An amphoteric and hydrogen-bond-rich artificial α-amino acid for highly durable aqueous redox flow batteries
Bibliographic record
Abstract
Aqueous organic redox flow batteries offer promising prospects for large-scale, high-safety, and cost-effective energy storage systems with no reliance on scarce mineral resources. However, challenges such as limited water solubility and poor stability hinder the practical application of organic redox molecules in aqueous organic redox flow batteries. Herein, we report the design and synthesis of an artificial redox-active α-amino acid molecule by functionalizing 1,5-dihydroxyanthraquinone with natural cysteine side group, which exhibits enhanced aqueous solubility and redox reversibility in alkaline aqueous organic redox flow batteries. Owing to its unique zwitterionic structure and abundant hydrogen bonds, the negolyte based on artificial α-amino acid molecule exhibits a very low capacity decay rate of 0.00025% per cycle (equivalent to 0.011% per day) under 1 M electron transfer. Theoretical simulations and spectroscopic analyses underscore the importance of the symmetric distribution and abundant hydrogen-bonding interactions of amphipathic amino acid side chains in enhancing the stability of the anthraquinone redox core and reducing its dimerization, as well as enhancing its water solubility and redox reversibility. This study presents the promising potential of nature-inspired principles in designing electrochemically stable, redox-active organic molecules, contributing to the advancement of large-scale, biocompatible, and sustainable aqueous organic redox flow batteries.
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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 | 0.000 |
| 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 | 0.000 |
| 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 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".