An active bifunctional natural dye for stable all-solid-state organic batteries
Bibliographic record
Abstract
Sustainable and cost-effective organic electrode materials are promising for next-generation lithium-ion batteries but are hindered by severe shuttle effects. While all-solid-state batteries offer a potential solution, chemical and mechanical incompatibility between organic electrode materials and inorganic solid electrolytes limit areal capacity and cycling stability, falling short of practical requirements. Here, we report a bifunctional indigo natural dye that serves as both an active material and a solid molecular catalyst in sulfide-based all-solid-state batteries, addressing these compatibility challenges. Contrary to the prevailing view that chemical reactions between organic electrode materials and sulfide solid electrolytes are detrimental, our study reveals that controlled reactions between indigo and Li6PS5Cl solid electrolyte catalyze their synergistic redox process after optimizing electrode microstructures. This strategy enables a high reversible capacity of 583 mAh g−1 (Li6PS5Cl contribution: 379 mAh g−1) at 0.1 C, a high areal capacity of 3.84 mAh cm−2, and good cycling stability at an operation temperature of 25 °C. These findings highlight the potential of bifunctional organic electrode materials in sulfide-based all-solid-state batteries to overcome the key challenges of organic electrode materials in practical applications. Incompatibility between organic electrodes and inorganic solid electrolytes limits the performance of solid-state organic batteries. Here, the authors introduce indigo natural dye as a redox-active material and molecular catalyst, enabling high capacity and long cycle life via synergistic redox reactions with sulfide electrolytes.
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 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 | 0.000 |
| 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 | 0.001 | 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".