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Record W4409924672 · doi:10.26434/chemrxiv-2025-k697f

Enhanced Flow Battery Electrolyte Solubility andStability via Synergistic Anthraquinone Interactions

2025· preprint· en· W4409924672 on OpenAlexafffund
Meysam Maleki, Marc‐Antoni Goulet

Bibliographic record

VenueChemRxiv · 2025
Typepreprint
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsConcordia University
FundersFonds de recherche du Québec – Nature et technologiesConcordia University
KeywordsElectrolyteAnthraquinoneSolubilityBattery (electricity)ChemistryFlow batteryChemical engineeringFlow (mathematics)Inorganic chemistryElectrodeOrganic chemistryThermodynamicsPhysical chemistryEngineeringMechanicsPhysics

Abstract

fetched live from OpenAlex

Cost-effective anthraquinones, such as Alizarin, are promising for aqueous organic redox flow batteries (RFBs), but their low solubility limits the energy density of the electrolyte. Moreover, their degradation during cycling reduces battery lifespan. Here, we reveal molecular synergy between Alizarin and its derivative, Alizarin Red S, which increases their combined solubility by 40%, from an expected 0.5 M (0.2 M Alizarin + 0.3 M Alizarin Red S, their practical individual solubility limits) to 0.7 M. Additionally, these interactions lead to a 40% reduction in capacity fade rate compared to a rule of mixtures prediction, demonstrating the stabilizing effects of molecular interactions in the binary electrolyte. Extending this approach to a ternary mixture with 2,6-DHAQ, we achieve a total concentration of 1 M, further demonstrating synergistic interactions. These findings highlight how mixtures of redox active molecules can unexpectedly enhance the stability and energy density of organic RFBs through molecular cooperativity.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
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 score0.676
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.003
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 head0.017
GPT teacher head0.288
Teacher spread0.272 · 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

Citations2
Published2025
Admission routes2
Has abstractyes

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