Recent Advances in Development of Organic Battery Materials for Monovalent and Multivalent Metal-Ion Rechargeable Batteries
Bibliographic record
Abstract
Rechargeable monovalent and multivalent metal-ion batteries have emerged as sustainable energy storage systems in view of their low cost, high safety, rich resources, and abundance of metallic resources (monovalent metals such as Li, Na and K and multivalent metals such as Mg, Ca, Zn and Al). However, their further development and application are hindered by the lack of high-energy electrode materials. Organic battery materials (OBMs) in both monovalent and multivalent metal–organic batteries (MOBs) offer unique opportunities thanks to their abundant structural diversity and tunability. This Review presents the recent progress on the developments of OBMs ( Collection of organic battery materials from the recently published articles in a single issue of the three ACS journals such as ACS Applied Polymer Materials, ACS Applied Energy Materials, and ACS Applied Materials & Interfaces ) in enhancing the electrochemical performances of MOBs, including nonaqueous rechargeable monovalent Li/Na/K-ion batteries, Li–S batteries and aqueous/nonaqueous rechargeable multivalent Mg/Ca/Zn/Al-ion batteries. We first presented the structural characteristics, energy storage mechanism and electrochemical performance of different types of OBMs in MOBs, including redox-active polymer, organosulfur compounds, redox-active porous polymers, conductive polymers, organic carbonyl compounds, small molecules, aromatic polymers/polyimides, polymer frameworks, covalent-organic frameworks and metal–organic frameworks and so on. Further, an overview of OBMs in MOBs, the correlation between molecular structure and electrochemical redox properties, and electrolyte system with working potential range is also provided and discussed. This is then followed by an overview on different strategies employed to realize the high-performances of MOBs through structural engineering, polymerization, hybridization, and amorphization of OBMs. Finally, a conclusion and perspective is given for the future development in OBMs for MOBs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".