Artificial α-amino acid based on cysteine grafted natural aloe-emodin for aqueous organic redox flow batteries
Bibliographic record
Abstract
Natural redox-active anthraquinone derivatives possess promising attributes for applications in aqueous organic redox flow batteries (AORFBs) due to their environmental friendliness and abundant sources. However, their limited aqueous solubility and electrochemical stability have posed significant challenges to their practical utilization. Herein, inspired by click chemistry, we report the synthesis of an artificial α-amino acid derived from cysteine-functionalized natural aloe-emodin (namely Cys-AE), which exhibits good water-solubility and redox-reversibility, particularly suited for alkaline AORFBs. The bio-inspired Cys-AE molecule exhibits a threefold increase in aqueous solubility compared to pristine aloe-emodin. Furthermore, the AORFB based Cys-AE negolyte with an electron concentration of 1.0 M demonstrates a low capacity fade rate of 0.000948% cycle−1 (equivalent to 0.0438% day−1) during 592 cycles, significantly outperforming the AORFB based on pristine aloe-emodin (0.00446% cycle−1, or 0.908% day−1) during 1564 cycles. Our investigation incorporates time-dependent density functional theory (TDDFT) simulations and detailed spectroscopic analyses reveal the essential role played by the asymmetric distribution of multiple solubilizing groups in enhancing the aqueous solubility and cycling stability of Cys-AE. This study highlights the potential of nature-inspired molecular engineering strategies in creating and crafting redox-reversible organic species poised to revolutionize large-scale and sustainable energy storage applications. Aqueous organic redox flow batteries face challenges due to the low solubility and stability of natural anthraquinones. Here, authors design a cysteine-functionalized aloe-emodin derivative with enhanced solubility and stability, achieving long-cycle life and high efficiency in alkaline 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".