Recent Progress on Organic Electrodes Materials for Rechargeable Batteries and Supercapacitors
Bibliographic record
Abstract
Rechargeable batteries are essential elements for many applications, ranging from portable use up to electric vehicles. Among them, lithium-ion batteries have taken an increasing importance in the day life. However, they suffer of several limitations: safety concerns and risks of thermal runaway, cost, and high carbon footprint, starting with the extraction of the transition metals in ores with low metal content. These limitations were the motivation for an intensive research to replace the inorganic electrodes by organic electrodes. Subsequently, the disadvantages that are mentioned above are overcome, but are replaced by new ones, including the solubility of the organic molecules in the electrolytes and lower operational voltage. However, recent progress has been made. The lower voltage, even though it is partly compensated by a larger capacity density, may preclude the use of organic electrodes for electric vehicles, but the very long cycling lives and the fast kinetics reached recently suggest their use in grid storage and regulation, and possibly in hybrid electric vehicles (HEVs). The purpose of this work is to review the different results and strategies that are currently being used to obtain organic electrodes that make them competitive with lithium-ion batteries for such applications.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.004 | 0.002 |
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".