074009.

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)0.003
metaresearch head score (Gemma)0.008
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: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0030.006
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0060.003

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 head0.025
GPT teacher head0.280
Teacher spread0.255 · 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
Published2023
Admission routes1
Has abstractyes

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