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Record W4386867038 · doi:10.1149/ma2023-013781mtgabs

Utilizing Microelectrodes to Understand Indirect Redox Targeting Reactions for High Energy Density Flow Batteries

2023· article· en· W4386867038 on OpenAlexaff
Dan R Rourke, Shabdiki B. Chaurasia, Sundar Rajan Aravamuthan, Shyam Kumar Pahari, Vikram Singh, Janine Mauzeroll, Patrick J. Cappillino, Ertan Ağar

Bibliographic record

VenueECS Meeting Abstracts · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsMcGill University
Fundersnot available
KeywordsEnergy storageFlow batteryRedoxVanadiumElectrolyteChemistryOxidizing agentElectrochemistryChemical engineeringMaterials scienceElectrodeInorganic chemistryThermodynamicsPower (physics)

Abstract

fetched live from OpenAlex

One of the major problems with widespread implementation of renewable energy is the lack of cost-effective long-duration grid-scale energy storage filling in for the intermittent nature of solar and wind energy. Redox flow batteries (RFBs) are considered a promising solution to this problem due to their decoupled energy and power capabilities, allowing them to be scaled up for long-duration more efficiently and cost-effectively than other electrochemical energy storage devices [1]. The energy capacity of RFBs is primarily determined by the quantity of active species in the electrolyte, which is a product of the volume and the concentration. Thus, the limiting factor for energy density in RFBs is typically the solubility of the active species [2]. One probable path to enhancing the energy density without increasing the concentration is by utilizing the concept of indirect redox targeting reactions [3]. These redox-targeting flow batteries (RTFB) use solid charge storage materials in the electrolyte tanks to boost capacity, allowing the active species to act as mediators which transfer their charge by reversibly reducing or oxidizing the solid material [4]. In this study, cobalt hexacyanoferrate (CoHCF) was used as a solid capacity booster in an electrolyte containing vanadium(iv/v)bis-hydroxyiminodiacetate (VBH) as mediators in acetonitrile. Constant current cycling was performed in a symmetric cell configuration with vanadium ions shuttling between vanadium(iv) and vanadium(v) oxidation states. To monitor the concentration of redox active species accurately and in real-time, a carbon fiber ultramicroelectrode (UME) was installed on the capacity limiting half-cell. This in-line UME set-up provides a better understanding of how CoHCF interacts with mediators and insights on active species crossover between half-cells. In addition, this study aims to examine the kinetics of the indirect redox-targeting reactions. As a rarely studied charge transfer process, very few studies have been reported on the kinetics of indirect redox targeting reactions [5]. Scanning electrochemical microscopy is used to understand how counter cations affect the kinetics of the reaction between the solid booster material and the mediator. Various counter cations (e.g., Ca 2+ , Na + , Li + ) are used with the previously mentioned VBH-based mediators in order to determine the effective rate constants (k eff ) as well as the reversibility of the charge transfer reactions. References: [1] X. Wang, J. Chai, J. Jiang, Nano Mater. Sci. , 1, 17-24 (2021) [2] S. K. Pahari, T. C. Gokoglan, B. R. B. Visayas, J. Woehl, J. A. Golen, R. Howland, M. L. Mayes, E. Agar, P. J. Cappillino, RSC Adv. , 11, 5432-5443 (2021). [3] F. Zhang, M. Gao, S. Huang, H. Zhang, X. Wang, L. Liu, M. Han, Q. Wang, Adv. Mater. , 34, 2104562 (2021). [4] J. Egitto, T. C. Gokoglan, S.K. Pahari, J. N. Bolibok, S. R. Aravamuthan, F. Liu, X. Jin, P. J. Cappillino, E. Agar, ASME. J. Electrochem. En. Conv. Stor. , 19(4): 041005 (2022). [5] R. Yan, J. Ghilane, K. C. Phuah, T. N. Pham Truong, S. Adams, H. N. Randriamahazaka, Q. Wang, J. Phys. Chem. Lett. , 9 (3), 491-496 (2018).

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 categoriesMeta-epidemiology (narrow)
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 score0.238
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.025
GPT teacher head0.262
Teacher spread0.237 · 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.

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

Citations0
Published2023
Admission routes1
Has abstractyes

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