Enhanced Flow Battery Electrolyte Solubility andStability via Synergistic Anthraquinone Interactions
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".