Integrated Trap‐Adsorption‐Catalysis Nanoreactor for Shuttle‐Free Aqueous Zinc‐Iodide Batteries
Bibliographic record
Abstract
Abstract Aqueous zinc‐iodine batteries (AZIBs) are very promising energy storage systems owing to their safety, reliability, large specific capacity, and durable lifespan. However, the sluggish iodine redox kinetics and polyiodides shuttle effect severely impedes their wider application. Addressing these challenges, this study develops a new multifunctional nanoreactor integrating “Trap‐Adsorption‐Catalysis” advantages, which features the electron‐rich cobalt (Co) nanoparticles embedded in porous activated carbon (AC). Benefiting from the integrated advantages of trap‐adsorption‐catalysis behavior in the nanoreactor, this novel system enables the fast iodine (I2/I‐) conversion by enhanced kinetics, achieving high utilization of iodine and corrosion‐free zinc anode without producing polyiodides. In situ UV–vis spectroscopy, theoretical calculation combined with electrochemical analysis demonstrates that the Co@AC nanoreactor reduces the adsorption energy and conversion energy barrier of iodine species, and accelerates the conversion of polyiodides to improve the electrochemical properties. Notably, the Co@AC/I2 cathode delivers an outstanding rate capability of 221.1 and 102.5 mA h g−1 at the current density of 0.5 and 25C with high CE over >99.9%, respectively, low self‐discharge rate over 96h and high energy efficiency (EE) of 89.2% at 5.0C over 1000 cycles. These self‐discharge and EE properties are the best among AZIBs systems with Co‐based host cathodes ever reported.
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 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 | 0.001 | 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".