Perylenedianhydride-Based Polyimides as Organic Cathodes for Rechargeable Lithium and Sodium Batteries
Bibliographic record
Abstract
Organic carbonyl molecules have recently been investigated as redox-active electrode materials in rechargeable organic batteries (ROBs), and although redox-active polymers offer high specific energy density and tunable redox potential windows, their undesirable dissolution into aprotic electrolytes during charge/discharge cycling and their poor electronic conductivity compromised their utilization in ROBs. To overcome these challenges, we synthesized, for the first time, two 3,4:9,10-perylenetetracarboxylic dianhydride ( PTCDA )-based polyimides, namely, perylenediimide-benzidine ( PDI-Bz ) and perylenediimide-urea ( PDI-Ur), and utilized them as organic cathode materials for lithium-ion batteries and sodium-ion batteries. These cathode materials are synthesized through imidization of a non-bay-substituted PTCDA unit by using bifunctional amine compounds (i.e., benzidine and carbonyl diamine (urea)) via a simple one-step reaction. Our organic metal-ion batteries employing PDI-Bz demonstrate a high discharge capacity of 120 mAh/g (with a reversible capacity of ∼54 mAh/g) vs Li + /Li and the second discharge capacity of 111 mAh/g (∼74 mAh/g) vs Na + /Na with two discharge voltage plateaus in the range of 1.9–2.4 V. The cells retained a capacity retention of 46% vs Li + /Li and 55.2% vs Na + /Na over 50 cycles. PDI-Ur exhibits higher lithiation capacity of ∼119 mAh/g at the 14th cycling (increased discharge capacity of ∼118 mAh/g at the 25th cycling). In SIBs, PDI-Ur shows an initial discharge capacity of ∼119 mAh/g with a single discharge voltage plateau around 1.9 V vs Na + /Na and the capacity retention of ∼78.7% (∼93 mAh/g) over 50 cycles, both of which are suggesting a potential feasibility of these PTCDA -based polyimides as promising organic cathode materials for high-capacity metal-ions 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) | 0.001 | 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) | 0.001 | 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".