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 power 1 . 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 weeks 3 . 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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".