Stability of Alizarin for Aqueous Organic Redox Flow Batteries
Bibliographic record
Abstract
The intermittent nature of renewable energy sources like solar and wind requires suitable large-scale energy storage technologies to properly respond supply and demand of energy. Aqueous organic redox flow batteries (AORFBs) have demonstrated great potential to revolutionize grid-scale energy storage as a result of their low-cost, safety and ability to separate energy and power1. However, most of the organic compounds suffer from lack of sufficient long-term stability. Alizarin (1,2-dihydroxyanthraquinone) is a low-cost, nontoxic, and industrially accessible dye which could potentially be used as an affordable redox-active negative electrolye (negolyte)2. Here, we present an in-depth investigation on the long-term cycling stability of Alizarin. Furthermore, we implement an aggressive cycling conditions to investigate the degradation of the cycled negolyte within just a single day, achieving an equivalent capacity fade percentage as electrolyte cycled for over two weeks3. The capacity fade of alizarin was partially mitigated by the employment of a SOC restriction strategy which was able to decrease the capacity fade rate by more than 60 %.4 Capacity fade measured via cycling is corroborated by ex situ chemical analysis methods. References: (1) Kwabi, D. G.; Ji, Y.; Aziz, M. J. Electrolyte Lifetime in Aqueous Organic Redox Flow Batteries: A Critical Review. Chem. Rev. 2020, 120 (14), 6467–6489. (2) Liu, Y.; Lu, S.; Chen, S.; Wang, H.; Zhang, J.; Xiang, Y. A Sustainable Redox Flow Battery with Alizarin-Based Aqueous Organic Electrolyte. ACS Appl. Energy Mater. 2019, 2 (4), (3) Goulet, M.-A.; Aziz, M. J. Flow Battery Molecular Reactant Stability Determined by Symmetric Cell Cycling Methods. J. Electrochem. Soc. 2018, 165 (7), A1466–A1477. (4) Goulet, M.-A.; Tong, L.; Pollack, D. A.; Tabor, D. P.; Odom, S. A.; Aspuru-Guzik, A.; Kwan, E. E.; Gordon, R. G.; Aziz, M. J. Extending the Lifetime of Organic Flow Batteries via Redox State Management. J. Am. Chem. Soc. 2019, 141 (20), 8014–8019.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".